No gene-specific optimization of mutation rate in Escherichia coli

The divergence of mutation rates and spectra across the Tree of Life

Owing to advances in genome sequencing, genome stability has become one of the most scrutinized cellular traits across the Tree of Life. Despite its centrality to all things biological, the mutation rate (per nucleotide site per generation) ranges over three orders of magnitude among species and several-fold within individual phylogenetic lineages. Within all major organismal groups, mutation rates scale negatively with the effective population size of a species and with the amount of functional DNA in the genome. This relationship is most parsimoniously explained by the drift-barrier hypothesis, which postulates that natural selection typically operates to reduce mutation rates until further improvement is thwarted by the power of random genetic drift. Despite this constraint, the molecular mechanisms underlying DNA replication fidelity and repair are free to wander, provided the performance of the entire system is maintained at the prevailing level. The evolutionary flexibility of the mutation rate bears on the resolution of several prior conundrums in phylogenetic and population-genetic analysis and raises challenges for future applications in these areas.

RNase H genes cause distinct impacts on RNA:DNA hybrid formation and mutagenesis genome wide

RNA:DNA hybrids compromise replication fork progression and genome integrity in all cells. The overall impacts of naturally occurring RNA:DNA hybrids on genome integrity, and the relative contributions of ribonucleases H to mitigating the negative effects of hybrids, remain unknown. Here, we investigate the contributions of RNases HII (RnhB) and HIII (RnhC) to hybrid removal, DNA replication, and mutagenesis genome wide. Deletion of either rnhB or rnhC triggers RNA:DNA hybrid accumulation but with distinct patterns of mutagenesis and hybrid accumulation. Across all cells, hybrids accumulate strongly in noncoding RNAs and 5'-UTRs of coding sequences. For ΔrnhB, hybrids accumulate preferentially in untranslated regions and early in coding sequences. We show that hybrid accumulation is particularly sensitive to gene expression in ΔrnhC cells. DNA replication in ΔrnhC cells is disrupted, leading to transversions and structural variation. Our results resolve the outstanding question of how hybrids in native genomic contexts cause mutagenesis and shape genome organization.

RNase H genes cause distinct impacts on RNA:DNA hybrid formation and mutagenesis genome-wide

RNA:DNA hybrids such as R-loops affect genome integrity and DNA replication fork progression. The overall impacts of naturally occurring RNA:DNA hybrids on genome integrity, and the relative contributions of ribonucleases H to mitigating the negative effects of hybrids, remain unknown. Here, we investigate the contributions of RNases HII (RnhB) and HIII (RnhC) to hybrid removal, DNA replication, and mutagenesis genome-wide. Deletion of either rnhB or rnhC triggers RNA:DNA hybrid accumulation, but with distinct patterns of mutagenesis and hybrid accumulation. Across all cells, hybrids accumulate most strongly in non-coding RNAs and 5'-UTRs of coding sequences. For Δ rnhB , hybrids accumulate preferentially in untranslated regions and early in coding sequences. Hybrid accumulation is particularly sensitive to gene expression in Δ rnhC ; in cells lacking RnhC, DNA replication is disrupted leading to transversions and structural variation. Our results resolve the outstanding question of how hybrids in native genomic contexts interact with replication to cause mutagenesis and shape genome organization.

Evolved bacterial resistance to the chemotherapy gemcitabine modulates its efficacy in co-cultured cancer cells

Drug metabolism by the microbiome can influence anticancer treatment success. We previously suggested that chemotherapies with antimicrobial activity can select for adaptations in bacterial drug metabolism that can inadvertently influence the host's chemoresistance. We demonstrated that evolved resistance against fluoropyrimidine chemotherapy lowered its efficacy in worms feeding on drug-evolved bacteria (Rosener et al., 2020). Here, we examine a model system that captures local interactions that can occur in the tumor microenvironment. Gammaproteobacteria-colonizing pancreatic tumors can degrade the nucleoside-analog chemotherapy gemcitabine and, in doing so, can increase the tumor's chemoresistance. Using a genetic screen in Escherichia coli, we mapped all loss-of-function mutations conferring gemcitabine resistance. Surprisingly, we infer that one third of top resistance mutations increase or decrease bacterial drug breakdown and therefore can either lower or raise the gemcitabine load in the local environment. Experiments in three E. coli strains revealed that evolved adaptation converged to inactivation of the nucleoside permease NupC, an adaptation that increased the drug burden on co-cultured cancer cells. The two studies provide complementary insights on the potential impact of microbiome adaptation to chemotherapy by showing that bacteria-drug interactions can have local and systemic influence on drug activity.

What Has Genomics Taught An Evolutionary Biologist?

Genomics, an interdisciplinary field of biology on the structure, function, and evolution of genomes, has revolutionized many subdisciplines of life sciences, including my field of evolutionary biology, by supplying huge data, bringing high-throughput technologies, and offering a new approach to biology. In this review, I describe what I have learned from genomics and highlight the fundamental knowledge and mechanistic insights gained. I focus on three broad topics that are central to evolutionary biology and beyond-variation, interaction, and selection-and use primarily my own research and study subjects as examples. In the next decade or two, I expect that the most important contributions of genomics to evolutionary biology will be to provide genome sequences of nearly all known species on Earth, facilitate high-throughput phenotyping of natural variants and systematically constructed mutants for mapping genotype-phenotype-fitness landscapes, and assist the determination of causality in evolutionary processes using experimental evolution.

