COVID Variants Are Like “a Thief Changing Clothes” – and Our Camera System Barely Exists
Whether it's "natural selection" as Darwin called it, or it's "mutating" as the X-Men called it, living organisms change over time, developing thumbs or more efficient protein spikes, depending on the organism and the demands of its environment. The coronavirus that causes COVID-19, SARS-CoV-2, is not an exception, and now, after the virus has infected millions of people around the globe for more than a year, scientists are beginning to see those changes.
The notorious variants that have popped up include B.1.1.7, sometimes called the UK variant, as well as P.1 and B.1.351, which seem to have emerged in Brazil and South Africa respectively. As vaccinations are picking up pace, officials are warning that now
is not the time to become complacent or relax restrictions because the variants aren't well understood.
Some appear to be more transmissible, and deadlier, while others can evade the immune system's defenses better than earlier versions of the virus, potentially undermining the effectiveness of vaccines to some degree. Genomic surveillance, the process of sequencing the genetic code of the virus widely to observe changes and patterns, is a critical way that scientists can keep track of its evolution and work to understand how the variants might affect humans.
"It's like a thief changing clothes"
It's important to note that viruses mutate all the time. If there were funding and personnel to sequence the genome of every sample of the virus, scientists would see thousands of mutations. Not every variant deserves our attention. The vast majority of mutations are not important at all, but recognizing those that are is a crucial tool in getting and staying ahead of the virus. The work of sequencing, analyzing, observing patterns, and using public health tools as necessary is complicated and confusing to those without years of specialized training.
Jeremy Kamil, associate professor of microbiology and immunology at LSU Health Shreveport, in Louisiana, says that the variants developing are like a thief changing clothes. The thief goes in your house, steals your stuff, then leaves and puts on a different shirt and a wig, in the hopes you won't recognize them. Genomic surveillance catches the "thief" even in those different clothes.
One of the tricky things about variants is recognizing the point at which they move from interesting, to concerning at a local level, to dangerous in a larger context.
Understanding variants, both the uninteresting ones and the potentially concerning ones, gives public health officials and researchers at different levels a useful set of tools. Locally, knowing which variants are circulating in the community helps leaders know whether mask mandates and similar measures should be implemented or discontinued, or whether businesses and schools can open relatively safely.
There's more to it than observing new variants
Analysis is complex, particularly when it comes to understanding which variants are of concern. "So the question is always if a mutation becomes common, is that a random occurrence?" says Phoebe Lostroh, associate professor of molecular biology at Colorado College. "Or is the variant the result of some kind of selection because the mutation changes some property about the virus that makes it reproduce more quickly than variants of the virus that don't have that mutation? For a virus, [mutations can affect outcomes like] how much it replicates inside a person's body, how much somebody breathes it out, whether the particles that somebody might breathe in get smaller and can lead to greater transmission."
Along with all of those factors, accurate and useful genomic surveillance requires an understanding of where variants are occurring, how they are related, and an examination of why they might be prevalent.
For example, if a potentially worrisome variant appears in a community and begins to spread very quickly, it's not time to raise a public health alarm until several important questions have been answered, such as whether the variant is spreading due to specific events, or if it's happening because the mutation has allowed the virus to infect people more efficiently. Kamil offered a hypothetical scenario to explain: Imagine that a member of a community became infected and the virus mutated. That person went to church and three more people were infected, but one of them went to a karaoke bar and while singing infected 100 other people. Examining the conditions under which the virus has spread is, therefore, an essential part of untangling whether a mutation itself made the virus more transmissible or if an infected person's behaviors contributed to a local outbreak.
One of the tricky things about variants is recognizing the point at which they move from interesting, to concerning at a local level, to dangerous in a larger context. Genomic sequencing can help with that, but only when it's coordinated. When the same mutation occurs frequently, but is localized to one region, it's a concern, but when the same mutation happens in different places at the same time, it's much more likely that the "virus is learning that's a good mutation," explains Kamil.
The process is called convergent evolution, and it was a fascinating topic long before COVID. Just as your heritage can be traced through DNA, so can that of viruses, and when separate lineages develop similar traits it's almost like scientists can see evolution happening in real time. A mutation to SARS-CoV-2 that happens in more than one place at once is a mutation that makes it easier in some way for the virus to survive and that is when it may become alarming. The widespread, documented variants P.1 and B.1.351 are examples of convergence because they share some of the same virulent mutations despite having developed thousands of miles apart.
