Intra- vs. Interhost Evolution of SARS-CoV-2 Driven by Uncorrelated Selection-The Evolution Thwarted.

In viral evolution, a new mutation has to proliferate within the host (Stage I) in order to be transmitted and then compete in the host population (Stage II). We now analyze the intrahost single nucleotide variants (iSNVs) in a set of 79 SARS-CoV-2 infected patients with most transmissions tracked. Here, every mutation has two measures: 1) iSNV frequency within each individual host in Stage I; 2) occurrence among individuals ranging from 1 (private), 2-78 (public), to 79 (global) occurrences in Stage II. In Stage I, a small fraction of nonsynonymous iSNVs are sufficiently advantageous to rise to a high frequency, often 100%. However, such iSNVs usually fail to become public mutations. Thus, the selective forces in the two stages of evolution are uncorrelated and, possibly, antagonistic. For that reason, successful mutants, including many variants of concern, have to avoid being eliminated in Stage I when they first emerge. As a result, they may not have the transmission advantage to outcompete the dominant strains and, hence, are rare in the host population. Few of them could manage to slowly accumulate advantageous mutations to compete in Stage II. When they do, they would appear suddenly as in each of the six successive waves of SARS-CoV-2 strains. In conclusion, Stage I evolution, the gate-keeper, may contravene the long-term viral evolution and should be heeded in viral studies.

The Runaway Evolution of SARS-CoV-2 Leading to the Highly Evolved Delta Strain.

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.

Prevalence and spectrum of DNA mismatch repair gene variation in the general Chinese population.

BACKGROUND: Identifying genetic disease-susceptible individuals through population screening is considered as a promising approach for disease prevention. DNA mismatch repair (MMR) genes including MLH1, MSH2, MSH6 and PMS2 play essential roles in maintaining microsatellite stability through DNA mismatch repair, and pathogenic variation in MMR genes causes microsatellite instability and is the genetic predisposition for cancer as represented by the Lynch syndrome. While the prevalence and spectrum of MMR variation has been extensively studied in cancer, it remains largely elusive in the general population. Lack of the knowledge prevents effective prevention for MMR variation-caused cancer. In the current study, we addressed the issue by using the Chinese population as a model. METHODS: We performed extensive data mining to collect MMR variant data from 18 844 ethnic Chinese individuals and comprehensive analyses for the collected MMR variants to determine its prevalence, spectrum and features of the MMR data in the Chinese population. RESULTS: We identified 17 687 distinct MMR variants. We observed substantial differences of MMR variation between the general Chinese population and Chinese patients with cancer, identified highly Chinese-specific MMR variation through comparing MMR data between Chinese and non-Chinese populations, predicted the enrichment of deleterious variants in the unclassified Chinese-specific MMR variants, determined MMR pathogenic prevalence of 0.18% in the general Chinese population and determined that MMR variation in the general Chinese population is evolutionarily neutral. CONCLUSION: Our study provides a comprehensive view of MMR variation in the general Chinese population, a resource for biological study of human MMR variation, and a reference for MMR-related cancer applications.

Mutations Beget More Mutations-Rapid Evolution of Mutation Rate in Response to the Risk of Runaway Accumulation.

The rapidity with which the mutation rate evolves could greatly impact evolutionary patterns. Nevertheless, most studies simply assume a constant rate in the time scale of interest (Kimura 1983; Drake 1991; Kumar 2005; Li 2007; Lynch 2010). In contrast, recent studies of somatic mutations suggest that the mutation rate may vary by several orders of magnitude within a lifetime (Kandoth et al. 2013; Lawrence et al. 2013). To resolve the discrepancy, we now propose a runaway model, applicable to both the germline and soma, whereby mutator mutations form a positive-feedback loop. In this loop, any mutator mutation would increase the rate of acquiring the next mutator, thus triggering a runaway escalation in mutation rate. The process can be initiated more readily if there are many weak mutators than a few strong ones. Interestingly, even a small increase in the mutation rate at birth could trigger the runaway process, resulting in unfit progeny. In slowly reproducing species, the need to minimize the risk of this uncontrolled accumulation would thus favor setting the mutation rate low. In comparison, species that starts and ends reproduction sooner do not face the risk and may set the baseline mutation rate higher. The mutation rate would evolve in response to the risk of runaway mutation, in particular, when the generation time changes. A rapidly evolving mutation rate may shed new lights on many evolutionary phenomena (Elango et al. 2006; Thomas et al. 2010, 2018; Langergraber et al. 2012; Besenbacher et al. 2019).

The twin-beginnings of COVID-19 in Asia and Europe-one prevails quickly.

