A biophysical protein folding model accounts for most mutational fitness effects in viruses

Fitness effects of mutations fall on a continuum ranging from lethal to deleterious to beneficial. The distribution of fitness effects (DFE) among random mutations is an essential component of every evolutionary model and a mathematical portrait of robustness. Recent experiments on five viral species all revealed a characteristic bimodal shaped DFE, featuring peaks at neutrality and lethality. However, the phenotypic causes underlying observed fitness effects are still unknown, and presumably thought to vary unpredictably from one mutation to another. By combining population genetics simulations with a simple biophysical protein folding model, we show that protein thermodynamic stability accounts for a large fraction of observed mutational effects. We assume that moderately destabilizing mutations inflict a fitness penalty proportional to the reduction in folded protein, which depends continuously on folding free energy (\Delta G). Most mutations in our model affect fitness by altering \Delta G, while, based on simple estimates, \approx10% abolish activity and are unconditionally lethal. Mutations pushing \Delta G>0 are also considered lethal. Contrary to neutral network theory, we find that, in mutation/selection/drift steady-state, high mutation rates (m) lead to less stable proteins and a more dispersed DFE, i.e. less mutational robustness. Small population size (N) also decreases stability and robustness. In our model, a continuum of non-lethal mutations reduces fitness by \approx2% on average, while \approx10-35% of mutations are lethal, depending on N and m. Compensatory mutations are common in small populations with high mutation rates. More broadly, we conclude that interplay between biophysical and population genetic forces shapes the DFE.Comment: Main text: 12 pages, 5 figures Supplementary Information: 10 pages, 5 figure

Pervasive Sign Epistasis between Conjugative Plasmids and Drug-Resistance Chromosomal Mutations

Multidrug-resistant bacteria arise mostly by the accumulation of plasmids and chromosomal mutations. Typically, these resistant determinants are costly to the bacterial cell. Yet, recently, it has been found that, in Escherichia coli bacterial cells, a mutation conferring resistance to an antibiotic can be advantageous to the bacterial cell if another antibiotic-resistance mutation is already present, a phenomenon called sign epistasis. Here we study the interaction between antibiotic-resistance chromosomal mutations and conjugative (i.e., self-transmissible) plasmids and find many cases of sign epistasis (40%)—including one of reciprocal sign epistasis where the strain carrying both resistance determinants is fitter than the two strains carrying only one of the determinants. This implies that the acquisition of an additional resistance plasmid or of a resistance mutation often increases the fitness of a bacterial strain already resistant to antibiotics. We further show that there is an overall antagonistic interaction between mutations and plasmids (52%). These results further complicate expectations of resistance reversal by interdiction of antibiotic use

Replication Fork Reactivation in a dnaC2 Mutant at Non-Permissive Temperature in Escherichia coli

Replicative helicases unwind double-stranded DNA in front of the polymerase and ensure the processivity of DNA synthesis. In Escherichia coli, the helicase loader DnaC as well as factors involved in the formation of the open complex during the initiation of replication and primosomal proteins during the reactivation of arrested replication forks are required to recruit and deposit the replicative helicase onto single-stranded DNA prior to the formation of the replisome. dnaC2 is a thermosensitive allele of the gene specifying the helicase loader; at non-permissive temperature replication cannot initiate, but most ongoing rounds of replication continues through to completion (18% of dnaC2 cells fail to complete replication at non-permissive temperature). An assumption, which may be drawn from this observation, is that only a few replication forks are arrested under normal growth conditions. This assumption, however, is at odds with the severe and deleterious phenotypes associated with a null mutant of priA, the gene encoding a helicase implicated in the reactivation of arrested replication forks. We developed an assay that involves an abrupt inactivation of rounds of synchronized replication in a large population of cells, in order to evaluate the ability of dnaC2 cells to reactivate arrested replication forks at non-permissive temperature. We compared the rate at which arrested replication forks accumulated in dnaC2 priA+ and dnaC2 priA2 cells and observed that this rate was lower in dnaC2 priA+ cells. We conclude that while replication cannot initiate in a dnaC2 mutant at non-permissive temperature, a class of arrested replication forks (PriA-dependent and DnaC-independent) are reactivated within these cells

