Collective peroxide detoxification determines microbial mutation rate plasticity in E. coli.

Mutagenesis is responsive to many environmental factors. Evolution therefore depends on the environment not only for selection but also in determining the variation available in a population. One such environmental dependency is the inverse relationship between mutation rates and population density in many microbial species. Here, we determine the mechanism responsible for this mutation rate plasticity. Using dynamical computational modelling and in culture mutation rate estimation, we show that the negative relationship between mutation rate and population density arises from the collective ability of microbial populations to control concentrations of hydrogen peroxide. We demonstrate a loss of this density-associated mutation rate plasticity (DAMP) when Escherichia coli populations are deficient in the degradation of hydrogen peroxide. We further show that the reduction in mutation rate in denser populations is restored in peroxide degradation-deficient cells by the presence of wild-type cells in a mixed population. Together, these model-guided experiments provide a mechanistic explanation for DAMP, applicable across all domains of life, and frames mutation rate as a dynamic trait shaped by microbial community composition.

Mutators can drive the evolution of multi-resistance to antibiotics.

Antibiotic combination therapies are an approach used to counter the evolution of resistance; their purported benefit is they can stop the successive emergence of independent resistance mutations in the same genome. Here, we show that bacterial populations with 'mutators', organisms with defects in DNA repair, readily evolve resistance to combination antibiotic treatment when there is a delay in reaching inhibitory concentrations of antibiotic-under conditions where purely wild-type populations cannot. In populations of Escherichia coli subjected to combination treatment, we detected a diverse array of acquired mutations, including multiple alleles in the canonical targets of resistance for the two drugs, as well as mutations in multi-drug efflux pumps and genes involved in DNA replication and repair. Unexpectedly, mutators not only allowed multi-resistance to evolve under combination treatment where it was favoured, but also under single-drug treatments. Using simulations, we show that the increase in mutation rate of the two canonical resistance targets is sufficient to permit multi-resistance evolution in both single-drug and combination treatments. Under both conditions, the mutator allele swept to fixation through hitch-hiking with single-drug resistance, enabling subsequent resistance mutations to emerge. Ultimately, our results suggest that mutators may hinder the utility of combination therapy when mutators are present. Additionally, by raising the rates of genetic mutation, selection for multi-resistance may have the unwanted side-effect of increasing the potential to evolve resistance to future antibiotic treatments.

Horizontal gene transfer and ecological interactions jointly control microbiome stability.

Genes encoding resistance to stressors, such as antibiotics or environmental pollutants, are widespread across microbiomes, often encoded on mobile genetic elements. Yet, despite their prevalence, the impact of resistance genes and their mobility upon the dynamics of microbial communities remains largely unknown. Here we develop eco-evolutionary theory to explore how resistance genes alter the stability of diverse microbiomes in response to stressors. We show that adding resistance genes to a microbiome typically increases its overall stability, particularly for genes on mobile genetic elements with high transfer rates that efficiently spread resistance throughout the community. However, the impact of resistance genes upon the stability of individual taxa varies dramatically depending upon the identity of individual taxa, the mobility of the resistance gene, and the network of ecological interactions within the community. Nonmobile resistance genes can benefit susceptible taxa in cooperative communities yet damage those in competitive communities. Moreover, while the transfer of mobile resistance genes generally increases the stability of previously susceptible recipient taxa to perturbation, it can decrease the stability of the originally resistant donor taxon. We confirmed key theoretical predictions experimentally using competitive soil microcosm communities. Here the stability of a susceptible microbial community to perturbation was increased by adding mobile resistance genes encoded on conjugative plasmids but was decreased when these same genes were encoded on the chromosome. Together, these findings highlight the importance of the interplay between ecological interactions and horizontal gene transfer in driving the eco-evolutionary dynamics of diverse microbiomes.

Environmental and genetic influence on the rate and spectrum of spontaneous mutations in Escherichia coli.