In the spread of SARS-CoV-2, there have been multiple waves of replacement between strains, each of which having a distinct set of mutations. The first wave is a group of four mutations (C241T, C3037T, C14408T and A23403G [this being the amino acid change D614G]; all designated 0 to 1 below). This DG (D614G) group, fixed at the start of the pandemic, is the foundation of all subsequent waves of strains. Curiously, the DG group is absent in early Asian samples but present (and likely common) in Europe from the beginning. European data show that the high fitness of DG1111 requires the synergistic effect of all four mutations. However, the European strains would have had no time to evolve the four DG mutations (0 to 1), had they come directly from the early Asian DG0000 strain. Very likely, the European DG1111 strain had acquired the highly adaptive DG mutations in pre-pandemic Europe and had been spreading in parallel with the Asian strains. Two recent reports further support this twin-beginning interpretation. There was a period of two-way spread between Asia and Europe but, by May 2020, the European strains had supplanted the Asian strains globally. This large-scale replacement of one set of mutations for another has since been replayed many times as COVID-19 progresses.

Very large hidden genetic diversity in one single tumor: evidence for tumors-in-tumor.

Despite the concern of within-tumor genetic diversity, this diversity is in fact limited by the kinship among cells in the tumor. Indeed, genomic studies have amply supported the 'Nowell dogma' whereby cells of the same tumor descend from a single progenitor cell. In parallel, genomic data also suggest that the diversity could be >10-fold larger if tumor cells are of multiple origins. We develop an evolutionary hypothesis that a single tumor may often harbor multiple cell clones of independent origins, but only one would be large enough to be detected. To test the hypothesis, we search for independent tumors within a larger one (or tumors-in-tumor). Very high density sampling was done on two cases of colon tumors. Case 1 indeed has 13 independent clones of disparate sizes, many having heavy mutation burdens and potentially highly tumorigenic. In Case 2, despite a very intensive search, only two small independent clones could be found. The two cases show very similar movements and metastasis of the dominant clone. Cells initially move actively in the expanding tumor but become nearly immobile in late stages. In conclusion, tumors-in-tumor are plausible but could be very demanding to find. Despite their small sizes, they can enhance the within-tumor diversity by orders of magnitude. Such increases may contribute to the missing genetic diversity associated with the resistance to cancer therapy.

A theoretical exploration of the origin and early evolution of a pandemic.

A virus that can cause a global pandemic must be highly adaptive to human conditions. Such adaptation is not likely to have emerged suddenly but, instead, may have evolved step by step with each step favored by natural selection. It is thus necessary to develop a theory about the origin in order to guide the search. Here, we propose such a model whereby evolution occurs in both the virus and the hosts (where the evolution is somatic; i.e., in the immune system). The hosts comprise three groups - the wild animal hosts, the nearby human population, and farther-away human populations. The theory suggests that the conditions under which the pandemic has initially evolved are: (i) an abundance of wild animals in the place of origin (PL0); (ii) a nearby human population of low density; (iii) frequent and long-term animal-human contacts to permit step-by-step evolution; and (iv) a level of herd immunity in the animal and human hosts. In this model, the evolving virus may have regularly spread out of PL0 although such invasions often fail, leaving sporadic cases of early infections. The place of the first epidemic (PL1), where humans are immunologically naïve to the virus, is likely a distance away from PL0. Finally, this current model is only a first attempt and more theoretical models can be expected to guide the search for the origin of SARS-CoV-2.

On the founder effect in COVID-19 outbreaks: how many infected travelers may have started them all?

How many incoming travelers (I0 at time 0, equivalent to the 'founders' in evolutionary genetics) infected with SARS-CoV-2 who visit or return to a region could have started the epidemic of that region? I0 would be informative about the initiation and progression of epidemics. To obtain I0 , we analyze the genetic divergence among viral populations of different regions. By applying the 'individual-output' model of genetic drift to the SARS-CoV-2 diversities, we obtain I0 < 10, which could have been achieved by one infected traveler in a long-distance flight. The conclusion is robust regardless of the source population, the continuation of inputs (It for t > 0) or the fitness of the variants. With such a tiny trickle of human movement igniting many outbreaks, the crucial stage of repressing an epidemic in any region should, therefore, be the very first sign of local contagion when positive cases first become identifiable. The implications of the highly 'portable' epidemics, including their early evolution prior to any outbreak, are explored in the companion study (Ruan et al., personal communication).

Dynamic global analysis of transcription reveals the role of miRNAs in synergistic stabilization of gene expression.

Buffering exogenous perturbation is crucial to maintain transcriptional homeostasis during development. While miRNAs have been speculated to play a role in stability maintenance, previous studies seeking to check this conjecture focused on measurements of transcript levels at steady state or involved individual miRNA targets. We measured whole-genome expression dynamics by introducing a transient perturbation and establishing a perturbation and recovery system in Drosophila larvae. We inhibited all transcription and assayed transcriptomes at several time points during recovery from inhibition. We performed these experiments in the wild type and miRNA-deficient genetic backgrounds. Consistent with theories about miRNAs' function in stabilizing the transcriptome, we find that attenuating miRNA expression leads to weak impairment in degradation of targets but strong destabilization of target genes when transcription is re-activated. We further fitted a model that captures the essential aspects of transcription dynamics in our experiments and found that the miRNA target transcripts uniformly overshoot the original steady state as they recover from a general inhibition of transcription if global miRNA levels are reduced. Collectively, our results provide experimental evidence for the idea that miRNAs act cumulatively to stabilize the transcriptional regulatory network. We therefore found a promising approach to assess the effect of these molecules on transcription dynamics.