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A central question in evolutionary biology is why some species have more genetic diversity than others and a no less important question is why selection efficacy varies among species. Although these questions have started to be tackled in animals, they have not been addressed to the same extent in plants. Here, we estimated nucleotide diversity at synonymous, πS, and nonsynonymous sites, πN, and a measure of the efficacy of selection, the ratio πN/πS, in 34 animal and 28 plant species using full genome data. We then evaluated the relationship of nucleotide diversity and selection efficacy with effective population size, the distribution of fitness effect and life history traits. In animals, our data confirm that longevity and propagule size are the variables that best explain the variation in πS among species. In plants longevity also plays a major role as well as mating system. As predicted by the nearly neutral theory of molecular evolution, the log of πN/πS decreased linearly with the log of πS but the slope was weaker in plants than in animals. This appears to be due to a higher mutation rate in long lived plants, and the difference disappears when πS is rescaled by the mutation rate. Differences in the distribution of fitness effect of new mutations also contributed to variation in πN/πS among species.

Comprehensive genotype–phenotype mapping of the green fluorescent protein shows that the local fitness peak is narrow, shaped by a high prevalence of epistatic interactions, providing for the loss of fluorescence when the joint effect of mutations exceeds a threshold. Genotype to phenotype mapping of a model protein Fyodor Kondrashov and colleagues report comprehensive genotype–phenotype mapping across an entire protein, based on analysis of the fitness landscape of green fluorescent protein (GFP) using a molecular barcoding and sequencing approach. They find that the fitness landscape is characterized by locally narrow regions, combined with a high prevalence of epistatic interactions, providing for the loss of fluorescence when the joint effect of mutations exceeds a threshold. Fitness landscapes 1 , 2 depict how genotypes manifest at the phenotypic level and form the basis of our understanding of many areas of biology 2 , 3 , 4 , 5 , 6 , 7 , yet their properties remain elusive. Previous studies have analysed specific genes, often using their function as a proxy for fitness 2 , 4 , experimentally assessing the effect on function of single mutations and their combinations in a specific sequence 2 , 5 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 or in different sequences 2 , 3 , 5 , 16 , 17 , 18 . However, systematic high-throughput studies of the local fitness landscape of an entire protein have not yet been reported. Here we visualize an extensive region of the local fitness landscape of the green fluorescent protein from Aequorea victoria (avGFP) by measuring the native function (fluorescence) of tens of thousands of derivative genotypes of avGFP. We show that the fitness landscape of avGFP is narrow, with 3/4 of the derivatives with a single mutation showing reduced fluorescence and half of the derivatives with four mutations being completely non-fluorescent. The narrowness is enhanced by epistasis, which was detected in up to 30% of genotypes with multiple mutations and mostly occurred through the cumulative effect of slightly deleterious mutations causing a threshold-like decrease in protein stability and a concomitant loss of fluorescence. A model of orthologous sequence divergence spanning hundreds of millions of years predicted the extent of epistasis in our data, indicating congruence between the fitness landscape properties at the local and global scales. The characterization of the local fitness landscape of avGFP has important implications for several fields including molecular evolution, population genetics and protein design.

The BA.2 sublineage of the SARS-CoV-2 Omicron variant has become dominant in most countries around the world; however, the prevalence of BA.4 and BA.5 is increasing rapidly in several regions. BA.2 is less pathogenic in animal models than previously circulating variants of concern 1 – 4 . Compared with BA.2, however, BA.4 and BA.5 possess additional substitutions in the spike protein, which play a key role in viral entry, raising concerns that the replication capacity and pathogenicity of BA.4 and BA.5 are higher than those of BA.2. Here we have evaluated the replicative ability and pathogenicity of BA.4 and BA.5 isolates in wild-type Syrian hamsters, human ACE2 (hACE2) transgenic hamsters and hACE2 transgenic mice. We have observed no obvious differences among BA.2, BA.4 and BA.5 isolates in growth ability or pathogenicity in rodent models, and less pathogenicity compared to a previously circulating Delta (B.1.617.2 lineage) isolate. In addition, in vivo competition experiments revealed that BA.5 outcompeted BA.2 in hamsters, whereas BA.4 and BA.2 exhibited similar fitness. These findings suggest that BA.4 and BA.5 clinical isolates have similar pathogenicity to BA.2 in rodents and that BA.5 possesses viral fitness superior to that of BA.2. Results indicate that the sublineages BA.4 and BA.5 of SARS-CoV-2 Omicron variants have similar pathogenicity to that of the BA.2 sublineage in rodents, highlighting the importance of evaluating viral replication and pathogenesis using clinical isolates.