However, even variants that are emerging in different places at the same time don't present the kind of threat SARS-CoV-2 did in 2019. "This is nature," says Kamil. "It just means that this virus will not easily be driven to extinction or complete elimination by vaccines." Although a person who has already had COVID-19 can be reinfected with a variant, "it is almost always much milder disease" than the original infection, Kamil adds. Rather than causing full-fledged disease, variants have the potiental to "penetrate herd immunity, spreading relatively quietly among people who have developed natural immunity or been vaccinated, until the virus finds someone who has no immunity yet, and that person would be at risk of hospitalization-grade severe disease or death."
Surveillance and predictions
According to Lostroh, genomic surveillance can help scientists predict what's going to happen. "With the British strain, for instance, that's more transmissible, you can measure how fast it's doubling in the population and you can sort of tell whether we should take more measures against this mutation. Should we shut things down a little longer because that mutation is present in the population? That could be really useful if you did enough sampling in the population that you knew where it was," says Lostroh. If, for example, the more transmissible strain was present in 50 percent of cases, but in another county or state it was barely present, it would allow for rolling lockdowns instead of sweeping measures.
Variants are also extremely important when it comes to the development, manufacture, and distribution of vaccines. "You're also looking at medical countermeasures, such as whether your vaccine is still effective, or if your antiviral needs to be updated," says Lane Warmbrod, a senior analyst and research associate at Johns Hopkins Center for Health Security.
Properly funded and extensive genomic surveillance could eventually help control endemic diseases, too, like the seasonal flu, or other common respiratory infections. Kamil says he envisions a future in which genomic surveillance allows for prediction of sickness just as the weather is predicted today. "It's a 51 for infection today at the San Francisco Airport. There's been detection of some respiratory viruses," he says, offering an example. He says that if you're a vulnerable person, if you're immune-suppressed for some reason, you may want to wear a mask based on the sickness report.
The U.S. has the ability, but lacks standards
The benefits of widespread genomic surveillance are clear, and the United States certainly has the necessary technology, equipment, and personnel to carry it out. But, it's not happening at the speed and extent it needs to for the country to gain the benefits.
"The numbers are improving," said Kamil. "We're probably still at less than half a percent of all the samples that have been taken have been sequenced since the beginning of the pandemic."
Although there's no consensus on how many sequences is ideal for a robust surveillance program, modeling performed by the company Illumina suggests about 5 percent of positive tests should be sequenced. The reasons the U.S. has lagged in implementing a sequencing program are complex and varied, but solvable.
Perhaps the most important element that is currently missing is leadership. In order to conduct an effective genomic surveillance program, there need to be standards. The Johns Hopkins Center for Health Security recently published a paper with recommendations as to what kinds of elements need to be standardized in order to make the best use of sequencing technology and analysis.
"Along with which bioinformatic pipelines you're going to use to do the analyses, which sequencing strategy protocol are you going to use, what's your sampling strategy going to be, how is the data is going to be reported, what data gets reported," says Warmbrod. Currently, there's no guidance from the CDC on any of those things. So, while scientists can collect and report information, they may be collecting and reporting different information that isn't comparable, making it less useful for public health measures and vaccine updates.
Globally, one of the most important tools in making the information from genomic surveillance useful is GISAID, a platform designed for scientists to share -- and, importantly, to be credited for -- their data regarding genetic sequences of influenza. Originally, it was launched as a database of bird flu sequences, but has evolved to become an essential tool used by the WHO to make flu vaccine virus recommendations each year. Scientists who share their credentials have free access to the database, and anyone who uses information from the database must credit the scientist who uploaded that information.
Safety, logistics, and funding matter
Scientists at university labs and other small organizations have been uploading sequences to GISAID almost from the beginning of the pandemic, but their funding is generally limited, and there are no standards regarding information collection or reporting. Private, for-profit labs haven't had motivation to set up sequencing programs, although many of them have the logistical capabilities and funding to do so. Public health departments are understaffed, underfunded, and overwhelmed.
University labs may also be limited by safety concerns. The SARS-CoV-2 virus is dangerous, and there's a question of how samples should be transported to labs for sequencing.
Larger, for-profit organizations often have the tools and distribution capabilities to safely collect and sequence samples, but there hasn't been a profit motive. Genomic sequencing is less expensive now than ever before, but even at $100 per sample, the cost adds up -- not to mention the cost of employing a scientist with the proper credentials to analyze the sequence.
The path forward
The recently passed COVID-19 relief bill does have some funding to address genomic sequencing. Specifically, the American Rescue Plan Act includes $1.75 billion in funding for the Centers for Disease Control and Prevention's Advanced Molecular Detection (AMD) program. In an interview last month, CDC Director Rochelle Walensky said that the additional funding will be "a dial. And we're going to need to dial it up." AMD has already announced a collaboration called the Sequencing for Public Health Emergency Response, Epidemiology, and Surveillance (SPHERES) Initiative that will bring together scientists from public health, academic, clinical, and non-profit laboratories across the country with the goal of accelerating sequencing.