Spontaneous mutations are the ultimate source of novel genetic variation on which evolution operates. Although mutation rate is often discussed as a single parameter in evolution, it comprises multiple distinct types of changes at the level of DNA. Moreover, the rates of these distinct changes can be independently influenced by genomic background and environmental conditions. Using fluctuation tests, we characterized the spectrum of spontaneous mutations in Escherichia coli grown in low and high glucose environments. These conditions are known to affect the rate of spontaneous mutation in wild-type MG1655, but not in a ΔluxS deletant strain - a gene with roles in both quorum sensing and the recycling of methylation products used in E. coli's DNA repair process. We find an increase in AT>GC transitions in the low glucose environment, suggesting that processes relating to the production or repair of this mutation could drive the response of overall mutation rate to glucose concentration. Interestingly, this increase in AT>GC transitions is maintained by the glucose non-responsive ΔluxS deletant. Instead, an elevated rate of GC>TA transversions, more common in a high glucose environment, leads to a net non-responsiveness of overall mutation rate for this strain. Our results show how relatively subtle changes, such as the concentration of a carbon substrate or loss of a regulatory gene, can substantially influence the amount and nature of genetic variation available to selection.

Distinct microbiome profiles in convergent wisdom tooth impactions.

AIMS: Long-term retention of impacted third molars (wisdom teeth) is associated with plaque stagnation and the development of caries on the adjacent surface of the neighboring second molar. While caries and tooth loss are common outcomes of impaction, there is currently insufficient evidence to support the pre-emptive removal of asymptomatic wisdom teeth. Emerging evidence suggests that convergently growing impactions are associated with caries. We have therefore investigated the composition of dental plaque on the distal surface of the mandibular second molar at various impaction angles. METHODS AND RESULTS: We have compared the microbiome of these surfaces at four impaction angulations using short-read sequencing of the bacterial 16S rRNA gene: two convergent (horizontal and mesial) and two divergent (distal and vertical) angulations, and in cases where the wisdom tooth is missing. Horizontal angulations exhibited lower microbial diversity than mesial impactions. Amplicon Sequence Variants (ASVs) associated with Veillonella were significantly more abundant at impactions with angulations toward the midline. Using machine learning, a random forest classifier trained to distinguish microbiome profiles was used to predict the native angulations for a subset of samples, with samples from the two convergent impactions estimated with the greatest accuracy. CONCLUSIONS: Differences in microbial diversity were apparent between caries-associated convergent (horizontal and mesial) impacted wisdom teeth, as well as greater abundances of Veillonella ASVs at horizontal impactions.

Morphological Phylogenetics Evaluated Using Novel Evolutionary Simulations.

Evolutionary inferences require reliable phylogenies. Morphological data have traditionally been analyzed using maximum parsimony, but recent simulation studies have suggested that Bayesian analyses yield more accurate trees. This debate is ongoing, in part, because of ambiguity over modes of morphological evolution and a lack of appropriate models. Here, we investigate phylogenetic methods using two novel simulation models-one in which morphological characters evolve stochastically along lineages and another in which individuals undergo selection. Both models generate character data and lineage splitting simultaneously: the resulting trees are an emergent property, rather than a fixed parameter. Standard consensus methods for Bayesian searches (Mki) yield fewer incorrect nodes and quartets than the standard consensus trees recovered using equal weighting and implied weighting parsimony searches. Distances between the pool of derived trees (most parsimonious or posterior distribution) and the true trees-measured using Robinson-Foulds (RF), subtree prune and regraft (SPR), and tree bisection reconnection (TBR) metrics-demonstrate that this is related to the search strategy and consensus method of each technique. The amount and structure of homoplasy in character data differ between models. Morphological coherence, which has previously not been considered in this context, proves to be a more important factor for phylogenetic accuracy than homoplasy. Selection-based models exhibit relatively lower homoplasy, lower morphological coherence, and higher inaccuracy in inferred trees. Selection is a dominant driver of morphological evolution, but we demonstrate that it has a confounding effect on numerous character properties which are fundamental to phylogenetic inference. We suggest that the current debate should move beyond considerations of parsimony versus Bayesian, toward identifying modes of morphological evolution and using these to build models for probabilistic search methods. [Bayesian; evolution; morphology; parsimony; phylogenetics; selection; simulation.].

Spontaneous mutation rate is a plastic trait associated with population density across domains of life.

