Interplay between pleiotropy and secondary selection determines rise and fall of mutators in stress response

Mutation rate variability as a driving force in adaptive evolution

Mutation rate is a key determinant of the pace as well as outcome of evolution, and variability in this rate has been shown in different scenarios to play a key role in evolutionary adaptation and resistance evolution under stress caused by selective pressure. Here we investigate the dynamics of resistance fixation in a bacterial population with variable mutation rates, and we show that evolutionary outcomes are most sensitive to mutation rate variations when the population is subject to environmental and demographic conditions that suppress the evolutionary advantage of high-fitness subpopulations. By directly mapping a biophysical fitness function to the system-level dynamics of the population, we show that both low and very high, but not intermediate, levels of stress in the form of an antibiotic result in a disproportionate effect of hypermutation on resistance fixation. We demonstrate how this behavior is directly tied to the extent of genetic hitchhiking in the system, the propagation of high-mutation rate cells through association with high-fitness mutations. Our results indicate a substantial role for mutation rate flexibility in the evolution of antibiotic resistance under conditions that present a weak advantage over wildtype to resistant cells.

Evolutionary dynamics of viral escape under antibodies stress: A biophysical model

Viruses constantly face the selection pressure of antibodies, either from innate immune response of the host or from administered antibodies for treatment. We explore the interplay between the biophysical properties of viral proteins and the population and demographic variables in the viral escape. The demographic and population genetics aspect of the viral escape have been explored before; however one important assumption was the a priori distribution of fitness effects (DFE). Here, we relax this assumption by instead considering a realistic biophysics-based genotype-phenotype relationship for RNA viruses escaping antibodies stress. In this model the DFE is itself an evolvable property that depends on the genetic background (epistasis) and the distribution of biophysical effects of mutations, which is informed by biochemical experiments and theoretical calculations in protein engineering. We quantitatively explore in silico the viability of viral populations under antibodies pressure and derive the phase diagram that defines the fate of the virus population (extinction or escape from stress) in a range of viral mutation rates and antibodies concentrations. We find that viruses are most resistant to stress at an optimal mutation rate (OMR) determined by the competition between supply of beneficial mutation to facilitate escape from stressors and lethal mutagenesis caused by excess of destabilizing mutations. We then show the quantitative dependence of the OMR on genome length and viral burst size. We also recapitulate the experimental observation that viruses with longer genomes have smaller mutation rate per nucleotide.

Biophysics of protein evolution and evolutionary protein biophysics

The study of molecular evolution at the level of protein-coding genes often entails comparing large datasets of sequences to infer their evolutionary relationships. Despite the importance of a protein's structure and conformational dynamics to its function and thus its fitness, common phylogenetic methods embody minimal biophysical knowledge of proteins. To underscore the biophysical constraints on natural selection, we survey effects of protein mutations, highlighting the physical basis for marginal stability of natural globular proteins and how requirement for kinetic stability and avoidance of misfolding and misinteractions might have affected protein evolution. The biophysical underpinnings of these effects have been addressed by models with an explicit coarse-grained spatial representation of the polypeptide chain. Sequence-structure mappings based on such models are powerful conceptual tools that rationalize mutational robustness, evolvability, epistasis, promiscuous function performed by 'hidden' conformational states, resolution of adaptive conflicts and conformational switches in the evolution from one protein fold to another. Recently, protein biophysics has been applied to derive more accurate evolutionary accounts of sequence data. Methods have also been developed to exploit sequence-based evolutionary information to predict biophysical behaviours of proteins. The success of these approaches demonstrates a deep synergy between the fields of protein biophysics and protein evolution.

Is catalytic activity of chaperones a selectable trait for the emergence of heat shock response?

Although heat shock response is ubiquitous in bacterial cells, the underlying physical chemistry behind heat shock response remains poorly understood. To study the response of cell populations to heat shock we employ a physics-based ab initio model of living cells where protein biophysics (i.e., folding and protein-protein interactions in crowded cellular environments) and important aspects of proteins homeostasis are coupled with realistic population dynamics simulations. By postulating a genotype-phenotype relationship we define a cell division rate in terms of functional concentrations of proteins and protein complexes, whose Boltzmann stabilities of folding and strengths of their functional interactions are exactly evaluated from their sequence information. We compare and contrast evolutionary dynamics for two models of chaperon action. In the active model, foldase chaperones function as nonequilibrium machines to accelerate the rate of protein folding. In the passive model, holdase chaperones form reversible complexes with proteins in their misfolded conformations to maintain their solubility. We find that only cells expressing foldase chaperones are capable of genuine heat shock response to the increase in the amount of unfolded proteins at elevated temperatures. In response to heat shock, cells' limited resources are redistributed differently for active and passive models. For the active model, foldase chaperones are overexpressed at the expense of downregulation of high abundance proteins, whereas for the passive model; cells react to heat shock by downregulating their high abundance proteins, as their low abundance proteins are upregulated.

