The race is on. Vaccines against the virus that causes Covid-19 are spreading around the world, the tip of the hypodermic spear of a year’s scientific triumph. But that multifaceted virus, like all things that infect humans and make them sick, attacks and prevents.
Virology versus epidemiology. Vaccinology versus evolution. Mutation versus mutation, transmission versus infection, virus versus vaccine. Begin! Your! Engines! The past year (horrible, tragic, bad, bad) may have seemed like a direct battle between scientists and a virus to find new drugs and vaccines. But this was not just a standing fight; it was also an insect hunt – a subtle push-pull in a dozen different vectors. Viruses are not exactly alive, but they still follow the same rule book for all living things on Earth: adapt or die. Understanding these more hidden forces – how viruses evolve within us, their hosts, and how they change the way they pass from one person to another – will define the next phase of the pandemic.
It’s easy to freak out with the new variants of the SARS-CoV-2 virus, with its science fiction nomenclature. There is B.1.1.7, who seems to be a genius at infecting new people. And you have B.1.351 and P.1 – perhaps not better at transmitting from one host to another, but better at avoiding an immune response (natural or the type that a vaccine induces). Many who escape the immune system share the same mutation, even though they are distant relatives. This, as the saying goes, is life. “The way the virus evolves, the fundamentals of evolution, are the same. The difference is that it occurs on a very, very large scale. There are so many people infected and each one contains many viruses. So there are many opportunities for the virus to mutate and try new things, ”says Adam Lauring, a virologist at the University of Michigan who studies viral evolution. “Once in a while one of those takes off. It is a rare event, but when the virus has so many opportunities to control it, it will happen with increasing frequency. ”This is both an epidemiology game, in other words, and evolutionary biology.
So while it may seem that these variants have some kind of evil intent – to make people sicker, to kill all humans! – this is not what is happening. Viruses want nothing; they are just verbs. Infect, reproduce, infect. A virus that kills very efficiently does not become a virus for long, because dead hosts cannot walk around breathing in uninfected, but susceptible, suckers. Therefore, one hypothesis says that these successful mutations are mainly changes in the way the virus infects. That is, they improve the way the virus enters a human being, or enters a human cell, or reproduces in that cell (because the more viruses a person produces, the more they emit and the more likely it is to reach someone else) ).
This is probably why all of these similar variants seem to come up all at once and quickly. Viruses are just small portions of proteins wrapped in large code molecules of genetic material. In SARS-CoV-2, that material is RNA. And some viruses burst mutations more often than others.
Viruses evolve because they reproduce – in fact, this is their complete trick – and errors infiltrate this genetic material in the process. Over the generations, sometimes these random or “stochastic” errors make the virus better in its actions; sometimes they make it worse. Which means that the circumstances of a virus’s life, or type of life, act against random changes in the code underlying its genes. (SARS-CoV-2 appears to mutate at almost the same rate as other RNA viruses, although, like other coronaviruses in its family, it has a built-in error-correction mechanism. It needs it, because its genome is so large, relatively speaking, three times the size of the HIV genome, the virus that causes AIDS, for example. “Without the revision, it would probably create many mutations per virus replication event to remain viable,” says Katrina Lythgoe, evolutionary big data epidemiologist Institute at Oxford University This type of genomic suicide is called crossing the “catastrophe limit of error”.