Tracking the Evolution of a Pandemic
A close examination of the genetic evolution of the three major influenza epidemics of the 20th century concludes that all of the viruses involved evolved slowly, through interspecies genetic exchange, and that genes from the catastrophic 1918 pandemic may have been circulating as many as seven years earlier. If true, this means that widespread genetic surveillance methods should have ample time to detect the next pandemic strain, and possibly even vaccinate against it before it gets out of control.
Prior research suggested that the 1918 influenza strain was the result of an avian virus introduced into humans just before the epidemic began. But the latest study, published today in the Proceedings of the National Academy of Sciences, suggests that all three influenza pandemics–1918, 1957, and 1968–were the result of stepwise genetic integrations of both avian and mammalian genes over a number of years, ultimately creating the more virulent virus strains.
And although the research was done before the emergence of the current H1N1 “swine flu” strain, the scientists’ conclusions are relevant, showing that the current virus follows the same historical pattern. For each pandemic, “our results argued that there was at least one intermediate host that was most likely to be pigs, and that they’re involved in the emergence of these pandemic strains,” says Gavin Smith, the paper’s lead author and a viral-evolution researcher at the State Key Laboratory of Emerging Infectious Diseases, at the University of Hong Kong.
The researchers collected all available genetic sequences of the influenza virus–human, bird, and pig variants–then plugged the data into a computer program that uses genetic information to build evolutionary trees, dating species’ divergence back to their most recent common ancestor. But there are no known precursor viruses to the 1918 strain, so the computational results can only infer the time of interspecies transmission, based on known patterns of genetic evolution. The genetic data itself was derived from virus strains that have evolved since 1918.
Such studies have only become possible in the past few years, with the advancement of computational techniques that can incorporate known rates of various species’ evolution–techniques that are proving to be quite accurate when tested against known relationships. But the results are still, as Smith notes, “all just inference,” working backward from known relationships and based on estimated dates.
According to the virus’s updated family tree, the 1918 strain was not newly minted but actually a slightly modified version of a mild flu strain already in the human population. In fact, according to the new analysis, some genes of the virus may have been circulating as early as 1911. “It was certainly different in terms of severity of the actual pandemic,” Smith says. “But our results show that, in terms of how the virus emerged, it looks like much the same mechanism of the 1957 and 1968 pandemics, where the virus gets introduced into the human population over a period of time and reassorts with the previous human strain.”
Each of the pandemics appears to have the same pattern when emerging in humans, with different genetic components floating around in people for a few years before a pandemic strain is detected. And the detailed computational analysis showed that different component genes of the viruses seemed to be different ages. “What this suggests is that it’s not one virus coming in and mixing with the human seasonal strain to produce a pandemic strain,” Smith says. “Rather, there are a number of reassortment events, where one gene comes in and mixes with the human strain, and then another gene comes in and mixes with the human in a stepwise pattern.”
If the researchers are right, the 1918 flu may have even more in common with the current swine flu virus than scientists previously believed. And finding such a pattern among known pandemic strains holds implications for future surveillance. By looking backward, at which genes have caused prior influenza strains to turn lethal, the research may one day enable researchers to look forward too. “What this paper is saying is that we’re actually in a position now to get hints about these viruses even years in advance,” says Greg Poland, a vaccine and infectious-disease expert at the Mayo Clinic, who was not involved in the research. “I think it will inform surveillance efforts, I think it will inform vaccine development efforts, and I think it will eventually inform policy-making decisions.”
In addition to keeping an eye out for influenza variants in humans, Poland and Smith believe that it’s just as important to start doing deeper surveillance in birds and pigs, and on a much more extensive basis. And, Poland notes, knowing that the strains emerge slowly could help inform vaccine efforts as well.
“There’s no reason we can’t move away from [creating] a vaccine against what we think we know will circulate this year, toward including proteins from variants we suspect might become problematic in the future,” says Poland.
Smith hopes that more full-genome sequencing will provide advance warning of which genes might show up in humans, and that a deeper look at the genomes will provide clues about where and why the animal-to-human transmission occurs. He also hopes that one day, the team’s research could help change governmental approaches from pandemic preparedness to pandemic prevention. “But the problem is that we still don’t know what it is about a virus that makes it pandemic,” Smith says. “Is it mutation? Is it a certain combination of genes? These are things that we still need to look at.”
The inside story of how ChatGPT was built from the people who made it
Exclusive conversations that take us behind the scenes of a cultural phenomenon.
ChatGPT is about to revolutionize the economy. We need to decide what that looks like.
New large language models will transform many jobs. Whether they will lead to widespread prosperity or not is up to us.
Sam Altman invested $180 million into a company trying to delay death
Can anti-aging breakthroughs add 10 healthy years to the human life span? The CEO of OpenAI is paying to find out.
GPT-4 is bigger and better than ChatGPT—but OpenAI won’t say why
We got a first look at the much-anticipated big new language model from OpenAI. But this time how it works is even more deeply under wraps.
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.