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.

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.

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.

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

How many incoming travelers ( 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? would be informative about the initiation and progression of epidemics. To obtain , 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 < 10, which could have been achieved by one infected traveler in a long-distance flight.

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 , , and 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.

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.

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.

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.