Hi All. I have an interest in the evolution of respiratory viruses - my co-author and I published a review article in Reviews in Medical Virology last year.
I have a question about the SARS-CoV-2 strains that are excluded from Nextstrain because they have too many private mutations. I’m interested to know if the proportion of excluded strains increases during surges in C-19, and whether it decreases in the lulls.
The reason I’m interested in this is explained in the thread below - originally a Twitter thread.
I would also consider writing an article on this if anyone is interested.
All comments and constructive criticism would be much appreciated - please let me know if I’m missing something important!
We’re told that the genome of CoV-2 is so large it needs a proofreading function. Large RNA viruses are said to live on the edge of ‘error catastrophe’, where a small increase in mutation rate would drastically decrease the odds of stable replication
But what does the evolution of the proofreading mechanism itself look like? That’s not obvious
We know the CoV-2 proofreading function reduces mutation by a factor of ~ 20. It includes a protein called ExoN/NSP14.
But some mutations still get through - so what happens if a mutation lands in eg ExoN itself? That’s likely to increase the mutation rate, including more mutations landing in ExoN
On the other hand, it seems likely that a higher mutation rate would benefit the virus in the short-term. There’s no shortage of mutations within each infected human host, but many fewer mutations are likely to be breathed out and actually make the journey to another host
(Bear in mind also that mutations that increase the chance of transmission might not be selected within the host - in fact the opposite may well be the case – many can only be selected during transmission)
So, with more mutations, it seems likely that our low-fidelity mutant could adapt more quickly to new opportunities than the regular strains
So - the first problem is to explain how this proof-reading mechanism could have evolved and how it could be maintained.
An analogy that’s used is cancer. Cancer cells are favored by “natural selection” in the short-term, but they have no long-term future. It’s a bit like a human situation where you might say “a few selfish people are going to spoil this for everyone”
So how does this work and what would the outward appearance of a low-fidelity mutant be during an epidemic?
Presumably it would generate a surge of cases, outcompeting the high-fidelity strains. However, it would then be unable to stop mutating, and (a bit like in cancer) mutations would accumulate in vital viral functions, so the surge would be followed by a collapse
At that point the hi-fi strains would once again be selected, and we would be back with a more stable strain, maybe now with one or two beneficial new mutations (the hi-fi strains still evolve, slowly)
I can’t see how these “cancerous” surges can be avoided, and I can’t see how the resulting cases profile can look like anything other than repeated cycles of “surge and collapse”
It just happens this is exactly what we see with C19 – impressive & unpredictable surges, followed by very rapid peaking & collapse. (I know there’re other explanations **see below.) Here⬇️are cases in India & S Africa – countries where control measures/lockdowns may be difficult
So here’s the problem: if the mutation rate is fluctuating, why don’t we see⬇️an increased number of mutations in eg Nextstrain.org during surges, and why doesn’t the number of mutations fall back when the hi-fi strains come through at the end of the surge?
Amazingly, there is a possible answer to this question: NextStrain has a policy of excluding any strain with an unusual number of what they call “private mutations” – those that differ between the query sequence and the nearest neighbor sequence
So here’s my prediction: the proportion of sequences excluded by NextStrain goes up during surges, and falls in the lulls.
**BTW I know there are other explanations of the extraordinary Covid peaks and monotonic (continuous) falls, such as increasing immunity, behavioural changes in hosts, and non-linear percolation effects
However, IMO these explanations can’t work well: can we believe that immunity and behaviour changed so dramatically that R in South Africa fell from > 1.2 (pink band) to < 0.8 (blue band) in 11 days? We need something really powerful to explain these reversals
And bear in mind that immunity was not high after this particular surge - it was followed by three similar surges in the following months/years!