Is the mutation rate lower in genomic regions of stronger selective constraints?

A study of the plant Arabidopsis thaliana detected lower mutation rates in genomic regions where mutations are more likely to be deleterious, challenging the principle that mutagenesis is blind to its consequence. To examine the generality of this finding, we analyze large mutational data from baker's yeast and humans. The yeast data do not exhibit this trend, whereas the human data show an opposite trend that disappears upon the control of potential confounders. We find that the Arabidopsis study identified substantially more mutations than reported in the original data-generating studies and expected from Arabidopsis' mutation rate. These extra mutations are enriched in polynucleotide tracts and have relatively low sequencing qualities so are likely sequencing errors. Furthermore, the polynucleotide “mutations” can produce the purported mutational trend in Arabidopsis. Together, our results do not support lower mutagenesis of genomic regions of stronger selective constraints in the plant, fungal, and animal models examined.

Mutation bias reflects natural selection in Arabidopsis thaliana

Since the first half of the twentieth century, evolutionary theory has been dominated by the idea that mutations occur randomly with respect to their consequences1. Here we test this assumption with large surveys of de novo mutations in the plant Arabidopsis thaliana. In contrast to expectations, we find that mutations occur less often in functionally constrained regions of the genome-mutation frequency is reduced by half inside gene bodies and by two-thirds in essential genes. With independent genomic mutation datasets, including from the largest Arabidopsis mutation accumulation experiment conducted to date, we demonstrate that epigenomic and physical features explain over 90% of variance in the genome-wide pattern of mutation bias surrounding genes. Observed mutation frequencies around genes in turn accurately predict patterns of genetic polymorphisms in natural Arabidopsis accessions (r = 0.96). That mutation bias is the primary force behind patterns of sequence evolution around genes in natural accessions is supported by analyses of allele frequencies. Finally, we find that genes subject to stronger purifying selection have a lower mutation rate. We conclude that epigenome-associated mutation bias2 reduces the occurrence of deleterious mutations in Arabidopsis, challenging the prevailing paradigm that mutation is a directionless force in evolution.

Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns

Despite the wide perception that microbes have rapid growth rates, many environments like seawater and soil are often dominated by microorganisms that can only grow very slowly. Our knowledge about growth is necessarily biased toward easily culturable organisms, which tend to be those that grow fast, because microbial growth rates have traditionally been measured using laboratory growth experiments. However, how are potential growth rates distributed in nature? Using genomic data, we predicted the growth rates of over 200,000 organisms, including many as yet uncultivated species. These data reveal how current culture collections are strongly biased toward fast-growing organisms. Finally, we noticed a bimodal distribution of maximal growth rates, suggesting a natural division of microbial growth strategies into two classes.

Maximal growth rate is a basic parameter of microbial lifestyle that varies over several orders of magnitude, with doubling times ranging from a matter of minutes to multiple days. Growth rates are typically measured using laboratory culture experiments. Yet, we lack sufficient understanding of the physiology of most microbes to design appropriate culture conditions for them, severely limiting our ability to assess the global diversity of microbial growth rates. Genomic estimators of maximal growth rate provide a practical solution to survey the distribution of microbial growth potential, regardless of cultivation status. We developed an improved maximal growth rate estimator and predicted maximal growth rates from over 200,000 genomes, metagenome-assembled genomes, and single-cell amplified genomes to survey growth potential across the range of prokaryotic diversity; extensions allow estimates from 16S rRNA sequences alone as well as weighted community estimates from metagenomes. We compared the growth rates of cultivated and uncultivated organisms to illustrate how culture collections are strongly biased toward organisms capable of rapid growth. Finally, we found that organisms naturally group into two growth classes and observed a bias in growth predictions for extremely slow-growing organisms. These observations ultimately led us to suggest evolutionary definitions of oligotrophy and copiotrophy based on the selective regime an organism occupies. We found that these growth classes are associated with distinct selective regimes and genomic functional potentials.

Super-rational aspiration induced strategy updating helps resolve the tragedy of the commons in a cooperation system with exit rights

Avoiding the tragedy of the commons requires altruists to incur some losses to benefit the group. Although specific rules and self-enforcing agreements could help maintain the cooperation system stable, the costly recognition and free-rider problem are still questioned these two cooperation maintenance mechanisms. We here considered the situation of both exit costs and exit benefits in the asymmetric prisoner's dilemma game and introduced a super-rational aspiration induced strategy updating, where players adjust strategies in line with their payoffs and aspirations. If their payoffs reach or exceed the aspiration levels, which may be rational or super-rational, they keep their strategies. Otherwise, they imitate a local neighbor's strategy. We explored this rule in the structured and well-mixed population. The results show that super-rationality and asymmetry could together promote cooperation when exit costs exist. With exit benefit, super-rationality promotes cooperation in both structures and asymmetry only works in the well-mixed population. This suggests that the introduced strategy updating rule could sustain cooperation among egoists with exit rights.