Disentangling Direct from Indirect Co-Evolution of Residues in Protein Alignments

Predicting protein structure from primary sequence is one of the ultimate challenges in computational biology. Given the large amount of available sequence data, the analysis of co-evolution, i.e., statistical dependency, between columns in multiple alignments of protein domain sequences remains one of the most promising avenues for predicting residues that are contacting in the structure. A key impediment to this approach is that strong statistical dependencies are also observed for many residue pairs that are distal in the structure. Using a comprehensive analysis of protein domains with available three-dimensional structures we show that co-evolving contacts very commonly form chains that percolate through the protein structure, inducing indirect statistical dependencies between many distal pairs of residues. We characterize the distributions of length and spatial distance traveled by these co-evolving contact chains and show that they explain a large fraction of observed statistical dependencies between structurally distal pairs. We adapt a recently developed Bayesian network model into a rigorous procedure for disentangling direct from indirect statistical dependencies, and we demonstrate that this method not only successfully accomplishes this task, but also allows contacts with weak statistical dependency to be detected. To illustrate how additional information can be incorporated into our method, we incorporate a phylogenetic correction, and we develop an informative prior that takes into account that the probability for a pair of residues to contact depends strongly on their primary-sequence distance and the amount of conservation that the corresponding columns in the multiple alignment exhibit. We show that our model including these extensions dramatically improves the accuracy of contact prediction from multiple sequence alignments

Systemic properties of metabolic networks lead to an epistasis-based model for heterosis

The genetic and molecular approaches to heterosis usually do not rely on any model of the genotype–phenotype relationship. From the generalization of Kacser and Burns’ biochemical model for dominance and epistasis to networks with several variable enzymes, we hypothesized that metabolic heterosis could be observed because the response of the flux towards enzyme activities and/or concentrations follows a multi-dimensional hyperbolic-like relationship. To corroborate this, we used the values of systemic parameters accounting for the kinetic behaviour of four enzymes of the upstream part of glycolysis, and simulated genetic variability by varying in silico enzyme concentrations. Then we “crossed” virtual parents to get 1,000 hybrids, and showed that best-parent heterosis was frequently observed. The decomposition of the flux value into genetic effects, with the help of a novel multilocus epistasis index, revealed that antagonistic additive-by-additive epistasis effects play the major role in this framework of the genotype–phenotype relationship. This result is consistent with various observations in quantitative and evolutionary genetics, and provides a model unifying the genetic effects underlying heterosis

Natural selection underlies apparent stress-induced mutagenesis in a bacteriophage infection model

The emergence of mutations following growth-limiting conditions underlies bacterial drug resistance, viral escape from the immune system and fundamental evolution-driven events. Intriguingly, whether mutations are induced by growth limitation conditions or are randomly generated during growth and then selected by growth limitation conditions remains an open question(1). Here, we show that bacteriophage T7 undergoes apparent stress-induced mutagenesis when selected for improved recognition of its host's receptor. In our unique experimental set-up, the growth limitation condition is physically and temporally separated from mutagenesis: growth limitation occurs while phage DNA is outside the host, and spontaneous mutations occur during phage DNA replication inside the host. We show that the selected beneficial mutations are not pre-existing and that the initial slow phage growth is enabled by the phage particle's low-efficiency DNA injection into the host. Thus, the phage particle allows phage populations to initially extend their host range without mutagenesis by virtue of residual recognition of the host receptor. Mutations appear during non-selective intracellular replication, and the frequency of mutant phages increases by natural selection acting on free phages, which are not capable of mutagenesis

Inference of Co-Evolving Site Pairs: an Excellent Predictor of Contact Residue Pairs in Protein 3D structures