The unprecedented rate of extinction calls for efficient use of genetics to help conserve biodiversity. Several recent genomic and simulation-based studies have argued that the field of conservation biology has placed too much focus on conserving genome-wide genetic variation, and that the field should instead focus on managing the subset of functional genetic variation that is thought to affect fitness. Here, we critically evaluate the feasibility and likely benefits of this approach in conservation. We find that population genetics theory and empirical results show that conserving genome-wide genetic variation is generally the best approach to prevent inbreeding depression and loss of adaptive potential from driving populations toward extinction. Focusing conservation efforts on presumably functional genetic variation will only be feasible occasionally, often misleading, and counterproductive when prioritized over genome-wide genetic variation. Given the increasing rate of habitat loss and other environmental changes, failure to recognize the detrimental effects of lost genome-wide genetic variation on long-term population viability will only worsen the biodiversity crisis.

The seasonal human influenza A/H3N2 virus undergoes rapid evolution, which produces significant year-to-year sequence turnover in the population of circulating strains. Adaptive mutations respond to human immune challenge and occur primarily in antigenic epitopes, the antibody-binding domains of the viral surface protein haemagglutinin. Here we develop a fitness model for haemagglutinin that predicts the evolution of the viral population from one year to the next. Two factors are shown to determine the fitness of a strain: adaptive epitope changes and deleterious mutations outside the epitopes. We infer both fitness components for the strains circulating in a given year, using population-genetic data of all previous strains. From fitness and frequency of each strain, we predict the frequency of its descendent strains in the following year. This fitness model maps the adaptive history of influenza A and suggests a principled method for vaccine selection. Our results call for a more comprehensive epidemiology of influenza and other fast-evolving pathogens that integrates antigenic phenotypes with other viral functions coupled by genetic linkage. A computational approach for predicting the future evolution of the human influenza virus, based on population-genetic data of previous strains, is presented; this model holds promise for improving vaccine strain selection for seasonal influenza. Keeping tabs on influenza virus evolution Marta Łuksza and Michael Lässig present a computational approach for predicting the future evolution of influenza virus. The authors develop a fitness model for the influenza haemagglutinin protein, based on population-genetic data from all previous strains, that allows them to predict the future evolution of currently existing clades. This computational model holds promise for improving vaccine strain selection.

IncHI2 is a common type of large mcr-1-carrying plasmids that have been found worldwide. Large plasmids could impose metabolic burden for host bacterial strains, we therefore examine the stability and fitness cost of a mcr-1-carrying 265.5-kb IncHI2 plasmid, pMCR1_1943, in Escherichia coli in nutrient-rich LB and nutrient-restricted M9 broth. Stability tests revealed that pMCR1_1943 was stably maintained with a stability frequency of 0.99±0.01 (mean ± standard deviation) after 880 generations in LB and 0.97±0.00 after 220 generations in M9 broth. Relative fitness (expressed as w, defined as relative fitness of the plasmid-carrying strain compared to the plasmid-free progenitor strain) was examined using the 24-h head to head competitions. pMCR1_1943 initially imposed costs (w, 0.88±0.03 in LB, 0.87±0.01 in M9) but such costs were largely reduced after 14-day cultures (w, 0.97±0.03 in LB, 0.95±0.03 in M9). The stable maintenance and the largely compensated cost after passage may contribute to the wide spread of mcr-1-carrying IncHI2 plasmids. To investigate potential mechanisms for the reduced fitness cost, we performed whole genome sequencing and single nucleotide polymorphism calling for the competitor strains. We identified that molecular chaperone-encoding dnaK, cell division protein-encoding cpoB and repeat protein-encoding rhsC were associated with the cost reduction for pMCR1_1943, which may represent new mechanisms for host bacterial strains to compensate fitness costs imposed by large plasmids and warrant further studies.

Synonymous mutations in protein-coding genes do not alter protein sequences and are thus generally presumed to be neutral or nearly neutral 1 – 5 . Here, to experimentally verify this presumption, we constructed 8,341 yeast mutants each carrying a synonymous, nonsynonymous or nonsense mutation in one of 21 endogenous genes with diverse functions and expression levels and measured their fitness relative to the wild type in a rich medium. Three-quarters of synonymous mutations resulted in a significant reduction in fitness, and the distribution of fitness effects was overall similar—albeit nonidentical—between synonymous and nonsynonymous mutations. Both synonymous and nonsynonymous mutations frequently disturbed the level of mRNA expression of the mutated gene, and the extent of the disturbance partially predicted the fitness effect. Investigations in additional environments revealed greater across-environment fitness variations for nonsynonymous mutants than for synonymous mutants despite their similar fitness distributions in each environment, suggesting that a smaller proportion of nonsynonymous mutants than synonymous mutants are always non-deleterious in a changing environment to permit fixation, potentially explaining the common observation of substantially lower nonsynonymous than synonymous substitution rates. The strong non-neutrality of most synonymous mutations, if it holds true for other genes and in other organisms, would require re-examination of numerous biological conclusions about mutation, selection, effective population size, divergence time and disease mechanisms that rely on the assumption that synoymous mutations are neutral. A survey of 8,341 mutations in 21 yeast genes shows that synonymous mutations are nearly as harmful as nonsynonymous mutations, in part because they both affect the mRNA level of the gene mutated.