Divergent Evolution of Mutation Rates and Biases in the Long-Term Evolution Experiment with Escherichia coli

All organisms encode enzymes that replicate, maintain, pack, recombine, and repair their genetic material. For this reason, mutation rates and biases also evolve by mutation, variation, and natural selection. By examining metagenomic time series of the Lenski long-term evolution experiment (LTEE) with Escherichia coli (Good BH, McDonald MJ, Barrick JE, Lenski RE, Desai MM. 2017. The dynamics of molecular evolution over 60,000 generations. Nature 551(7678):45–50.), we find that local mutation rate variation has evolved during the LTEE. Each LTEE population has evolved idiosyncratic differences in their rates of point mutations, indels, and mobile element insertions, due to the fixation of various hypermutator and antimutator alleles. One LTEE population, called Ara+3, shows a strong, symmetric wave pattern in its density of point mutations, radiating from the origin of replication. This pattern is largely missing from the other LTEE populations, most of which evolved missense, indel, or structural mutations in topA, fis, and dusB—loci that all affect DNA topology. The distribution of mutations in those genes over time suggests epistasis and historical contingency in the evolution of DNA topology, which may have in turn affected local mutation rates. Overall, the replicate populations of the LTEE have largely diverged in their mutation rates and biases, even though they have adapted to identical abiotic conditions.

Effective Population Size Predicts Local Rates but Not Local Mitigation of Read-through Errors

In correctly predicting that selection efficiency is positively correlated with the effective population size (Ne), the nearly neutral theory provides a coherent understanding of between-species variation in numerous genomic parameters, including heritable error (germline mutation) rates. Does the same theory also explain variation in phenotypic error rates and in abundance of error mitigation mechanisms? Translational read-through provides a model to investigate both issues as it is common, mostly nonadaptive, and has good proxy for rate (TAA being the least leaky stop codon) and potential error mitigation via "fail-safe" 3' additional stop codons (ASCs). Prior theory of translational read-through has suggested that when population sizes are high, weak selection for local mitigation can be effective thus predicting a positive correlation between ASC enrichment and Ne. Contra to prediction, we find that ASC enrichment is not correlated with Ne. ASC enrichment, although highly phylogenetically patchy, is, however, more common both in unicellular species and in genes expressed in unicellular modes in multicellular species. By contrast, Ne does positively correlate with TAA enrichment. These results imply that local phenotypic error rates, not local mitigation rates, are consistent with a drift barrier/nearly neutral model.

Higher Germline Mutagenesis of Genes with Stronger Testis Expressions Refutes the Transcriptional Scanning Hypothesis

Why are more genes expressed in the testis than in any other organ in mammals? The recently proposed transcriptional scanning hypothesis posits that transcription alleviates mutagenesis through transcription-coupled repair so has been selected in the testis to modulate the germline mutation rate in a gene-specific manner. Here, we show that this hypothesis is theoretically untenable because the selection would be too weak to have an effect in mammals. Furthermore, the analysis purported to support the hypothesis did not control known confounding factors and inappropriately excluded genes with no observed de novo mutations. After remedying these problems, we find the human germline mutation rate of a gene to rise with its testis expression level. This trend also exists for inferred coding strand-originated mutations, suggesting that it arises from transcription-associated mutagenesis. Furthermore, the testis expression level of a gene robustly correlates with its overall expression in other organs, nullifying the need to explain the testis silencing of a minority of genes by adaptive germline mutagenesis. Taken together, our results demonstrate that human testis transcription increases the germline mutation rate, rejecting the transcriptional scanning hypothesis of extensive gene expressions in the mammalian testis.

A Dual-Mechanism Antibiotic Kills Gram-Negative Bacteria and Avoids Drug Resistance

The rise of antibiotic resistance and declining discovery of new antibiotics has created a global health crisis. Of particular concern, no new antibiotic classes have been approved for treating Gram-negative pathogens in decades. Here, we characterize a compound, SCH-79797, that kills both Gram-negative and Gram-positive bacteria through a unique dual-targeting mechanism of action (MoA) with undetectably low resistance frequencies. To characterize its MoA, we combined quantitative imaging, proteomic, genetic, metabolomic, and cell-based assays. This pipeline demonstrates that SCH-79797 has two independent cellular targets, folate metabolism and bacterial membrane integrity, and outperforms combination treatments in killing methicillin-resistant Staphylococcus aureus (MRSA) persisters. Building on the molecular core of SCH-79797, we developed a derivative, Irresistin-16, with increased potency and showed its efficacy against Neisseria gonorrhoeae in a mouse vaginal infection model. This promising antibiotic lead suggests that combining multiple MoAs onto a single chemical scaffold may be an underappreciated approach to targeting challenging bacterial pathogens.