Residue-residue interactions that fold a protein into a unique three-dimensional structure and make it play a specific function impose structural and functional constraints on each residue site. Selective constraints on residue sites are recorded in amino acid orders in homologous sequences and also in the evolutionary trace of amino acid substitutions. A challenge is to extract direct dependences between residue sites by removing indirect dependences through other residues within a protein or even through other molecules. Recent attempts of disentangling direct from indirect dependences of amino acid types between residue positions in multiple sequence alignments have revealed that the strength of inferred residue pair couplings is an excellent predictor of residue-residue proximity in folded structures. Here, we report an alternative attempt of inferring co-evolving site pairs from concurrent and compensatory substitutions between sites in each branch of a phylogenetic tree. First, branch lengths of a phylogenetic tree inferred by the neighbor-joining method are optimized as well as other parameters by maximizing a likelihood of the tree in a mechanistic codon substitution model. Mean changes of quantities, which are characteristic of concurrent and compensatory substitutions, accompanied by substitutions at each site in each branch of the tree are estimated with the likelihood of each substitution. Partial correlation coefficients of the characteristic changes along branches between sites are calculated and used to rank co-evolving site pairs. Accuracy of contact prediction based on the present co-evolution score is comparable to that achieved by a maximum entropy model of protein sequences for 15 protein families taken from the Pfam release 26.0. Besides, this excellent accuracy indicates that compensatory substitutions are significant in protein evolution.Comment: 17 pages, 4 figures, and 4 tables with supplementary information of 5 figure

“One-Size-Fits-All”? Optimizing Treatment Duration for Bacterial Infections

Historically, antibiotic treatment guidelines have aimed to maximize treatment efficacy and minimize toxicity, but have not considered the evolution of antibiotic resistance. Optimizing the duration and dosing of treatment to minimize the duration of symptomatic infection and selection pressure for resistance simultaneously has the potential to extend the useful therapeutic life of these valuable life-saving drugs without compromising the interests of individual patients

Compensatory Evolution of pbp Mutations Restores the Fitness Cost Imposed by β-Lactam Resistance in Streptococcus pneumoniae

The prevalence of antibiotic resistance genes in pathogenic bacteria is a major challenge to treating many infectious diseases. The spread of these genes is driven by the strong selection imposed by the use of antibacterial drugs. However, in the absence of drug selection, antibiotic resistance genes impose a fitness cost, which can be ameliorated by compensatory mutations. In Streptococcus pneumoniae, β-lactam resistance is caused by mutations in three penicillin-binding proteins, PBP1a, PBP2x, and PBP2b, all of which are implicated in cell wall synthesis and the cell division cycle. We found that the fitness cost and cell division defects conferred by pbp2b mutations (as determined by fitness competitive assays in vitro and in vivo and fluorescence microscopy) were fully compensated by the acquisition of pbp2x and pbp1a mutations, apparently by means of an increased stability and a consequent mislocalization of these protein mutants. Thus, these compensatory combinations of pbp mutant alleles resulted in an increase in the level and spectrum of β-lactam resistance. This report describes a direct correlation between antibiotic resistance increase and fitness cost compensation, both caused by the same gene mutations acquired by horizontal transfer. The clinical origin of the pbp mutations suggests that this intergenic compensatory process is involved in the persistence of β-lactam resistance among circulating strains. We propose that this compensatory mechanism is relevant for β-lactam resistance evolution in Streptococcus pneumoniae

DnaC Inactivation in Escherichia coli K-12 Induces the SOS Response and Expression of Nucleotide Biosynthesis Genes

Background: Initiation of chromosome replication in E. coli requires the DnaA and DnaC proteins and conditionally-lethal dnaA and dnaC mutants are often used to synchronize cell populations. Methodology/Principal Findings: DNA microarrays were used to measure mRNA steady-state levels in initiation-deficient dnaA46 and dnaC2 bacteria at permissive and non-permissive temperatures and their expression profiles were compared to MG1655 wildtype cells. For both mutants there was altered expression of genes involved in nucleotide biosynthesis at the non-permissive temperature. Transcription of the dnaA and dnaC genes was increased at the non-permissive temperature in the respective mutant strains indicating auto-regulation of both genes. Induction of the SOS regulon was observed in dnaC2 cells at 38uC and 42uC. Flow cytometric analysis revealed that dnaC2 mutant cells at non-permissive temperature had completed the early stages of chromosome replication initiation. Conclusion/Significance: We suggest that in dnaC2 cells the SOS response is triggered by persistent open-complex formation at oriC and/or by arrested forks that require DnaC for replication restart