Mutation rate of SARS-CoV-2 and emergence of mutators during experimental evolution.

Background and objectives: To understand how organisms evolve, it is fundamental to study how mutations emerge and establish. Here, we estimated the rate of mutation accumulation of SARS-CoV-2 in vitro and investigated the repeatability of its evolution when facing a new cell type but no immune or drug pressures. Methodology: We performed experimental evolution with two strains of SARS-CoV-2, one carrying the originally described spike protein (CoV-2-D) and another carrying the D614G mutation that has spread worldwide (CoV-2-G). After 15 passages in Vero cells and whole genome sequencing, we characterized the spectrum and rate of the emerging mutations and looked for evidences of selection across the genomes of both strains. Results: From the frequencies of the mutations accumulated, and excluding the genes with signals of selection, we estimate a spontaneous mutation rate of 1.3 × 10 -6 ± 0.2 × 10-6 per-base per-infection cycle (mean across both lineages of SARS-CoV-2 ± 2SEM). We further show that mutation accumulation is larger in the CoV-2-D lineage and heterogeneous along the genome, consistent with the action of positive selection on the spike protein, which accumulated five times more mutations than the corresponding genomic average. We also observe the emergence of mutators in the CoV-2-G background, likely linked to mutations in the RNA-dependent RNA polymerase and/or in the error-correcting exonuclease protein. Conclusions and implications: These results provide valuable information on how spontaneous mutations emerge in SARS-CoV-2 and on how selection can shape its genome toward adaptation to new environments. Lay Summary: Each time a virus replicates inside a cell, errors (mutations) occur. Here, via laboratory propagation in cells originally isolated from the kidney epithelium of African green monkeys, we estimated the rate at which the SARS-CoV-2 virus mutates-an important parameter for understanding how it can evolve within and across humans. We also confirm the potential of its Spike protein to adapt to a new environment and report the emergence of mutators-viral populations where mutations occur at a significantly faster rate.

Molecular signatures of resource competition: Clonal interference favors ecological diversification and can lead to incipient speciation.

Microbial ecosystems harbor an astonishing diversity that can persist for long times. To understand how such diversity is structured and maintained, ecological and evolutionary processes need to be integrated at similar timescales. Here, we study a model of resource competition that allows for evolution via de novo mutation, and focus on rapidly adapting asexual populations with large mutational inputs, as typical of many bacteria species. We characterize the adaptation and diversification of an initially maladapted population and show how the eco-evolutionary dynamics are shaped by the interaction between simultaneously emerging lineages - clonal interference. We find that in large populations, more intense clonal interference can foster diversification under sympatry, increasing the probability that phenotypically and genetically distinct clusters coexist. In smaller populations, the accumulation of deleterious and compensatory mutations can push further the diversification process and kick-start speciation. Our findings have implications beyond microbial populations, providing novel insights about the interplay between ecology and evolution in clonal populations.

Competition dynamics in long-term propagations of Schizosaccharomyces pombe strain communities.

Experimental evolution studies with microorganisms such as bacteria and yeast have been an increasingly important and powerful tool to draw long-term inferences of how microbes interact. However, while several strains of the same species often exist in natural environments, many ecology and evolution studies in microbes are typically performed with isogenic populations of bacteria or yeast. In the present study, we firstly perform a genotypic and phenotypic characterization of two laboratory and eight natural strains of the yeast Schizosaccharomyces pombe. We then propagated, in a rich resource environment, yeast communities of 2, 3, 4, and 5 strains for hundreds of generations and asked which fitness-related phenotypes-maximum growth rate or relative competitive fitness-would better predict the outcome of a focal strain during the propagations. While the strain's growth rates would wrongly predict long-term coexistence, pairwise competitive fitness with a focal strain qualitatively predicted the success or extinction of the focal strain by a simple multigenotype population genetics model, given the initial community composition. Interestingly, we have also measured the competitive fitness of the ancestral and evolved communities by the end of the experiment (≈370 generations) and observed frequent maladaptation to the abiotic environment in communities with more than three members. Overall, our results aid establishing pairwise competitive fitness as good qualitative measurement of long-term community composition but also reveal a complex adaptive scenario when trying to predict the evolutionary outcome of those communities.

Dysbiosis individualizes the fitness effect of antibiotic resistance in the mammalian gut.

In the absence of antibiotics, it is essential that antibiotic resistance has a fitness cost for microorganisms if suspending antibiotics treatment is to be a useful strategy for reducing antibiotic resistance. However, the cost of antibiotic resistance within the complex ecosystem of the mammalian gut is not well understood. Here, using mice, we show that the same antibiotic resistance mutation can reduce fitness in one host, while being neutral or even increasing fitness in other hosts. Such antagonistic pleiotropy is shaped by the microbiota because resistance in germ-free mice is consistently costly across all hosts, and the host-specific effect on antibiotic resistance is reduced in hosts with similar microbiotas. Using an eco-evolutionary model of competition for resources, we identify a general mechanism that underlies between-host variation and predicts that the dynamics of compensatory evolution of resistant bacteria should be host specific, a prediction that was supported by experimental evolution in vivo. The microbiome of each human is close to unique, and our results suggest that the short-term cost of resistances and their long-term within-host evolution are also highly personalized, a finding that may contribute to the observed variable outcome of withdrawing antibiotics to reduce resistance levels.