Chemotherapy induced microsatellite instability and loss of heterozygosity in chromosomes 2, 5, 10, and 17 in solid tumor patients

Background

The inevitable side effects of the currently used chemotherapy are associated with serious syndromes. Genotoxic effects and consequent genetic instability may play an important role in these syndromes. The aim of the study was to evaluate chemotherapy-related microsatellite instability (MSI), loss of heterozygosity (LOH), and loss of mismatch repair (MMR) expression in solid tumor patients.

Methods

Samples were collected from 117 de novo patients with solid tumors of different origins. Specimens, taken pre- and post-treatment, were screened for MSI and LOH in 10 microsatellite sequences in blood, and expression of five MMR proteins were analyzed in cancer tissues using immunohistochemistry. Statistical analysis included the use of; Fisher’s exact test, Chi Square, and an inter-rater reliability test using Cohen’s kappa coefficient.

Results

Microsatellite analysis showed that 66.7% of the patients had MSI, including 23.1% high-positive MSI and 43.6% low-positive MSI. A large portion (41%) of the patients exhibited LOH in addition to MSI. MSI and LOH were detected in seven loci in which incidence rates ranged from 3.8% positive for Bat-26 to 34.6% positive for Tp53-Alu. Immunohistochemistry revealed that human mutL homolog 1 (hMLH1) expression was deficient in 29.1% of the patients, whereas 18.8%, 23.9%, 13.4%, and 9.7% were deficient for human mutS homolog 2 (hMSH2), P53, human mutS homolog 6 (hMSH6) and human post-meiotic segregation increased 2 (hPMS2), respectively. There was a significant correlation between MSI and LOH incidence in Tp53-Alu, Mfd41, and APC with low or deficient expression of hMLH1, hMSH2, and P53. A significant association between MSI and LOH, and incidence of secondary tumors was also evident.

Conclusions

The negative correlation between MMR expression, MSI, and LOH and increased resistance to anti-cancer drugs and development of secondary cancers demonstrates a useful aid in early detection of potential chemotherapy-related side-effects. The diagnostic value demonstrated in our earlier study on breast cancer patients was confirmed for other solid tumors.

Electronic supplementary material

The online version of this article (doi:10.1186/s12935-014-0118-4) contains supplementary material, which is available to authorized users.

The advantage of arriving first: characteristic times in finite size populations of error-prone replicators

We study the evolution of a finite size population formed by mutationally isolated lineages of error-prone replicators in a two-peak fitness landscape. Computer simulations are performed to gain a stochastic description of the system dynamics. More specifically, for different population sizes, we compute the probability of each lineage being selected in terms of their mutation rates and the amplification factors of the fittest phenotypes. We interpret the results as the compromise between the characteristic time a lineage takes to reach its fittest phenotype by crossing the neutral valley and the selective value of the sequences that form the lineages. A main conclusion is drawn: for finite population sizes, the survival probability of the lineage that arrives first to the fittest phenotype rises significantly.

Stress-induced hypermutation as a physical property of life, a force of natural selection and its role in four thought experiments

The independence of genetic mutation rate from selection is central to neo-Darwinian evolutionary theory. However, it has been continuously challenged for more than 30 years by experimental evidence of genetic mutation rate transiently increasing in response to stress (stress-induced hypermutation, SIH). The prominent concept of evolved evolvability (EE) explains that natural selection for strategies more competitive at evolutionary adaptation itself gives rise to mechanisms dynamically adjusting mutation rates to environmental stress. Here, we theoretically investigate the alternative (not mutually exclusive) hypothesis that SIH is an inherent physical property of all genetically reproducing life. We define stress as any condition lowering the capability of utilizing metabolic resources for genome storage and replication. This thermodynamical analysis indicates stress-induced increases in the genetic mutation rate in genome storage and in genome replication as inherent physical properties of genetically reproducing life. Further integrating SIH into an overall organismic thermodynamic budget identifies SIH as a force of natural selection, alongside death rate, replication rate and constitutive mutation rate differences. We execute four thought experiments with a non-recombinant lesion mutant strain to predict experimental observations due to SIH in response to different stresses and stress combinations. We find (1) acceleration of adaptation over models without SIH, (2) possibility of adaptation at high stresses which are not explicable by mutation in genome replication alone and (3) different adaptive potential under high growth-inhibiting versus high lethal stresses. The predictions are directly comparable to culture experiments (colony size time courses, antibacterial resistance assay and occurrence of lesion-reversion mutant colonies) and genome sequence analysis. Considering suggestions of drug-mediated disruption of SIH and attempts to target mutation-associated sites with chemotherapeutic agents to prevent resistance, our findings seem to be relevant knowledge for resistance-averse drug development and administration.