Rates of random, spontaneous mutation can vary plastically, dependent upon the environment. Such plasticity affects evolutionary trajectories and may be adaptive. We recently identified an inverse plastic association between mutation rate and population density at 1 locus in 1 species of bacterium. It is unknown how widespread this association is, whether it varies among organisms, and what molecular mechanisms of mutagenesis or repair are required for this mutation-rate plasticity. Here, we address all 3 questions. We identify a strong negative association between mutation rate and population density across 70 years of published literature, comprising hundreds of mutation rates estimated using phenotypic markers of mutation (fluctuation tests) from all domains of life and viruses. We test this relationship experimentally, determining that there is indeed density-associated mutation-rate plasticity (DAMP) at multiple loci in both eukaryotes and bacteria, with up to 23-fold lower mutation rates at higher population densities. We find that the degree of plasticity varies, even among closely related organisms. Nonetheless, in each domain tested, DAMP requires proteins scavenging the mutagenic oxidised nucleotide 8-oxo-dGTP. This implies that phenotypic markers give a more precise view of mutation rate than previously believed: having accounted for other known factors affecting mutation rate, controlling for population density can reduce variation in mutation-rate estimates by 93%. Widespread DAMP, which we manipulate genetically in disparate organisms, also provides a novel trait to use in the fight against the evolution of antimicrobial resistance. Such a prevalent environmental association and conserved mechanism suggest that mutation has varied plastically with population density since the early origins of life.

Gut eosinophils and their impact on the mucus-resident microbiota.

The gut has the largest commensal bacterial population in the body and its composition can be impacted by host factors such as production of immunoglobulin A (IgA). Eosinophils in the gut have been implicated in the production of antibacterial factors and maintenance of IgA-secreting plasma cells. We used an eosinophil-deficient mouse (∆dblGATA-1-/- ) and littermate controls to investigate the role of eosinophils in the regulation of the microbiota, with particular emphasis on mucus-resident species in the small and large intestine. We found no differences in IgA production or IgA-expressing plasma cells between naive littermates in the small or large intestine. However, denaturing gel gradient electrophoresis revealed differences in the bacterial communities of the mucus and stools between wild-type mice and ∆dblGATA-1-/- mice, with the greatest separation between the mucus microbial communities. Mucus-resident bacteria in ∆dblGATA-1-/- mice had reduced diversity in the mucus compared with the stools. A quantitative PCR panel of selected bacteria showed that the most significant differences in the microbiota were between mucus-resident bacteria and those in stool, such as the abundance of Clostridiales and Bacteroides. Our data implicate eosinophils in the regulation of the microbiota, especially the bacteria most hyperlocal to the gut barrier. Although we see differences between host genotypes in the overall microbial communities, further work is required to establish specifically which bacteria are different between these groups. Most importantly, the data revealed that the mucus and stool microbiota are discrete communities. Stool analysis alone may be insufficient to comprehensively explore and define the role of the gut microbiota in health and disease.

Environmental pleiotropy and demographic history direct adaptation under antibiotic selection.

Evolutionary rescue following environmental change requires mutations permitting population growth in the new environment. If change is severe enough to prevent most of the population reproducing, rescue becomes reliant on mutations already present. If change is sustained, the fitness effects in both environments, and how they are associated-termed 'environmental pleiotropy'-may determine which alleles are ultimately favoured. A population's demographic history-its size over time-influences the variation present. Although demographic history is known to affect the probability of evolutionary rescue, how it interacts with environmental pleiotropy during severe and sustained environmental change remains unexplored. Here, we demonstrate how these factors interact during antibiotic resistance evolution, a key example of evolutionary rescue fuelled by pre-existing mutations with pleiotropic fitness effects. We combine published data with novel simulations to characterise environmental pleiotropy and its effects on resistance evolution under different demographic histories. Comparisons among resistance alleles typically revealed no correlation for fitness-i.e., neutral pleiotropy-above and below the sensitive strain's minimum inhibitory concentration. Resistance allele frequency following experimental evolution showed opposing correlations with their fitness effects in the presence and absence of antibiotic. Simulations demonstrated that effects of environmental pleiotropy on allele frequencies depended on demographic history. At the population level, the major influence of environmental pleiotropy was on mean fitness, rather than the probability of evolutionary rescue or diversity. Our work suggests that determining both environmental pleiotropy and demographic history is critical for predicting resistance evolution, and we discuss the practicalities of this during in vivo evolution.

