Spatial structure facilitates evolutionary rescue by drug resistance.

The complex spatial structure and the heterogeneity within biofilms lead to the emergence of specific social behaviors. However, the impact of resistant mutants within bacterial communities is still mostly unknown. Thus, we determined whether antibiotic resistant mutants display selfish or altruistic behaviors in mixed Pseudomonas aeruginosa biofilms exposed to antibiotics. ECFP-tagged P. aeruginosa strain PAO1 and its EYFP-tagged derivatives hyperproducing the β-lactamase AmpC or the efflux pump MexAB-OprM were used to develop single or mixed biofilms. Mature biofilms were challenged with different concentrations of β-lactams to monitor biofilm structural dynamics, using confocal laser scanning microscopy (CLSM), and population dynamics, through enumeration of viable cells. While exposure of single wild-type PAO1 biofilms to β-lactams lead to a major reduction in bacterial load, it had little effect on biofilms formed by the resistant mutants. However, the most reveling finding was that bacterial load of wild-type PAO1 was significantly increased when growing in mixed biofilms compared to single biofilms. In agreement with CFU enumeration data, CLSM images revealed the amplification of the resistant mutants and their protection of susceptible populations. These findings show that mutants expressing diverse resistance mechanisms, including β-lactamases, but also, as evidenced for the first time, efflux pumps, protect the whole biofilm community, preserving susceptible populations from the effect of antibiotics. Thus, these results are a step forward to understanding antibiotic resistance dynamics in biofilms, as well as the population biology of bacterial pathogens in chronic infections, where the coexistence of susceptible and resistant variants is a hallmark.

The evolution of multiple antibiotic resistance is an increasing global problem. Resistance mutations are known to impair fitness, and the evolution of resistance to multiple drugs depends both on their costs individually and on how they interact—epistasis. Information on the level of epistasis between antibiotic resistance mutations is of key importance to understanding epistasis amongst deleterious alleles, a key theoretical question, and to improving public health measures. Here we show that in an antibiotic-free environment the cost of multiple resistance is smaller than expected, a signature of pervasive positive epistasis among alleles that confer resistance to antibiotics. Competition assays reveal that the cost of resistance to a given antibiotic is dependent on the presence of resistance alleles for other antibiotics. Surprisingly we find that a significant fraction of resistant mutations can be beneficial in certain resistant genetic backgrounds, that some double resistances entail no measurable cost, and that some allelic combinations are hotspots for rapid compensation. These results provide additional insight as to why multi-resistant bacteria are so prevalent and reveal an extra layer of complexity on epistatic patterns previously unrecognized, since it is hidden in genome-wide studies of genetic interactions using gene knockouts.

Historically, the use of antibiotics was not well regulated in veterinary medicine. The emergence of antibiotic resistance (ABR) in pathogenic bacteria in human and veterinary medicine has driven the need for greater antibiotic stewardship. The preservation of certain antibiotic classes for use exclusively in humans, especially in cases of multidrug resistance, has highlighted the need for veterinarians to reduce its use and redefine dosage regimens of antibiotics to ensure efficacy and guard against the development of ABR pathogens. The minimum inhibitory concentration (MIC), the lowest concentration of an antibiotic drug that will prevent the growth of a bacterium, is recognised as a method to assist in antibiotic dosage determination. Minimum inhibitory concentrations sometimes fail to deal with first-step mutants in bacterial populations; therefore dosing regimens based solely on MIC can lead to the development of ABR. The mutant prevention concentration (MPC) is the minimum inhibitory antibiotic concentration of the most resistant first-step mutant. Mutant prevention concentration determination as a complementary and sometimes preferable alternative to MIC determination for veterinarians when managing bacterial pathogens. The results of this study focused on livestock pathogens and antibiotics used to treat them, which had a MIC value of 0.25 µg/mL for enrofloxacin against all 27 isolates of Salmonella typhimurium. The MPC values were 0.50 µg/mL, with the exception of five isolates that had MPC values of 4.00 µg/mL. The MPC test yielded 65.52% (18 isolates) Salmonella isolates with florfenicol MICs in the sensitive range, while 11 isolates were in the resistant range. Seventeen isolates (58.62%) of Pasteurella multocida had MIC values in the susceptible range and 41.38% (12 isolates) had an intermediate MIC value. Mutant prevention concentration determinations as done in this study is effective for the antibiotic treatment of bacterial infections and minimising the development of resistance. The MPC method can be used to better control to prevent the development of antibiotic drug resistance used in animals.