Bringing molecules back into molecular evolution

Much molecular-evolution research is concerned with sequence analysis. Yet these sequences represent real, three-dimensional molecules with complex structure and function. Here I highlight a growing trend in the field to incorporate molecular structure and function into computational molecular-evolution work. I consider three focus areas: reconstruction and analysis of past evolutionary events, such as phylogenetic inference or methods to infer selection pressures; development of toy models and simulations to identify fundamental principles of molecular evolution; and atom-level, highly realistic computational modeling of molecular structure and function aimed at making predictions about possible future evolutionary events.

The evolution of stress-induced hypermutation in asexual populations

Numerous empirical studies show that stress of various kinds induces a state of hypermutation in bacteria via multiple mechanisms, but theoretical treatment of this intriguing phenomenon is lacking. We used deterministic and stochastic models to study the evolution of stress-induced hypermutation in infinite and finite-size populations of bacteria undergoing selection, mutation, and random genetic drift in constant environments and in changing ones. Our results suggest that if beneficial mutations occur, even rarely, then stress-induced hypermutation is advantageous for bacteria at both the individual and the population levels and that it is likely to evolve in populations of bacteria in a wide range of conditions because it is favored by selection. These results imply that mutations are not, as the current view holds, uniformly distributed in populations, but rather that mutations are more common in stressed individuals and populations. Because mutation is the raw material of evolution, these results have a profound impact on broad aspects of evolution and biology.

Genotypic and phenotypic variation in Pseudomonas aeruginosa reveals signatures of secondary infection and mutator activity in certain cystic fibrosis patients with chronic lung infections

Evolutionary adaptation of Pseudomonas aeruginosa to the cystic fibrosis lung is limited by genetic variation, which depends on rates of horizontal gene transfer and mutation supply. Because each may increase following secondary infection or mutator emergence, we sought to ascertain the incidence of secondary infection and genetic variability in populations containing or lacking mutators. Forty-nine strains collected over 3 years from 16 patients were phenotyped for antibiotic resistance and mutator status and were genotyped by repetitive-sequence PCR (rep-PCR), pulsed-field gel electrophoresis (PFGE), and multilocus sequence typing (MLST). Though phenotypic and genetic polymorphisms were widespread and clustered more strongly within than between longitudinal series, their distribution revealed instances of secondary infection. Sequence data, however, indicated that interlineage recombination predated initial strain isolation. Mutator series were more likely to be multiply antibiotic resistant, but not necessarily more variable in their nucleotide sequences, than nonmutators. One mutator and one nonmutator series were sequenced at mismatch repair loci and analyzed for gene content using DNA microarrays. Both were wild type with respect to mutL, but mutators carried an 8-bp mutS deletion causing a frameshift mutation. Both series lacked 126 genes encoding pilins, siderophores, and virulence factors whose inactivation has been linked to adaptation during chronic infection. Mutators exhibited loss of severalfold more genes having functions related to mobile elements, motility, and attachment. A 105-kb, 86-gene deletion was observed in one nonmutator that resulted in loss of virulence factors related to pyoverdine synthesis and elements of the multidrug efflux regulon. Diminished DNA repair activity may facilitate but not be absolutely required for rapid evolutionary change.

Low pesticide rates may hasten the evolution of resistance by increasing mutation frequencies

At very low pesticide rates, a certain low proportion of pests may receive a sublethal dose, are highly stressed by the pesticide and yet survive. Stress is a general enhancer of mutation rates. Thus, the survivors are likely to have more than normal mutations, which might include mutations leading to pesticide resistance, both for multifactorial (polygenic, gene amplification, sequential allelic mutations) and for major gene resistance. Management strategies should consider how to eliminate the subpopulation of pests with the high mutation rates, but the best strategy is probably to avoid too low application rates of pesticides from the outset.

Topology of protein interaction network shapes protein abundances and strengths of their functional and nonspecific interactions

How do living cells achieve sufficient abundances of functional protein complexes while minimizing promiscuous nonfunctional interactions? Here we study this problem using a first-principle model of the cell whose phenotypic traits are directly determined from its genome through biophysical properties of protein structures and binding interactions in a crowded cellular environment. The model cell includes three independent prototypical pathways, whose topologies of protein-protein interaction (PPI) subnetworks are different, but whose contributions to the cell fitness are equal. Model cells evolve through genotypic mutations and phenotypic protein copy number variations. We found a strong relationship between evolved physical-chemical properties of protein interactions and their abundances due to a "frustration" effect: Strengthening of functional interactions brings about hydrophobic interfaces, which make proteins prone to promiscuous binding. The balancing act is achieved by lowering concentrations of hub proteins while raising solubilities and abundances of functional monomers. On the basis of these principles we generated and analyzed a possible realization of the proteome-wide PPI network in yeast. In this simulation we found that high-throughput affinity capture-mass spectroscopy experiments can detect functional interactions with high fidelity only for high-abundance proteins while missing most interactions for low-abundance proteins.