Variable Effects of Exposure to Formulated Microbicides on Antibiotic Susceptibility in Firmicutes and Proteobacteria.

UNLABELLED: Microbicides are broad-spectrum antimicrobial agents that generally interact with multiple pharmacological targets. While they are widely deployed in disinfectant, antiseptic, and preservative formulations, data relating to their potential to select for microbicide or antibiotic resistance have been generated mainly by testing the compounds in much simpler aqueous solutions. In the current investigation, antibiotic susceptibility was determined for bacteria that had previously exhibited decreased microbicide susceptibility following repeated exposure to microbicides either in formulation with sequestrants and surfactants or in simple aqueous solution. Statistically significant increases in antibiotic susceptibility occurred for 12% of bacteria after exposure to microbicides in formulation and 20% of bacteria after exposure to microbicides in aqueous solutions, while 22% became significantly less susceptible to the antibiotics, regardless of formulation. Of the combinations of a bacterium and an antibiotic for which British Society for Antimicrobial Chemotherapy breakpoints are available, none became resistant. Linear modeling taking into account phylogeny, microbicide, antibiotic, and formulation identified small but significant effects of formulation that varied depending on the bacterium and microbicide. Adaptation to formulated benzalkonium chloride in particular was more likely to increase antibiotic susceptibility than adaptation to the simple aqueous solution. In conclusion, bacterial adaptation through repeated microbicide exposure was associated with both increases and decreases in antibiotic susceptibility. Formulation of the microbicide to which the bacteria had previously adapted had an identifiable effect on antibiotic susceptibility, but it effect was typically small relative to the differences observed among microbicides. Susceptibility changes resulting in resistance were not observed. IMPORTANCE: The safety of certain microbicide applications has been questioned due to the possibility that microbicide exposure could select for microbicide and antibiotic resistance. Evidence that this may happen is based mainly on in vitro experiments where bacteria have been exposed to microbicides in aqueous solution. Microbicides are, however, normally deployed in products formulated with surfactants, sequestrants, and other compounds. While this may influence the frequency and extent of susceptibility changes, few studies reported in the literature have assessed this. In the current investigation, therefore, we have investigated changes in antibiotic susceptibility in bacteria which exhibited decreased microbicide susceptibility following repeated exposure to microbicides in simple aqueous solutions and in formulation. We report that the microbicide formulation had an identifiable effect on antibiotic susceptibility, but it was typically small relative to the differences observed among microbicides. We did not observe susceptibility changes resulting in resistance.

Monotonicity of fitness landscapes and mutation rate control.

A common view in evolutionary biology is that mutation rates are minimised. However, studies in combinatorial optimisation and search have shown a clear advantage of using variable mutation rates as a control parameter to optimise the performance of evolutionary algorithms. Much biological theory in this area is based on Ronald Fisher's work, who used Euclidean geometry to study the relation between mutation size and expected fitness of the offspring in infinite phenotypic spaces. Here we reconsider this theory based on the alternative geometry of discrete and finite spaces of DNA sequences. First, we consider the geometric case of fitness being isomorphic to distance from an optimum, and show how problems of optimal mutation rate control can be solved exactly or approximately depending on additional constraints of the problem. Then we consider the general case of fitness communicating only partial information about the distance. We define weak monotonicity of fitness landscapes and prove that this property holds in all landscapes that are continuous and open at the optimum. This theoretical result motivates our hypothesis that optimal mutation rate functions in such landscapes will increase when fitness decreases in some neighbourhood of an optimum, resembling the control functions derived in the geometric case. We test this hypothesis experimentally by analysing approximately optimal mutation rate control functions in 115 complete landscapes of binding scores between DNA sequences and transcription factors. Our findings support the hypothesis and find that the increase of mutation rate is more rapid in landscapes that are less monotonic (more rugged). We discuss the relevance of these findings to living organisms.

Unraveling adaptive evolution: how a single point mutation affects the protein coregulation network.