Advances in DNA sequencing technology and computation now enable genome-wide scans for natural selection to be conducted on unprecedented scales. By examining patterns of sequence variation among individuals, biologists are identifying genes and variants that affect fitness. Despite this progress, most population genetic methods for characterizing selection assume that variants mutate in a simple manner and at a low rate. Because these assumptions are violated by repetitive sequences, selection remains uncharacterized for an appreciable percentage of the genome. To meet this challenge, we focus on microsatellites, repetitive variants that mutate orders of magnitude faster than single nucleotide variants, can harbor substantial variation, and are known to influence biological function in some cases. We introduce four general models of natural selection that are each characterized by just two parameters, are easily simulated, and are specifically designed for microsatellites. Using a random forests approach to approximate Bayesian computation, we fit these models to carefully chosen microsatellites genotyped in 200 humans from a diverse collection of eight populations. Altogether, we reconstruct detailed fitness landscapes for 43 microsatellites we classify as targets of selection. Microsatellite fitness surfaces are diverse, including a range of selection strengths, contributions from dominance, and variation in the number and size of optimal alleles. Microsatellites that are subject to selection include loci known to cause trinucleotide expansion disorders and modulate gene expression, as well as intergenic loci with no obvious function. The heterogeneity in fitness landscapes we report suggests that genome-scale analyses like those used to assess selection targeting single nucleotide variants run the risk of oversimplifying the evolutionary dynamics of microsatellites. Moreover, our fitness landscapes provide a valuable visualization of the selective dynamics navigated by microsatellites.

Quantifying the pathogenicity of protein variants in human disease-related genes would have a marked effect on clinical decisions, yet the overwhelming majority (over 98%) of these variants still have unknown consequences 1 – 3 . In principle, computational methods could support the large-scale interpretation of genetic variants. However, state-of-the-art methods 4 – 10 have relied on training machine learning models on known disease labels. As these labels are sparse, biased and of variable quality, the resulting models have been considered insufficiently reliable 11 . Here we propose an approach that leverages deep generative models to predict variant pathogenicity without relying on labels. By modelling the distribution of sequence variation across organisms, we implicitly capture constraints on the protein sequences that maintain fitness. Our model EVE (evolutionary model of variant effect) not only outperforms computational approaches that rely on labelled data but also performs on par with, if not better than, predictions from high-throughput experiments, which are increasingly used as evidence for variant classification 12 – 16 . We predict the pathogenicity of more than 36 million variants across 3,219 disease genes and provide evidence for the classification of more than 256,000 variants of unknown significance. Our work suggests that models of evolutionary information can provide valuable independent evidence for variant interpretation that will be widely useful in research and clinical settings. A new computational method, EVE, classifies human genetic variants in disease genes using deep generative models trained solely on evolutionary sequences.

Population-level spacer diversity is a key fitness determinant of CRISPR-Cas adaptive immune systems because it limits the emergence of escape virus. Diversity in CRISPR-Cas immunity The CRISPR-Cas adaptive immune system of prokaryotes uses spacer sequences derived from viruses and other invading mobile genetic elements to target and destroy these elements in a sequence-specific manner. As a result, spacer diversity is often high in bacterial populations, but its importance has been unclear as it has been proposed that viruses overcome spacer diversity by evolving escape mutations. Using experimental evolution to track the co-evolution of a Pseudomonas aeruginosa bacteriophage with its host bacterium, Edze Westra and colleagues demonstrate that a high level of spacer diversity actually drives the virus population to extinction, as a result of the synergy between spacer diversity and the high specificity of viral infection. These data reveal that high spacer diversity is key to the success of CRISPR-Cas immunity as it limits the ability of viruses to escape. Prokaryotic CRISPR-Cas adaptive immune systems insert spacers derived from viruses and other parasitic DNA elements into CRISPR loci to provide sequence-specific immunity 1 , 2 . This frequently results in high within-population spacer diversity 3 , 4 , 5 , 6 , but it is unclear if and why this is important. Here we show that, as a result of this spacer diversity, viruses can no longer evolve to overcome CRISPR-Cas by point mutation, which results in rapid virus extinction. This effect arises from synergy between spacer diversity and the high specificity of infection, which greatly increases overall population resistance. We propose that the resulting short-lived nature of CRISPR-dependent bacteria–virus coevolution has provided strong selection for the evolution of sophisticated virus-encoded anti-CRISPR mechanisms 7 .