The comparative population genetics of Neisseria meningitidis and Neisseria gonorrhoeae

Neisseria meningitidis and N. gonorrhoeae are closely related pathogenic bacteria. To compare their population genetics, we compiled a dataset of 1,145 genes found across 20 N. meningitidis and 15 N. gonorrhoeae genomes. We find that N. meningitidis is seven-times more diverse than N. gonorrhoeae in their combined core genome. Both species have acquired the majority of their diversity by recombination with divergent strains, however, we find that N. meningitidis has acquired more of its diversity by recombination than N. gonorrhoeae. We find that linkage disequilibrium (LD) declines rapidly across the genomes of both species. Several observations suggest that N. meningitidis has a higher effective population size than N. gonorrhoeae; it is more diverse, the ratio of non-synonymous to synonymous polymorphism is lower, and LD declines more rapidly to a lower asymptote in N. meningitidis. The two species share a modest amount of variation, half of which seems to have been acquired by lateral gene transfer and half from their common ancestor. We investigate whether diversity varies across the genome of each species and find that it does. Much of this variation is due to different levels of lateral gene transfer. However, we also find some evidence that the effective population size varies across the genome. We test for adaptive evolution in the core genome using a McDonald–Kreitman test and by considering the diversity around non-synonymous sites that are fixed for different alleles in the two species. We find some evidence for adaptive evolution using both approaches.

Old Trade, New Tricks: Insights into the Spontaneous Mutation Process from the Partnering of Classical Mutation Accumulation Experiments with High-Throughput Genomic Approaches

Mutations spawn genetic variation which, in turn, fuels evolution. Hence, experimental investigations into the rate and fitness effects of spontaneous mutations are central to the study of evolution. Mutation accumulation (MA) experiments have served as a cornerstone for furthering our understanding of spontaneous mutations for four decades. In the pregenomic era, phenotypic measurements of fitness-related traits in MA lines were used to indirectly estimate key mutational parameters, such as the genomic mutation rate, new mutational variance per generation, and the average fitness effect of mutations. Rapidly emerging next-generating sequencing technology has supplanted this phenotype-dependent approach, enabling direct empirical estimates of the mutation rate and a more nuanced understanding of the relative contributions of different classes of mutations to the standing genetic variation. Whole-genome sequencing of MA lines bears immense potential to provide a unified account of the evolutionary process at multiple levels-the genetic basis of variation, and the evolutionary dynamics of mutations under the forces of selection and drift. In this review, we have attempted to synthesize key insights into the spontaneous mutation process that are rapidly emerging from the partnering of classical MA experiments with high-throughput sequencing, with particular emphasis on the spontaneous rates and molecular properties of different mutational classes in nuclear and mitochondrial genomes of diverse taxa, the contribution of mutations to the evolution of gene expression, and the rate and stability of transgenerational epigenetic modifications. Future advances in sequencing technologies will enable greater species representation to further refine our understanding of mutational parameters and their functional consequences.

Metabolic Determinants of Enzyme Evolution in a Genome-Scale Bacterial Metabolic Network

Different genes and proteins evolve at very different rates. To identify the factors that explain these differences is an important aspect of research in molecular evolution. One such factor is the role a protein plays in a large molecular network. Here, we analyze the evolutionary rates of enzyme-coding genes in the genome-scale metabolic network of Escherichia coli to find the evolutionary constraints imposed by the structure and function of this complex metabolic system. Central and highly connected enzymes appear to evolve more slowly than less connected enzymes, but we find that they do so as a by-product of their high abundance, and not because of their position in the metabolic network. In contrast, enzymes catalyzing reactions with high metabolic flux-high substrate to product conversion rates-evolve slowly even after we account for their abundance. Moreover, enzymes catalyzing reactions that are difficult to by-pass through alternative pathways, such that they are essential in many different genetic backgrounds, also evolve more slowly. Our analyses show that an enzyme's role in the function of a metabolic network affects its evolution more than its place in the network's structure. They highlight the value of a system-level perspective for studies of molecular evolution.

The Contribution of Purifying Selection, Linkage, and Mutation Bias to the Negative Correlation between Gene Expression and Polymorphism Density in Yeast Populations

The negative correlation between the rate of protein evolution and expression level of a gene has been recognized as a universal law of the evolutionary biology (Koonin 2011). In our study, we apply a population-based approach to systematically investigate the relative importance of unequal mutation rate, linkage, and selection in the origin of the expression-polymorphism anticorrelation. We analyzed the DNA sequence of protein coding genes of 24 Saccharomyces cerevisiae and 58 Schizosaccharomyces pombe strains. We found that highly expressed genes had a substantially decreased number of polymorphic sites when compared with genes transcribed less extensively. This expression-dependent reduction was especially strong in the nonsynonymous sites, although it was also present in the synonymous sites and untranslated regions, both up and down of a gene. Most importantly, no such trend was found in introns. We used these observations, as well as analyses of site frequency spectra and data from mutation accumulation experiments, to show that the purifying selection acting on nonsynonymous sites was the main, but not exclusive, factor impeding molecular evolution within the coding sequences of highly expressed genes. Linkage could not fully explain the observed pattern of polymorphism within the untranslated regions and synonymous sites, although the contribution of selection acting directly on synonymous variants was extremely small. Finally, we found that the impact of mutational bias was rather negligible.

A Novel Strategy for Detecting Recent Horizontal Gene Transfer and Its Application to Rhizobium Strains

Recent horizontal gene transfer (HGT) is crucial for enabling microbes to rapidly adapt to their novel environments without relying upon rare beneficial mutations that arise spontaneously. For several years now, computational approaches have been developed to detect HGT, but they typically lack the sensitivity and ability to detect recent HGT events. Here we introduce a novel strategy, named RecentHGT. The number of genes undergoing recent HGT between two bacterial genomes was estimated by a new algorithm derived from the expectation-maximization algorithm and is based on the theoretical sequence-similarity distribution of orthologous genes. We tested the proposed strategy by applying it to a set of 10 Rhizobium genomes, and detected several large-scale recent HGT events. We also found that our strategy was more sensitive than other available HGT detection methods. These HGT events were mainly mediated by symbiotic plasmids. Our new strategy can provide clear evidence of recent HGT events and thus it brings us closer to the goal of detecting these potentially adaptive evolution processes in rhizobia as well as pathogens.

