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On Friday, March 6, 2020, at 4:05pm, our Vice-Provost sent an email that the remaining classes for Winter Quarter were cancelled at Seattle Pacific University due to the COVID-19 epidemic. Classes wouldn’t meet in-person until September, and even then in a distanced and hybrid teaching model that continues to this day.

Seven separate emails sent to our faculty over the next three months used the word “unprecedented.” The word began to lose its savor after that, as words will, but has been used in two more all-faculty emails since, for a total of nine times in less than a year. (From 2008 to 2019, the word was used about once per year.)

For the first time, I thought through grocery supply chains and experimented with sound recording and webcam lighting. Prayer meetings shifted online as we prayed for about a dozen people I knew who were infected. I had never experienced this before. But the virus at the root of this unprecedented social disruption did itself have a precedent.

Everything natural since the Big Bang has a precedent. Living things depend on the events that came before in a long, rational chain. Even quasi-living viruses, being natural, have natural progenitors, and the genome of the SARS-CoV-2 virus that causes COVID-19 showed that it was no exception.

One of the first things I did when I heard of the SARS-CoV-2 virus, shaped by twenty-five years of biochemistry research, was to download its genome. According to the file timestamp, at 3:13pm on Monday, March 2, 2020, the SARS-CoV-2 genome arrived: a tiny, 30-kilobyte file of 29,844 letters — A, C, T, and G – representing the nucleotides in a single strand of RNA. This sequence was local, from an infected patient thirty miles away, at Providence Regional Medical Center in Everett, WA.

I would present that genome to my 4:30pm Natural Sciences Capstone Seminar (this class prep was indeed last-minute). The relevance of the material was self-explanatory. We were all trying to figure out just how bad this disease would be.

The entire genomic sequence of SARS-CoV-2 fit on a single slide in 4-point font. That particular arrangement of four nucleotides is translated into a particular arrangement of twenty amino acids in the viral proteins. Reading those letters, one could see that most SARS-CoV-2 proteins were in the same order as members of the coronavirus family. SARS-CoV-2 proteins show amino acids that are one-quarter to one-half identical to its coronavirus cousins.

SARS-CoV-2 is most similar to the first SARS coronavirus, which caused a scary pandemic in Asia two decades ago. The original SARS is like a brother to SARS-CoV-2. It shows less but still significant similarity to four other coronaviruses currently in circulation, which cause some common colds. These are more like its cousins.

This family tree of viral genomes let us guess how much the virus would spread and how many people it could infect. Two important epidemiological parameters are the infection fatality rate (how many people who get the infection die from it) and the basic reproduction number or R0 (how many people are infected by one infected person).

As these two parameters came into focus, we found that SARS-CoV-2 is the worst of both worlds: it is about as deadly as SARS, but it spreads as quickly as the common cold. Looked at individually, each parameter is precedented in related viruses. We have seen these numbers before, but not this combination, and not with so much global travel.

In that first class on March 3, I felt caught between dueling responsibilities. I wanted to give a clear vision of the danger of this virus, but I also didn’t want to cause needless panic as I read the stress in the students’ faces. Even then we knew that the virus attacked older people rather than younger, but that college-aged students could spread it to others.

I decided to take a leap of faith that science, being created by a consistent Creator, is real, and would continue into the future. So I told the students what I guessed: that SARS-CoV-2 was much worse than ordinary seasonal flu, but that this wasn’t The Walking Dead. They would have to carry a burden on behalf of others, which may in fact be heavier than carrying it for oneself.

I was nervous saying that. I knew this was a completely new virus and I could be wrong. I answered that doubt with reason, based on how its coronavirus cousins work, as confirmed by reading its genetic letters and estimating its virological numbers.

The longer-term question was how this story would end. My research is in structural immunology, so I knew the power of the immune system to evolve a response against new viruses, and our ability to train that response using vaccines. I also knew the power of the virus to evolve and evade immunity.

I got one number wrong in March — but that’s a good thing. Based on what I knew about vaccine development, I confidently predicted it would take 12-18 months to make a vaccine. Thanks to the unprecedentedly quick pace of development for the new mRNA vaccines, it took only nine months. These mRNA vaccines are a blessing and a gift, proof that one event in 2020 was better than expected.

But how fast would SARS-CoV-2 evolve over the next year? I assumed its evolutionary pace would follow the precedent of its coronavirus cousins. A typical coronavirus mutates about once every two weeks, and I applied this assumption to the two genomes from Washington state at that point. The first mid-January genome was different from the second, collected six weeks later, by three letters out of 29,844. Virologist Trevor Bedford tweeted that this fit the expected rate of evolution exactly.1

Since then, more than 360,000 SARS-CoV-2 genomes have been sequenced, and these confirm the initial estimate.2 On February 3, 2021, Bedford tweeted a graph (mutations vs. time) of these genomes showing that SARS-CoV-2 accumulated one mutation every 2.3 weeks on average, right in the middle of the original assumption.3

So many copies of the virus are running rampant right now that even this normal pace of evolution produces variants significantly more fit to survive. About two months ago, thousands of new SARS-CoV-2 genomes started to show disturbing patterns of identical mutations: one pattern in the United Kingdom, another in South Africa. The variants had disproportionate mutations in the spike protein.4 This is unlikely to be simple luck.

