Evolution of SARS-CoV-2 between February 1, 2020 and July 1, 2021 depicted by waves (i.e., successions of “mutation groups”) in UK (a), USA (b), India (c), and Global (d). Sequencing data were obtained from the GISAID database. The frequencies of nonsynonymous mutations (A) and synonymous mutations (S) reaching the frequency cutoff of 0.3 at their peaks are presented. The number of mutations is shown in the parentheses. Although a curve represents the rise and fall of a variant, each observed curve usually represents multiple curves that overlap completely. In COVID-19, there are five waves (W0 to W4). Note that the decline in each wave as the next one rises (between W1 and W2, W2 and W3, or W3 and W4) is true in both relative and absolute abundance.
Since the Coronavirus pandemic first took off in early 2020, the world has experienced several waves of new, more infectious variants. This presents creationists with two possibilities:
- Either their preferred 'intelligent [sic] designer' is intentionally modifying its original design to overcome human efforts to contain and control the pandemic.
- Or, the SARS-CoV-2 virus is evolving naturally under environmental pressure in exactly the way scientists explain how evolution works.
PART I—The Hypothesis of Runaway Viral Evolution
As stated in the Introduction, there are multiple factors that can accelerate the adaptive evolution of SARS-CoV-2. One of the factors that can be most easily formulated is the viral population size at time t, M(t). Other factors may be no less important, but the M(t) data are readily available. We shall follow the convention of using only one prevalent strain to represent the virions in each infected host. Hence, M(t) is assumed to be equivalent to the number of infections at that time. According to the standard theory (Crow and Kimura 1970; Eyre-Walker 2006; Ruan, Wang, Zhang, et al. 2020), the rate of adaptive evolution can be expressed as
where u is the mutation rate and f is the fate of the mutation (expressed in probability). The rate of adaptive evolution, R(t), is the number of advantageous mutations produced at time t that will become fixed, or at least become prevalent, in the population.
If all mutations are equal in fitness, f = 1/M(t). Thus, the rate of neutral evolution would be R(t) = u and the rate is independent of the population size. For adaptive mutations, f is a function of the selective advantage that is often independent of M(t) (Crow and Kimura 1970; Eyre-Walker 2006; Ruan, Wang, Zhang, et al. 2020). Interestingly, a higher R(t) means more advantageous mutations that promote infections and increase M(t). Therefore, R(t) and M(t) would form a positive feedback loop as indicated by arrows of acceleration:
When such feedbacks are in operation, M(t) would grow like a snowball, leading to out-of-control epidemics. M(t) → R(t) should not be in dispute but the other half, that is, R(t) → M(t), may not be true for most species. In general, adaptive evolution is not manifested as large population sizes, which are usually environmentally limited. In viral evolution, however, an increase in R(t) may be directly translated into a larger population size, given that viral populations can rapidly expand or contract by orders of magnitude.
The work was carried out before the latest wave, the Omicron variant, using data which showed how the Alpha variant, and then the Delta variant arose, the latter probably in India. Interestingly, the team predicted the Omicron wave in the concluding sentence in the abstract to their open access paper published recently in the journal Molecular Biology and Evolution.As stated in the Introduction, there are multiple factors that can accelerate the adaptive evolution of SARS-CoV-2. One of the factors that can be most easily formulated is the viral population size at time t, M(t). Other factors may be no less important, but the M(t) data are readily available. We shall follow the convention of using only one prevalent strain to represent the virions in each infected host. Hence, M(t) is assumed to be equivalent to the number of infections at that time. According to the standard theory (Crow and Kimura 1970; Eyre-Walker 2006; Ruan, Wang, Zhang, et al. 2020), the rate of adaptive evolution can be expressed as
R(t)=M(t)uf,
(1)
where u is the mutation rate and f is the fate of the mutation (expressed in probability). The rate of adaptive evolution, R(t), is the number of advantageous mutations produced at time t that will become fixed, or at least become prevalent, in the population.
If all mutations are equal in fitness, f = 1/M(t). Thus, the rate of neutral evolution would be R(t) = u and the rate is independent of the population size. For adaptive mutations, f is a function of the selective advantage that is often independent of M(t) (Crow and Kimura 1970; Eyre-Walker 2006; Ruan, Wang, Zhang, et al. 2020). Interestingly, a higher R(t) means more advantageous mutations that promote infections and increase M(t). Therefore, R(t) and M(t) would form a positive feedback loop as indicated by arrows of acceleration:
R(t)→M(t) andM(t)→R(t).
(2)
When such feedbacks are in operation, M(t) would grow like a snowball, leading to out-of-control epidemics. M(t) → R(t) should not be in dispute but the other half, that is, R(t) → M(t), may not be true for most species. In general, adaptive evolution is not manifested as large population sizes, which are usually environmentally limited. In viral evolution, however, an increase in R(t) may be directly translated into a larger population size, given that viral populations can rapidly expand or contract by orders of magnitude.