Was the Watchmaker Blind? Or Was She One-Eyed?

The question whether evolution is blind is usually presented as a choice between no goals at all ('the blind watchmaker') and long-term goals which would be external to the organism, for example in the form of special creation or intelligent design. The arguments either way do not address the question whether there are short-term goals within rather than external to organisms. Organisms and their interacting populations have evolved mechanisms by which they can harness blind stochasticity and so generate rapid functional responses to environmental challenges. They can achieve this by re-organising their genomes and/or their regulatory networks. Epigenetic as well as DNA changes are involved. Evolution may have no foresight, but it is at least partially directed by organisms themselves and by the populations of which they form part. Similar arguments support partial direction in the evolution of behavior.

Spontaneous Mutation Rate in the Smallest Photosynthetic Eukaryotes

Mutation is the ultimate source of genetic variation, and knowledge of mutation rates is fundamental for our understanding of all evolutionary processes. High throughput sequencing of mutation accumulation lines has provided genome wide spontaneous mutation rates in a dozen model species, but estimates from nonmodel organisms from much of the diversity of life are very limited. Here, we report mutation rates in four haploid marine bacterial-sized photosynthetic eukaryotic algae; Bathycoccus prasinos, Ostreococcus tauri, Ostreococcus mediterraneus, and Micromonas pusilla. The spontaneous mutation rate between species varies from μ = 4.4 × 10-10 to 9.8 × 10-10 mutations per nucleotide per generation. Within genomes, there is a two-fold increase of the mutation rate in intergenic regions, consistent with an optimization of mismatch and transcription-coupled DNA repair in coding sequences. Additionally, we show that deviation from the equilibrium GC content increases the mutation rate by ∼2% to ∼12% because of a GC bias in coding sequences. More generally, the difference between the observed and equilibrium GC content of genomes explains some of the inter-specific variation in mutation rates.

Genetic drift, selection and the evolution of the mutation rate

As one of the few cellular traits that can be quantified across the tree of life, DNA-replication fidelity provides an excellent platform for understanding fundamental evolutionary processes. Furthermore, because mutation is the ultimate source of all genetic variation, clarifying why mutation rates vary is crucial for understanding all areas of biology. A potentially revealing hypothesis for mutation-rate evolution is that natural selection primarily operates to improve replication fidelity, with the ultimate limits to what can be achieved set by the power of random genetic drift. This drift-barrier hypothesis is consistent with comparative measures of mutation rates, provides a simple explanation for the existence of error-prone polymerases and yields a formal counter-argument to the view that selection fine-tunes gene-specific mutation rates.

Comparative Analyses of Selection Operating on Nontranslated Intergenic Regions of Diverse Bacterial Species

Nontranslated intergenic regions (IGRs) compose 10-15% of bacterial genomes, and contain many regulatory elements with key functions. Despite this, there are few systematic studies on the strength and direction of selection operating on IGRs in bacteria using whole-genome sequence data sets. Here we exploit representative whole-genome data sets from six diverse bacterial species: Staphylococcus aureus, Streptococcus pneumoniae, Mycobacterium tuberculosis, Salmonella enterica, Klebsiella pneumoniae, and Escherichia coli We compare patterns of selection operating on IGRs using two independent methods: the proportion of singleton mutations and the d_I/d_S ratio, where d_I is the number of intergenic SNPs per intergenic site. We find that the strength of purifying selection operating over all intergenic sites is consistently intermediate between that operating on synonymous and nonsynonymous sites. Ribosome binding sites and noncoding RNAs tend to be under stronger selective constraint than promoters and Rho-independent terminators. Strikingly, a clear signal of purifying selection remains even when all these major categories of regulatory elements are excluded, and this constraint is highest immediately upstream of genes. While a paucity of variation means that the data for M. tuberculosis are more equivocal than for the other species, we find strong evidence for positive selection within promoters of this species. This points to a key adaptive role for regulatory changes in this important pathogen. Our study underlines the feasibility and utility of gauging the selective forces operating on bacterial IGRs from whole-genome sequence data, and suggests that our current understanding of the functionality of these sequences is far from complete.