Understanding the mechanisms of evolution requires identification of the molecular basis of the multiple (pleiotropic) effects of specific adaptive mutations. We have characterized the pleiotropic effects on protein levels of an adaptive single-base pair substitution in the coding sequence of a signaling pathway gene in the bacterium Pseudomonas fluorescens SBW25. We find 52 proteomic changes, corresponding to 46 identified proteins. None of these proteins is required for the adaptive phenotype. Instead, many are found within specific metabolic pathways associated with fitness-reducing (that is, antagonistic) effects of the mutation. The affected proteins fall within a single coregulatory network. The mutation 'rewires' this network by drawing particular proteins into tighter coregulating relationships. Although these changes are specific to the mutation studied, the quantitatively altered proteins are also affected in a coordinated way in other examples of evolution to the same niche.

Adaptation at the Extremes of Life: Experimental Evolution with the Extremophile Archaeon Sulfolobus acidocaldarius.

The archaeon Sulfolobus acidocaldarius has emerged as a promising thermophilic model system. Investigating how thermophiles adapt to changing temperatures is a key requirement, not only for understanding fundamental evolutionary processes but also for developing S. acidocaldarius as a chassis for bioengineering. One major obstacle to conducting experimental evolution with thermophiles is the expense of equipment maintenance and energy usage of traditional incubators for high-temperature growth. To address this challenge, a comprehensive experimental protocol for conducting experimental evolution in S. acidocaldarius is presented, utilizing low-cost and energy-efficient bench-top thermomixers. The protocol involves a batch culture technique with relatively small volumes (1.5 mL), enabling tracking of adaptation in multiple independent lineages. This method is easily scalable through the use of additional thermomixers. Such an approach increases the accessibility of S. acidocaldarius as a model system by reducing both initial investment and ongoing costs associated with experimental investigations. Moreover, the technique is transferable to other microbial systems for exploring adaptation to diverse environmental conditions.

Novel Colorimetric and Light Scatter Methods to Identify and Manage Peritoneal Dialysis-Associated Peritonitis at the Point-of-Care.

Introduction: Peritoneal dialysis (PD)-related peritonitis (PDRP) is a common cause of transfer to hemodialysis, patient morbidity, and is a risk factor for mortality. Associated patient anxiety can deter selection of PD for renal replacement therapy. Diagnosis relies on hospital laboratory tests; however, this might be achieved earlier if such information was available at the point-of-care (POC), thereby significantly improving outcomes. The presence of culturable microbes and the concentration of leukocytes in effluent both aid peritonitis diagnosis, as specified in the International Society for Peritoneal Dialysis (ISPD) diagnostic guidelines. Here, we report the development of 2 new methods providing such information in simple POC tests. Methods: One approach uses a tetrazolium-based chemical reporting system, primarily focused on detecting bacterial contamination and associated vancomycin-sensitivity. The second approach uses a novel forward light-scatter device (QuickCheck) to provide an instant quantitative cell count directly from PD patient effluent. Results: The tetrazolium approach detected and correctly distinguished laboratory isolates, taking 10 hours to provide non-quantitative results. We compared the technical performance of the light scatter leukocyte counting approach with spectrophotometry, hemocytometer counting and flow cytometry (Sysmex) using patient effluent samples. QuickCheck had high accuracy (94%) and was the most precise (coefficient of variation <4%), showing minimal bias, overall performing similarly to flow cytometry. Conclusion: These complementary new approaches provide a simple means to obtain information to assist diagnosis at the POC. The first provides antibiotic sensitivity following 10 hours incubation, whereas the second optical approach (QuickCheck), provides instant accurate total leukocyte count.

Absolute quantification of the glycolytic pathway in yeast: deployment of a complete QconCAT approach.

The availability of label-free data derived from yeast cells (based on the summed intensity of the three strongest, isoform-specific peptides) permitted a preliminary assessment of protein abundances for glycolytic proteins. Following this analysis, we demonstrate successful application of the QconCAT technology, which uses recombinant DNA techniques to generate artificial concatamers of large numbers of internal standard peptides, to the quantification of enzymes of the glycolysis pathway in the yeast Saccharomyces cerevisiae. A QconCAT of 88 kDa (59 tryptic peptides) corresponding to 27 isoenzymes was designed and built to encode two or three analyte peptides per protein, and after stable isotope labeling of the standard in vivo, protein levels were determined by LC-MS, using ultra high performance liquid chromatography-coupled mass spectrometry. We were able to determine absolute protein concentrations between 14,000 and 10 million molecules/cell. Issues such as efficiency of extraction and completeness of proteolysis are addressed, as well as generic factors such as optimal quantotypic peptide selection and expression. In addition, the same proteins were quantified by intensity-based label-free analysis, and both sets of data were compared with other quantification methods.