Such a collaboration is a step toward following the recommendations in the paper Warmbrod coauthored. Building capacity now, creating a network of labs, and standardizing procedures will mean improved health in the future. "I want to be optimistic," she says. "The good news is there are a lot of passionate, smart, capable people who are continuing to work with government and work with different stakeholders." She cautions, however, that without a national strategy we won't succeed.
"If we maximize the potential and create that framework now, we can also use it for endemic diseases," she says. "It's a very helpful system for more than COVID if we're smart in how we plan it."
Even before the pandemic created a need for more telehealth options, depression was a hot area of research for app developers. Given the high prevalence of depression and its connection to suicidality — especially among today’s teenagers and young adults who grew up with mobile devices, use them often, and experience these conditions with alarming frequency — apps for depression could be not only useful but lifesaving.
“For people who are not depressed, but have been depressed in the past, the apps can be helpful for maintaining positive thinking and behaviors,” said Andrea K. Wittenborn, PhD, director of the Couple and Family Therapy Doctoral Program and a professor in human development and family studies at Michigan State University. “For people who are mildly to severely depressed, apps can be a useful complement to working with a mental health professional.”
Health and fitness apps, in general, number in the hundreds of thousands. These are driving a market expected to reach $102.45 billion by next year. The mobile mental health app market is a small part of this but still sizable at $500 million, with revenues generated through user health insurance, employers, and direct payments from individuals.
Apps can provide data that health professionals cannot gather on their own. People’s constant interaction with smartphones and wearable devices yields data on many health conditions for millions of patients in their natural environments and while they go about their usual activities. Compared with the in-office measurements of weight and blood pressure and the brevity of doctor-patient interactions, the thousands of data points gathered unobtrusively over an extended time period provide a far better and more detailed picture of the person and their health.
At their most advanced level, apps for mental health, including depression, passively gather data on how the user touches and interacts with the mobile device through changes in digital biomarkers that relate to depressive symptoms and other conditions.
Building on three decades of research since early “apps” were used for delivering treatment manuals to health professionals, today’s more than 20,000 mental health apps have a wide range of functionalities and business models. Many of these apps can be useful for depression.
Some apps primarily provide a virtual connection to a group of mental health professionals employed or contracted by the app. Others have options for meditation, sleeping or, in the case of industry leaders Calm and Headspace, overall well-being. On the cutting edge are apps that detect changes in a person’s use of mobile devices and their interactions with them.
Apps such as AbleTo, Happify Health, and Woebot Health focus on cognitive behavioral therapy, a type of counseling with proven potential to change a person’s behaviors and feelings. “CBT has been demonstrated in innumerable studies over the last several decades to be effective in the treatment of behavioral health conditions such as depression and anxiety disorders,” said Dr. Reena Pande, chief medical officer at AbleTo. “CBT is intended to be delivered as a structured intervention incorporating key elements, including behavioral activation and adaptive thinking strategies.”
These CBT skills help break the negative self-talk (rumination) common in patients with depression. They are taught and reinforced by some self-guided apps, using either artificial intelligence or programmed interactions with users. Apps can address loneliness and isolation through connections with others, even when a symptomatic person doesn’t feel like leaving the house.
At their most advanced level, apps for mental health, including depression, passively gather data on how the user touches and interacts with the mobile device through changes in “digital biomarkers” that can be associated with onset or worsening of depressive symptoms and other cognitive conditions. In one study, Mindstrong Health gathered a year’s worth of data on how people use their smartphones, such as scrolling through articles, typing and clicking. Mindstrong, whose founders include former leaders of the National Institutes of Health, modeled the timing and order of these actions to make assessments that correlated closely with gold-standard tests of cognitive function.
National organizations of mental health professionals have been following the expanding number of available apps over the years with keen interest. App Advisor is an initiative of the American Psychiatric Association that helps psychiatrists and other mental health professionals navigate the issues raised by mobile health technology. App Advisor does not rate or recommend particular apps but rather provides guidance about why apps should be assessed and how health professionals can do this.
A website that does review mental health apps is One Mind Psyber Guide, an independent nonprofit that partners with several national organizations. One Mind users can select among numerous search terms for the condition and therapeutic approach of interest. Apps are rated on a five-point scale, with reviews written by professionals in the field.
Do mental health apps related to depression have the kind of safety and effectiveness data required for medications and other medical interventions? Not always — and not often. Yet the overall results have shown early promise, Wittenborn noted.