Direct Determination of the Mutation Rate in the Bumblebee Reveals Evidence for Weak Recombination-Associated Mutation and an Approximate Rate Constancy in Insects

Accurate knowledge of the mutation rate provides a base line for inferring expected rates of evolution, for testing evolutionary hypotheses and for estimation of key parameters. Advances in sequencing technology now permit direct estimates of the mutation rate from sequencing of close relatives. Within insects there have been three prior such estimates, two in nonsocial insects (Drosophila: 2.8 × 10-9 per bp per haploid genome per generation; Heliconius: 2.9 × 10-9) and one in a social species, the honeybee (3.4 × 10-9). Might the honeybee's rate be ∼20% higher because it has an exceptionally high recombination rate and recombination may be directly or indirectly mutagenic? To address this possibility, we provide a direct estimate of the mutation rate in the bumblebee (Bombus terrestris), this being a close relative of the honeybee but with a much lower recombination rate. We confirm that the crossover rate of the bumblebee is indeed much lower than honeybees (8.7 cM/Mb vs. 37 cM/Mb). Importantly, we find no significant difference in the mutation rates: we estimate for bumblebees a rate of 3.6 × 10-9 per haploid genome per generation (95% confidence intervals 2.38 × 10-9 and 5.37 × 10-9) which is just 5% higher than the estimate that of honeybees. Both genomes have approximately one new mutation per haploid genome per generation. While we find evidence for a direct coupling between recombination and mutation (also seen in honeybees), the effect is so weak as to leave almost no footprint on any between-species differences. The similarity in mutation rates suggests an approximate constancy of the mutation rate in insects.

A Magic Spot in Genome Maintenance

Nucleotide excision repair (NER) is the key DNA repair system that eliminates the majority of DNA helix-distorting lesions. RNA polymerase (RNAP) expedites the recognition of DNA damage by NER components via transcription-coupled DNA repair (TCR). In bacteria, a modified nucleotide ppGpp ('magic spot') is a pleiotropic second messenger that mediates the response to nutrient deficiencies by altering the initiation properties of RNAP. In this review, we discuss newly elucidated roles of guanosine 5'-diphosphate 3'-diphosphate (ppGpp) in transcription elongation that couple this alarmone to DNA damage repair and maintenance.

The Molecular Chaperone DnaK Is a Source of Mutational Robustness

Molecular chaperones, also known as heat-shock proteins, refold misfolded proteins and help other proteins reach their native conformation. Thanks to these abilities, some chaperones, such as the Hsp90 protein or the chaperonin GroEL, can buffer the deleterious phenotypic effects of mutations that alter protein structure and function. Hsp70 chaperones use a chaperoning mechanism different from that of Hsp90 and GroEL, and it is not known whether they can also buffer mutations. Here, we show that they can. To this end, we performed a mutation accumulation experiment in Escherichia coli, followed by whole-genome resequencing. Overexpression of the Hsp70 chaperone DnaK helps cells cope with mutational load and completely avoid the extinctions we observe in lineages evolving without chaperone overproduction. Additionally, our sequence data show that DnaK overexpression increases mutational robustness, the tolerance of its clients to nonsynonymous nucleotide substitutions. We also show that this elevated mutational buffering translates into differences in evolutionary rates on intermediate and long evolutionary time scales. Specifically, we studied the evolutionary rates of DnaK clients using the genomes of E. coli, Salmonella enterica, and 83 other gamma-proteobacteria. We find that clients that interact strongly with DnaK evolve faster than weakly interacting clients. Our results imply that all three major chaperone classes can buffer mutations and affect protein evolution. They illustrate how an individual protein like a chaperone can have a disproportionate effect on the evolution of a proteome.

Is Mutation Random or Targeted?: No Evidence for Hypermutability in Snail Toxin Genes

Ever since Luria and Delbruck, the notion that mutation is random with respect to fitness has been foundational to modern biology. However, various studies have claimed striking exceptions to this rule. One influential case involves toxin-encoding genes in snails of the genus Conus, termed conotoxins, a large gene family that undergoes rapid diversification of their protein-coding sequences by positive selection. Previous reconstructions of the sequence evolution of conotoxin genes claimed striking patterns: (1) elevated synonymous change, interpreted as being due to targeted "hypermutation" in this region; (2) elevated transversion-to-transition ratios, interpreted as reflective of the particular mechanism of hypermutation; and (3) much lower rates of synonymous change in the codons encoding several highly conserved cysteine residues, interpreted as strong position-specific codon bias. This work has spawned a variety of studies on the potential mechanisms of hypermutation and on causes for cysteine codon bias, and has inspired hypermutation hypotheses for various other fast-evolving genes. Here, I show that all three findings are likely to be artifacts of statistical reconstruction. First, by simulating nonsynonymous change I show that high rates of dN can lead to overestimation of dS. Second, I show that there is no evidence for any of these three patterns in comparisons of closely related conotoxin sequences, suggesting that the reported findings are due to breakdown of statistical methods at high levels of sequence divergence. The current findings suggest that mutation and codon bias in conotoxin genes may not be atypical, and that random mutation and selection can explain the evolution of even these exceptional loci.

Links between Transcription, Environmental Adaptation and Gene Variability in Escherichia coli: Correlations between Gene Expression and Gene Variability Reflect Growth Efficiencies

Gene expression is known to be the principle factor explaining how fast genes evolve. Highly transcribed genes evolve slowly because any negative impact caused by a particular mutation is magnified by protein abundance. However, gene expression is a phenotype that depends both on the environment and on the strains or species. We studied this phenotypic plasticity by analyzing the transcriptome profiles of four Escherichia coli strains grown in three different culture media, and explored how expression variability was linked to gene allelic diversity. Genes whose expression changed according to the media and not to the strains were less polymorphic than other genes. Genes for which transcription depended predominantly on the strain were more polymorphic than other genes and were involved in sensing and responding to environmental changes, with an overrepresentation of two-component system genes. Surprisingly, we found that the correlation between transcription and gene diversity was highly variable among growth conditions and could be used to quantify growth efficiency of a strain in a medium. Genetic variability was found to increase with gene expression in poor growth conditions. As such conditions are also characterized by down-regulation of all DNA repair systems, including transcription-coupled repair, we suggest that gene expression under stressful conditions may be mutagenic and thus leads to a variability in mutation rate among genes in the genome which contributes to the pattern of protein evolution.