Most SARS-CoV-2 spike proteins accumulate one amino acid mutation every four months on average. The variants we’re worried about accumulate one mutation every six weeks on average, so they have evolved about three times as quickly as a typical SARS-CoV-2 virus.5 This amounts to 9 differences out of 671 amino acids in the SARS-CoV-2 spike protein, which is less than 2% of the protein.

A 2% change is unlikely to change the shape or general function of the protein. But even a small increase in affinity for the host cell could make the difference between sickness and health, or between transmission and isolation. The good news is, right now, cases are dropping rapidly in the UK and South Africa, as they are in the United States. But if a new variant reverses this trend, can we make new vaccines to train immune systems against the new strains?

We have a precedent for making a vaccine to match a mutating virus: the seasonal flu. There are two groups of flu strains: Flu A is more nimble, mutating more quickly than Flu B.6 A year ago, Bedford thought SARS-CoV-2 would mutate like Flu B, but with the emergence of the new variants, he now thinks it can mutate more like Flu A, about two to three times more quickly,7 but still within a precedented range. Variants may reduce vaccine effectiveness by 20%,8 which could reduce the exceptional >90% effectiveness of some vaccines to a more typical but still effective >70%. Even if, we appear to have margin with these remarkable vaccines.

Therefore, SARS-CoV-2 is twice as transmissible as a bad seasonal flu9 and from 5 to 25 times more deadly for adults depending on age.10 Its vaccine escape and reinfection probability should be similar to Flu A. Flu evolution is predictable enough that we can project where the virus will evolve to in a year (with varying degrees of success)[i] and make vaccines that help keep the virus at bay. I see nothing in the data to suggest that SARS-CoV-2 is drastically different.  Its vaccine escape and reinfection probability should be similar to Flu A. Flu evolution is predictable enough that we can project where the virus will evolve to in a year (with varying degrees of success)11 and make vaccines that help keep the virus at bay. I see nothing in the data to suggest that SARS-CoV-2 is drastically different.

When it comes to what viruses can do, our imaginations are more shaped by Hollywood screenwriters than they are by the careful study of the constraints and limits of the natural world. SARS-CoV-2 is a formidable foe, but it is flesh and blood, or in viral equivalents, protein and RNA. Evolution runs on randomness, but the laws of physics circumscribe evolutionary paths followed by previous viruses. The God who gave these laws set chemical boundaries for the virus as God set boundaries for the sea (Proverbs 8:29). Those constraints provide some comfort as we work within our own mortal limits to predict an uncertain future with realism and hope.

Footnotes

  1. Trevor Bedford first put forward this interpretation of the data. A few months later the data became more complicated, and Bedford walked back some of the claims. Although we’re no longer sure that the two genomes are directly related, the rate of coronavirus mutation as 1-3 mutations per month has been confirmed by Bedford’s later work.
  2. Cyranoski, David. “Alarming COVID variants show vital role of genomic surveillance.” Nature (January 15, 2021).
  3. Calculated from Bedford’s finding of 22.7 mutations per year:https://twitter.com/trvrb/status/1356997084822466560 8:04 AM · Feb 3, 2021
  4. https://twitter.com/trvrb/status/1356997090379927553 8:04 AM · Feb 3, 2021
  5. I calculate this from the average “variant of concern” appearing to have 9 amino acid mutations in spike S1 according to the graph in Bedford’s previous tweet. Some variants have 10 but I see only one with 11 and one with 12 in the graph.
  6.  Kistler, Kathryn E., and Trevor Bedford. “Evidence for adaptive evolution in the receptor-binding domain of seasonal coronaviruses OC43 and 229E.” Elife 10 (2021): e64509.
  7. https://twitter.com/trvrb/status/1356997111875653632 8:04 AM ·Feb 3, 2021
  8. https://twitter.com/shaniben_ezra/status/1358450369353908224 8:19 AM · Feb 7, 2021 
  9. R0= 2.5 for SARS-CoV-2 vs. 1.7 for the 2009 pandemic influenza. Petersen, Eskild, et al. “Comparing SARS-CoV-2 with SARS-CoV and influenza pandemics.” The Lancet infectious diseases (2020).
  10.  Infection Fatality Rate (IFR) anging from 5 times more deadly for 30-year-olds to 25 times more deadly for 70-year-olds. Statistically significant differences in mortality between COVID-19 and seasonal flu for those below 30 years old were not found. Based on a meta-analysis by Marc Bevand: https://github.com/mbevand/covid19-age-stratified-ifr 
  11. Agor, Joseph K., and Osman Y. Özaltın. “Models for predicting the evolution of influenza to inform vaccine strain selection.” Human vaccines & immunotherapeutics 14.3 (2018): 678-683.

Ben McFarland

Ben McFarland, Professor of Biochemistry, Seattle Pacific University.