The science in the paper is fairly interesting. It observes that:
1) Certain types of mutations are favored by different host biologies. In other words, in humans, you would tend to see one type of coding substitution, in mice, pigs, bats, etc. you'd see another type of coding substitution. The paper gives specific mechanisms that underly this biases; I'm not an expert in these, so I can't say for sure if they are correct, but it the mechanisms as proposed make sense on a first read-through.
2) The early omicron variants have a set of mutations that would be consistent with evolution in a non-human host, overlapping most strongly with mice biology.
3) Perhaps even more significantly, _later_ omicron variants that have spread through human populations have picked up mutations that are once again more consistent with human biology. So the paper manages to show both positive and negative data toward their hypothesis, which makes it stronger.
For the TL;DR crowd, figure 3 summarizes this pretty well.
To analogize this in computer terms, it's as if someone was tracing the evolution of memes by observing the types of compression artifacts introduced in various re-compressions of the meme. For example, JPEG might introduce artifacts related to DCT math, whereas JPEG-2000 would introduce artifacts related to wavelet math. The analysis shows that the artifacts of this new Omicron meme has compression artifacts consistent with, say, Wavelet compression up until it jumped to being shared by JPEG-users, at which point newer modifications to the Omicron 'meme' show JPEG-consistent artifacts.
That's basically the scientific core of the paper. So, to contest the validity of the paper, I think it would be helpful to find citations that dispute the observation that mutations accumulate with a certain bias based on the host biology, rather than whattaboutism pointing to other hypotheses and leaving it at "there are other alternative explanations" therefore this paper is not interesting.
So, while I don't dispute your statement that there are other alternative explanations, what I would really like to see are comments addressing the core scientific thesis of the paper, which is that certain mutations appear more frequently in other host biologies; and that the first omicron cases showed a preponderance of these differently-biased mutations; and that since it has been circulating in humans, the later mutations are once again consistent with a human host.
At the very least, I thought it was a very interesting analysis, as I had never seen the molecular bias of mutations used as a proxy for host biology before. If this is true, then viruses that can spread through alternate host biologies would gain a diversity of mutations that would be less available co-evolving with a single host, which could lead to evolutionary advantages over viruses that are unable to hop between hosts.
> dispute the observation that mutations accumulate with a certain bias based on the host biology
To be clear that is not being disputed. IMO the central thesis of the paper is:
> the progenitor of Omicron jumped from humans to mice, rapidly accumulated mutations conducive to infecting that host, then jumped back into humans
That hypothesis is primarily supported by statistical evidence related to the frequency of certain amino acid mutations in the viral genome. For example:
> Considering that these two amino acid mutations are uncommon in human patients infected by non-Omicron SARS-CoV-2 variants (0.005% and 0.002%, respectively) we proposed the hypothesis that the progenitor of Omicron evolved in mice.
> searching against the GISAID database using Omicron’s backbone sequence. The top hits were again from the B.1.1 lineage, which differed from Omicron by 31 mutations, indicating that human SARS-CoV-2 variants reported to date could not provide a backbone for Omicron
While the paper certainly presents intriguing preliminary evidence, critically there was no analysis performed which accounted for variation or underestimates in viral mutation rates, number of infected hosts, or gaps in genomic DBs, etc. As such, there are certain hidden assumptions that were not explicitly addressed or conclusively proven.
Moreover, the "pleiotropic effect" is a significant confounding factor:
> predictions showed that the adaptation of Omicron to mice also promoted its adaptation to other species, such as humans, camels, and goats, via stronger RBD-ACE2 interaction (Fig. 6). Such a “pleiotropic effect” of mutations was likely caused by structural similarity of ACE2 across species
Finally, given that selective pressure due to mass distribution of S protein targeted vaccines was also not considered, I think it's fair to say that the evidence pointing to mice is fairly tenuous at this point. But the paper is still valid & interesting (I did not intend to suggest otherwise).
If the thesis is correct then this could be a way to find out if other viruses are jumping between humans and mice. Eventually we might be able to build a map of all host-to-host jumps and get a true picture of where disease resides in the world and how it spreads.
I don't think that anyone seriously questions that Omicron is a mutation of the original strain from Wuhan? So yes, if Omicron comes from mice it's a jump from humans to mice and back.
Not necessarily. It could be same ancestral source or the mouse could have been the original vector, not the bats. Vector and mutation tracing is not an exact science, but a probability game - nobody catches all mice everywhere, or has access to all intermediate variants.
Now, the origin in a different country does suggest a human host somewhere but it is not strictly necessary.
Ok to be honest though my gut feeling is that statistically making these sorts of inferences over 30 mutations seems pretty weak, especially since the level of difference between say human and mouse is not that great to start out with, and because these mutations are not independent; they are dependent on survival of the virus.
I guess part of the problem is that I don't know how to do the statistics on this sort of a problem, unless say I were to actually model the drift events on a computer and "manually" calculate probabilities given a certain set of assumptions. Do i trust biostatisticians to get this right? No. (Yes, I've worked with them)
1) Certain types of mutations are favored by different host biologies. In other words, in humans, you would tend to see one type of coding substitution, in mice, pigs, bats, etc. you'd see another type of coding substitution. The paper gives specific mechanisms that underly this biases; I'm not an expert in these, so I can't say for sure if they are correct, but it the mechanisms as proposed make sense on a first read-through.
2) The early omicron variants have a set of mutations that would be consistent with evolution in a non-human host, overlapping most strongly with mice biology.
3) Perhaps even more significantly, _later_ omicron variants that have spread through human populations have picked up mutations that are once again more consistent with human biology. So the paper manages to show both positive and negative data toward their hypothesis, which makes it stronger.
For the TL;DR crowd, figure 3 summarizes this pretty well.
To analogize this in computer terms, it's as if someone was tracing the evolution of memes by observing the types of compression artifacts introduced in various re-compressions of the meme. For example, JPEG might introduce artifacts related to DCT math, whereas JPEG-2000 would introduce artifacts related to wavelet math. The analysis shows that the artifacts of this new Omicron meme has compression artifacts consistent with, say, Wavelet compression up until it jumped to being shared by JPEG-users, at which point newer modifications to the Omicron 'meme' show JPEG-consistent artifacts.
That's basically the scientific core of the paper. So, to contest the validity of the paper, I think it would be helpful to find citations that dispute the observation that mutations accumulate with a certain bias based on the host biology, rather than whattaboutism pointing to other hypotheses and leaving it at "there are other alternative explanations" therefore this paper is not interesting.
So, while I don't dispute your statement that there are other alternative explanations, what I would really like to see are comments addressing the core scientific thesis of the paper, which is that certain mutations appear more frequently in other host biologies; and that the first omicron cases showed a preponderance of these differently-biased mutations; and that since it has been circulating in humans, the later mutations are once again consistent with a human host.
At the very least, I thought it was a very interesting analysis, as I had never seen the molecular bias of mutations used as a proxy for host biology before. If this is true, then viruses that can spread through alternate host biologies would gain a diversity of mutations that would be less available co-evolving with a single host, which could lead to evolutionary advantages over viruses that are unable to hop between hosts.