Rates and mechanisms of bacterial mutagenesis from maximum-depth sequencing

In 1943, Luria and Delbrück used a phage resistance assay to establish spontaneous mutation as a driving force of microbial diversity¹. Mutation rates are still studied using such assays, but these can only examine the small minority of mutations conferring survival in a particular condition. Newer approaches, such as long-term evolution followed by whole-genome sequencing ^{2, 3}, may be skewed by mutational “hot” or “cold” spots ^{3, 4}. Both approaches are affected by numerous caveats ^{5, 6, 7} (see Supplemental Information). We devise a method, Maximum-Depth Sequencing (MDS), to detect extremely rare variants in a population of cells through error-corrected, high-throughput sequencing. We directly measure locus-specific mutation rates in E. coli and show that they vary across the genome by at least an order of magnitude. Our data suggest that certain types of nucleotide misincorporation occur 10⁴-fold more frequently than the basal rate of mutations, but are repaired in vivo. Our data also suggest specific mechanisms of antibiotic-induced mutagenesis, including downregulation of mismatch repair via oxidative stress; transcription-replication conflicts; and in the case of fluoroquinolones, direct damage to DNA.

Nascent RNA folding mitigates transcription-associated mutagenesis

Transcription is mutagenic, in part because the R-loop formed by the binding of the nascent RNA with its DNA template exposes the nontemplate DNA strand to mutagens and primes unscheduled error-prone DNA synthesis. We hypothesize that strong folding of nascent RNA weakens R-loops and hence decreases mutagenesis. By a yeast forward mutation assay, we show that strengthening RNA folding and reducing R-loop formation by synonymous changes in a reporter gene can lower mutation rate by >80%. This effect is diminished after the overexpression of the gene encoding RNase H1 that degrades the RNA in a DNA–RNA hybrid, indicating that the effect is R-loop-dependent. Analysis of genomic data of yeast mutation accumulation lines and human neutral polymorphisms confirms the generality of these findings. This mechanism for local protection of genome integrity is of special importance to highly expressed genes because of their frequent transcription and strong RNA folding, the latter also improves translational fidelity. As a result, strengthening RNA folding simultaneously curtails genotypic and phenotypic mutations.

Determinants of spontaneous mutation in the bacterium Escherichia coli as revealed by whole-genome sequencing

A complete understanding of evolutionary processes requires that factors determining spontaneous mutation rates and spectra be identified and characterized. Using mutation accumulation followed by whole-genome sequencing, we found that the mutation rates of three widely diverged commensal Escherichia coli strains differ only by about 50%, suggesting that a rate of 1-2 × 10(-3) mutations per generation per genome is common for this bacterium. Four major forces are postulated to contribute to spontaneous mutations: intrinsic DNA polymerase errors, endogenously induced DNA damage, DNA damage caused by exogenous agents, and the activities of error-prone polymerases. To determine the relative importance of these factors, we studied 11 strains, each defective for a major DNA repair pathway. The striking result was that only loss of the ability to prevent or repair oxidative DNA damage significantly impacted mutation rates or spectra. These results suggest that, with the exception of oxidative damage, endogenously induced DNA damage does not perturb the overall accuracy of DNA replication in normally growing cells and that repair pathways may exist primarily to defend against exogenously induced DNA damage. The thousands of mutations caused by oxidative damage recovered across the entire genome revealed strong local-sequence biases of these mutations. Specifically, we found that the identity of the 3' base can affect the mutability of a purine by oxidative damage by as much as eightfold.

Mutation--The Engine of Evolution: Studying Mutation and Its Role in the Evolution of Bacteria

Mutation is the engine of evolution in that it generates the genetic variation on which the evolutionary process depends. To understand the evolutionary process we must therefore characterize the rates and patterns of mutation. Starting with the seminal Luria and Delbruck fluctuation experiments in 1943, studies utilizing a variety of approaches have revealed much about mutation rates and patterns and about how these may vary between different bacterial strains and species along the chromosome and between different growth conditions. This work provides a critical overview of the results and conclusions drawn from these studies, of the debate surrounding some of these conclusions, and of the challenges faced when studying mutation and its role in bacterial evolution.

Genetics: Feedforward loop for diversity

DNA-sequence analysis suggests that genetic mutations arise at elevated rates in genomes harbouring high levels of heterozygosity — the state in which the two copies of a genetic region contain sequence differences.