When a pathogen such as SARS-CoV-2 enters a new host, it experiences novel selection pressures and can be expected to evolve adaptations to those pressures. Under the 'right' circumstances, with a rapidly expanding epidemic and exponentially expanding pathogen population, evolution can enter a positive feedback loop leading to a runaway evolution where increasing numbers gives increased probability of novel combinations of mutations arising and forming new 'fitness groups', which then lead to another wave and another cycle of new mutations, and so on. The Chinese team showed that this is exactly what happened to give rise first to the Alpha, then the Delta variants.
The researchers identified four main drivers of this evolutionary process:
- First, the evolution of herd immunity may elicit an arms race between host and pathogen.
- Second, new strains may evolve and compete in the infection of human hosts.
- Third, with “mutations-begetting-mutations” the mutation rate may increase dramatically.
- Fourth, viral adaptive evolution and viral population size may mutually reinforce each other. For example, the emergence of SARS-CoV-2 variants of concern may be driven by acceleration of evolutionary rate as the spread of infection increases.
The evolution of the Delta strain in India. The rises (and falls) of five distinct strains are shown in different colors during the evolution of Delta. The light color indicates the non-Delta strains that eventually disappear. The four colors represent the pre-Delta strains (bearing E, E + M, and E + M+L mutations) as well as the latest Delta strain bearing E + M+L + R mutations. Note that each of the pre-Delta or Delta strains must start with a single haplotype bearing all the characteristic mutations; hence, the increase in frequency in the beginning must be very substantial. At each time point indicated, the portrayed strains add up to 100%. The size of the entire viral population increases with time, but the depiction of the total number corresponds only roughly with the trend. The sets of E, M, L, and R mutations are depicted in figure 2b shown in the figure as 10A2S for ten nonsynonymous and two synonymous mutations in the group. NC is for noncoding mutations.
The evolution of Delta in India followed a four-stage process (Early (E), Middle (M), Late (L) and Recent (R). As the diagram on the left shows, each group of mutations conferred a fitness advantage over the earlier variants. Of the E, M & L groups, the first detection remained at low levels only rising to >1% during February to March, 2021 respectively, and 50% is in April, May, and July. Each of the E, M & L groups conveyed a fitness advantage.
In the UK, where the Delta variant quickly became established, the team found that 20 of the 24 mutations which became Delta were present by December 2020 albeit at very low frequency (≪1%). They remained at a low frequency through April 2021, during which time Alpha was the dominant strain. However, by March 2021, all 24 mutations for Delta were present in the UK and a complete Delta haplotype had been assembled. It then exploded, replacing Alpha. It reached >1% in May, >50% in June and >90% in July. The partial Delta haplotype spread slowly in the UK but once the full 24-mutation haplotype had been assembled, it increased exponentially to become the dominant train, exactly as the team's model predicts.
The team give more details in the abstract to their paper:
AbstractAnd, once again we have a piece of research which refutes creationism and demonstrates the fundamental truth of the Theory of Evolution by Natural Selection in the obvious and superbly-well documented evolution of the SARS-CoV-2 virus, following, as it did, and still does, predicted patterns of evolution giving rise to successive waves of mutated variants. A better illustration of how evolution works, is difficult to imagine. Could this be the reason evangelical Christian frauds are so keen to fool their credulous followers into believing the whole thing is a hoax, so costing tens of thousands of lives of those foolish enough to fall for their deception?
In new epidemics after the host shift, the pathogens may experience accelerated evolution driven by novel selective pressures. When the accelerated evolution enters a positive feedback loop with the expanding epidemics, the pathogen’s runaway evolution may be triggered. To test this possibility in coronavirus disease 2019 (COVID-19), we analyze the extensive databases and identify five major waves of strains, one replacing the previous one in 2020–2021. The mutations differ entirely between waves and the number of mutations continues to increase, from 3-4 to 21-31. The latest wave in the fall of 2021 is the Delta strain which accrues 31 new mutations to become highly prevalent. Interestingly, these new mutations in Delta strain emerge in multiple stages with each stage driven by 6–12 coding mutations that form a fitness group. In short, the evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from the oldest to the youngest wave, and from the earlier to the later stages of the Delta wave, is a process of acceleration with more and more mutations. The global increase in the viral population size (M(t), at time t) and the mutation accumulation (R(t)) may have indeed triggered the runaway evolution in late 2020, leading to the highly evolved Alpha and then Delta strain. To suppress the pandemic, it is crucial to break the positive feedback loop between M(t) and R(t), neither of which has yet to be effectively dampened by late 2021. New waves after Delta, hence, should not be surprising. [My emphasis]
Yongsen Ruan, Mei Hou, Xiaolu Tang, Xionglei He, Xuemei Lu, Jian Lu, Chung-I Wu, Haijun Wen
The Runaway Evolution of SARS-CoV-2 Leading to the Highly Evolved Delta Strain
Molecular Biology and Evolution, Volume 39, Issue 3, March 2022, msac046, DOI: 10.1093/molbev/msac046
Copyright: © [year] The authors. Published by [publisher]
Open access
Reprinted under a Creative Commons Attribution 4.0 International license (CC BY 4.0)
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