Making the right connections: biological networks in the light of evolution.

Our understanding of how evolution acts on biological networks remains patchy, as is our knowledge of how that action is best identified, modelled and understood. Starting with network structure and the evolution of protein-protein interaction networks, we briefly survey the ways in which network evolution is being addressed in the fields of systems biology, development and ecology. The approaches highlighted demonstrate a movement away from a focus on network topology towards a more integrated view, placing biological properties centre-stage. We argue that there remains great potential in a closer synergy between evolutionary biology and biological network analysis, although that may require the development of novel approaches and even different analogies for biological networks themselves.

Array-based evolution of DNA aptamers allows modelling of an explicit sequence-fitness landscape.

Mapping the landscape of possible macromolecular polymer sequences to their fitness in performing biological functions is a challenge across the biosciences. A paradigm is the case of aptamers, nucleic acids that can be selected to bind particular target molecules. We have characterized the sequence-fitness landscape for aptamers binding allophycocyanin (APC) protein via a novel Closed Loop Aptameric Directed Evolution (CLADE) approach. In contrast to the conventional SELEX methodology, selection and mutation of aptamer sequences was carried out in silico, with explicit fitness assays for 44,131 aptamers of known sequence using DNA microarrays in vitro. We capture the landscape using a predictive machine learning model linking sequence features and function and validate this model using 5500 entirely separate test sequences, which give a very high observed versus predicted correlation of 0.87. This approach reveals a complex sequence-fitness mapping, and hypotheses for the physical basis of aptameric binding; it also enables rapid design of novel aptamers with desired binding properties. We demonstrate an extension to the approach by incorporating prior knowledge into CLADE, resulting in some of the tightest binding sequences.

Cage and maternal effects on the bacterial communities of the murine gut.

Findings from gut microbiome studies are strongly influenced by both experimental and analytical factors that can unintentionally bias their interpretation. Environment is also critical. Both co-housing and maternal effects are expected to affect microbiomes and have the potential to confound other manipulated factors, such as genetics. We therefore analysed microbiome data from a mouse experiment using littermate controls and tested differences among genotypes (wildtype versus colitis prone-mdr1a-/-), gut niches (stool versus mucus), host ages (6 versus 18 weeks), social groups (co-housed siblings of different genotypes) and maternal influence. We constructed a 16S phylogenetic tree from bacterial communities, fitting random forest models using all 428,234 clades identified. Models discriminated all criteria except host genotype, where no community differences were found. Host social groups differed in abundant, low-level, taxa whereas intermediate phylogenetic and abundance scales distinguished ages and niches. Thus, a carefully controlled experiment treating evolutionary clades of microbes equivalently without reference to taxonomy, clearly identifies whether and how gut microbial communities are distinct across ecologically important factors (niche and host age) and other experimental factors, notably cage effects and maternal influence. These findings highlight the importance of considering such environmental factors in future microbiome studies.

The lexicon of antimicrobial peptides: a complete set of arginine and tryptophan sequences.

Our understanding of the activity of cationic antimicrobial peptides (AMPs) has focused on well-characterized natural sequences, or limited sets of synthetic peptides designed de novo. We have undertaken a comprehensive investigation of the underlying primary structural features that give rise to the development of activity in AMPs. We consider a complete set of all possible peptides, up to 7 residues long, composed of positively charged arginine (R) and / or hydrophobic tryptophan (W), two features most commonly associated with activity. We found the shortest active peptides were 4 or 5 residues in length, and the overall landscapes of activity against gram-positive and gram-negative bacteria and a yeast were positively correlated. For all three organisms we found a single activity peak corresponding to sequences with around 40% R; the presence of adjacent W duplets and triplets also conferred greater activity. The mechanistic basis of these activities comprises a combination of lipid binding, particularly to negatively charged membranes, and additionally peptide aggregation, a mode of action previously uninvestigated for such peptides. The maximum specific antimicrobial activity appeared to occur in peptides of around 10 residues, suggesting 'diminishing returns' for developing larger peptides, when activity is considered per residue of peptide.