Synonymous Genetic Variation in Natural Isolates of Escherichia coli Does Not Predict Where Synonymous Substitutions Occur in a Long-Term Experiment

Synonymous genetic differences vary by more than 20-fold among genes in natural isolates of Escherichia coli. One hypothesis to explain this heterogeneity is that genes with high levels of synonymous variation mutate at higher rates than genes with low synonymous variation. If so, then one would expect to observe similar mutational patterns in evolution experiments. In fact, however, the pattern of synonymous substitutions in a long-term evolution experiment with E. coli does not support this hypothesis. In particular, the extent of synonymous variation across genes in that experiment does not reflect the variation observed in natural isolates of E. coli. Instead, gene length alone predicts with high accuracy the prevalence of synonymous changes in the experimental populations. We hypothesize that patterns of synonymous variation in natural E. coli populations are instead caused by differences across genomic regions in their effective population size that, in turn, reflect different histories of recombination, horizontal gene transfer, selection, and population structure.

Determinants of the rate of protein sequence evolution

The rate and mechanism of protein sequence evolution have been central questions in evolutionary biology since the 1960s. Although the rate of protein sequence evolution depends primarily on the level of functional constraint, exactly what determines functional constraint has remained unclear. The increasing availability of genomic data has enabled much needed empirical examinations on the nature of functional constraint. These studies found that the evolutionary rate of a protein is predominantly influenced by its expression level rather than functional importance. A combination of theoretical and empirical analyses has identified multiple mechanisms behind these observations and demonstrated a prominent role in protein evolution of selection against errors in molecular and cellular processes.

Studying tumorigenesis through network evolution and somatic mutational perturbations in the cancer interactome

Cells govern biological functions through complex biological networks. Perturbations to networks may drive cells to new phenotypic states, for example, tumorigenesis. Identifying how genetic lesions perturb molecular networks is a fundamental challenge. This study used large-scale human interactome data to systematically explore the relationship among network topology, somatic mutation, evolutionary rate, and evolutionary origin of cancer genes. We found the unique network centrality of cancer proteins, which is largely independent of gene essentiality. Cancer genes likely have experienced a lower evolutionary rate and stronger purifying selection than those of noncancer, Mendelian disease, and orphan disease genes. Cancer proteins tend to have ancient histories, likely originated in early metazoan, although they are younger than proteins encoded by Mendelian disease genes, orphan disease genes, and essential genes. We found that the protein evolutionary origin (age) positively correlates with protein connectivity in the human interactome. Furthermore, we investigated the network-attacking perturbations due to somatic mutations identified from 3,268 tumors across 12 cancer types in The Cancer Genome Atlas. We observed a positive correlation between protein connectivity and the number of nonsynonymous somatic mutations, whereas a weaker or insignificant correlation between protein connectivity and the number of synonymous somatic mutations. These observations suggest that somatic mutational network-attacking perturbations to hub genes play an important role in tumor emergence and evolution. Collectively, this work has broad biomedical implications for both basic cancer biology and the development of personalized cancer therapy.

Precise estimates of mutation rate and spectrum in yeast

Mutation is the ultimate source of genetic variation. The most direct and unbiased method of studying spontaneous mutations is via mutation accumulation (MA) lines. Until recently, MA experiments were limited by the cost of sequencing and thus provided us with small numbers of mutational events and therefore imprecise estimates of rates and patterns of mutation. We used whole-genome sequencing to identify nearly 1,000 spontaneous mutation events accumulated over ∼311,000 generations in 145 diploid MA lines of the budding yeast Saccharomyces cerevisiae. MA experiments are usually assumed to have negligible levels of selection, but even mild selection will remove strongly deleterious events. We take advantage of such patterns of selection and show that mutation classes such as indels and aneuploidies (especially monosomies) are proportionately much more likely to contribute mutations of large effect. We also provide conservative estimates of indel, aneuploidy, environment-dependent dominant lethal, and recessive lethal mutation rates. To our knowledge, for the first time in yeast MA data, we identified a sufficiently large number of single-nucleotide mutations to measure context-dependent mutation rates and were able to (i) confirm strong AT bias of mutation in yeast driven by high rate of mutations from C/G to T/A and (ii) detect a higher rate of mutation at C/G nucleotides in two specific contexts consistent with cytosine methylation in S. cerevisiae.

Geptop: a gene essentiality prediction tool for sequenced bacterial genomes based on orthology and phylogeny

Integrative genomics predictors, which score highly in predicting bacterial essential genes, would be unfeasible in most species because the data sources are limited. We developed a universal approach and tool designated Geptop, based on orthology and phylogeny, to offer gene essentiality annotations. In a series of tests, our Geptop method yielded higher area under curve (AUC) scores in the receiver operating curves than the integrative approaches. In the ten-fold cross-validations among randomly upset samples, Geptop yielded an AUC of 0.918, and in the cross-organism predictions for 19 organisms Geptop yielded AUC scores between 0.569 and 0.959. A test applied to the very recently determined essential gene dataset from the Porphyromonas gingivalis, which belongs to a phylum different with all of the above 19 bacterial genomes, gave an AUC of 0.77. Therefore, Geptop can be applied to any bacterial species whose genome has been sequenced. Compared with the essential genes uniquely identified by the lethal screening, the essential genes predicted only by Gepop are associated with more protein-protein interactions, especially in the three bacteria with lower AUC scores (<0.7). This may further illustrate the reliability and feasibility of our method in some sense. The web server and standalone version of Geptop are available at http://cefg.uestc.edu.cn/geptop/ free of charge. The tool has been run on 968 bacterial genomes and the results are accessible at the website.