“Studies that have attempted to detect depression from smartphone and wearable sensors [during a single session] have ranged in accuracy from about 86 to 89 percent,” Wittenborn said. “Studies that tried to predict changes in depression over time have been less accurate, with accuracy ranging from 59 to 85 percent.”
The Food and Drug Administration encourages the development of apps and has approved a few of them—mostly ones used by health professionals—but it is generally “hands off,” according to the American Psychiatric Association. The FDA has published a list of examples of software (including programming of apps) that it does not plan to regulate because they pose low risk to the public. First on the list is software that helps patients with diagnosed psychiatric conditions, including depression, maintain their behavioral coping skills by providing a “Skill of the Day” technique or message.
On its App Advisor site, the American Psychiatric Association says mental health apps can be dangerous or cause harm in multiple ways, such as by providing false information, overstating the app’s therapeutic value, selling personal data without clearly notifying users, and collecting data that isn’t relevant to mental health.
Although there is currently reason for caution, patients may eventually come to expect mental health professionals to recommend apps, especially as their rating systems, features and capabilities expand. Through such apps, patients might experience more and higher quality interactions with their mental health professionals. “Apps will continue to be refined and become more effective through future research,” said Wittenborn. “They will become more integrated into practice over time.”
Podcast: Has the First 150-Year-Old Already Been Born
Steven Austad is a pioneer in the field of aging, with over 200 scientific papers and book chapters on pretty much every aspect of biological aging that you could think of. He’s also a strong believer in the potential for anti-aging therapies, and he puts his money where his mouth is. In 2001, he bet a billion dollars that the first person to reach 150-years-old had already been born. I had a chance to talk with Steven for today’s podcast and asked if he still thinks the bet was a good idea, since the oldest person so far (that we know of), Jeanne Calment, died back in 1997. A few days after our conversation, the oldest person in the world, Kane Tanaka, died at 119.
Steven is the Protective Life Endowed Chair in Health Aging Research, a Distinguished Professor and Chair of the Department of Biology at the University of Alabama Birmingham. He's also Senior Scientific Director of the American Federation for Aging Research, which is managing a groundbreaking longevity research trial that started this year. Steven is also a great science communicator with five books, including one that comes out later this year, Methuselah’s Zoo, and he publishes prolifically in national media outlets.
See the rest of his bio linked below in the show notes.
Listen to the Episode
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Steven Austad is featured in the latest episode of Making Sense of Science. He's a distinguished professor of biology at the University of Alabama Birmingham and has a new book due to be published in August, Methuselah's Zoo.
Photo by Steve Wood
Show notes:
2:36 - Steven explains why a particular opossum convinced him to dedicate his career to studying longevity.
6:48 - Steven's billion dollar bet that someone alive today will make it to 150-years-old.
9:15 - The most likely people to make it to 150 (Hint: not men).
10:38 - I ask Steven about Elon Musk’s comments this month that if people lived a really long time, “we’d be stuck with old ideas and society wouldn’t advance.” Steve isn’t so fond of that take.
13:34 - Why women are winning maybe the most important battle of sexes: staying alive. This is an area that Steven has led research on (see show notes).
18:20 - Why women, on average, actually have more morbidities earlier than men, even though they live longer.
23:10 - How the pandemic could affect sex differences in longevity.
24:55 - How often should people work out and get other physical activity to maximize longevity and health span?
29:09 - Steven gave me the latest update on the TAME trial on metformin, and how he and others longevity experts designed this groundbreaking research on longevity not in their offices, not on a zoom call, but in a castle in the Spanish countryside.
32:10 - Which anti-aging therapies are the most promising at this point for future research.
39:32 - The drug cocktail approach to address multiple hallmarks of aging.
41:00 - How to read health news like a scientist.
45:38 - Should we try a Manhattan project for aging?
48:47 - Can Jeff Bezos and Larry Ellison help us live to 150?
Show links:
Steven Austad's bio
Pre-order Steven's new book, Methuselah's Zoo - https://www.amazon.com/dp/B09M2QGRJR/ref=dp-kindle-redirect?_encoding=UTF8&btkr=1
Steven's journal article on Sex Differences in Lifespan - https://pubmed.ncbi.nlm.nih.gov/27304504/
Elon Musk's comments on super longevity "asphyxiating" society - https://www.cnbc.com/2022/04/11/elon-musk-on-avoid...
Steven's article on how to read news articles about health like a pro - https://www.nextavenue.org/how-to-read-health-news...
AFAR's research on Targeting Aging with Metformin (TAME) - https://www.afar.org/tame-trial