For an infecting bacterium the human body provides several potential ecological niches with both internally (e.g. host immunity) and externally (e.g. antibiotic use) imposed growth restrictions that are expected to drive adaptive evolution in the bacterium, including the development of antibiotic resistance. To determine the extent and pattern of heterogeneity generated in a bacterial population during long-term antibiotic treatment, we examined in a monoclonal Mycobacterium tuberculosis infection antibiotic resistant mutants isolated from one patient during a 9-years period. There was a progressive accumulation of resistance mutations in the infecting clone. Furthermore, apparent clonal sweeps as well as co-existence of different resistant mutants were observed during this time, demonstrating that during treatment there is a high degree of dynamics in the bacterial population. These findings have important implications for diagnostics and treatment of drug resistant tuberculosis infections.

Mutations at seven recombinationally distinct chloroplast loci confer antibiotic resistance on chloroplast ribosomes of the green alga Chlamydomonas reinhardtii. Assays of polynucleotide-directed amino acid incorporation by ribosomes reconstituted from mutant and wild type subunits demonstrate that streptomycin, neamine/kanamycin and spectinomycin resistance mutations specifically affect the small ribosomal subunit, whereas mutations to erythromycin resistance affect the large subunit. Although in each case the subunit site of antibiotic resistance is the same as that observed in analogous mutations in Escherichia coli, the number of loci conferring resistance to a given antibiotic differs in the two organisms. We have previously shown that streptomycin resistance mutations in Chlamydomonas map at five discrete loci (one nuclear and four chloroplast), and that mutations to neamine/kanamycin and spectinomycin resistance appear to define a single chloroplast locus. Results presented here confirm our previous report that all chloroplast erythromycin resistance mutations isolated to date fall into two recombinationally distinct loci, and indicate that mutants at one of these loci may be further divided on the basis of their level of cross resistance to other macrolide antibiotics.

Objective To analyze the antibiotic resistance of Helicobacter pylori (Hp) to common clinical antibiotics in Jinhua area, and explore the mutation and distribution of clarithromycin and levofloxacin resistance genes. Methods A total of 398 Hp strains were isolated from gastroscope samples during January to December 2016 in Jinhua Central Hospital. The resistance of these strains to 7 antibiotics was analyzed in vitro. Meanwhile, the related genes which was resistant to levofloxacin and clarithromycin were determined by gene sequencing. Results Of the 398 Hp strains, the resistance rates to metronidazole, levofloxacin, clarithromycin were 80.15%, 38.69% and 12.56%, respectively. No strain was resistant to furazolidone, amoxicillin, gentamicin or tetracycline. There were 70 (17.59%) Hp strains susceptible to all tested antibiotics, 163 (40.95%) mono-, 135 (33.92%) double- and 30 (7.54%) triple-drug resistant strains. The clarithromycin resistance gene mutation loci was A2143G in 23S rRNA Ⅴgene. Meanwhile the amino acid mutations of levofloxacin resistance gene gyrA were N87K and D91H/G/N/Y. Conclusions Hp resistance to metronidazole, levofloxacin and clarithromycin are high in Jinhua area of Zhejiang. The clarithromycin resistance gene mutation loci is 2143 gene loci of 23S rRNA Ⅴ and the levofloxacin resistance gene mutation is 87 and 91 gene loci of gyrA in this area. Key words: Helicobacter pylori; Drug resistance; Gene mutation

It has been established that in order to study thermo- and electrophysical phenomena in wet steam turbines, studies of the wet steam flow volume charge effect on its dielectric and thermophysical properties have recently been carried out at A. Pidhornyi Institute of Mechanical Engineering Problems of the National Academy of Sciences of Ukraine. According to their results, it was established that the most representative electrophysical parameter, which allows to evaluate changes in the thermophysical properties of steam, which occur under the action of its own volume charge, is its dielectric constant. It is assumed that the value of the dielectric constant of an electrified steam can be significantly different from the value for a neutral steam, and a mathematical assessment of its possible change is made. It has been confirmed that the influence of electrophysical phenomena caused by the wet steam flow electrification is significant, but is not taken into account in the existing physical and mathematical thermodynamic models of the steam expansion process. It is proved that in order to clarify the main thermodynamic parameters and calculated characteristics of the electrified wet steam flow, it is necessary to determine how its dielectric constant changes. On the basis of the analysis, the relevance of experimental determination of the dielectric constant of a wet steam flow with a volume charge in order to obtain the dependence of its change on the temperature and pressure of the flow, as well as the density of the volume charge, is substantiated. To perform the research, a gas dynamic laboratory plant, which allows to obtain a wet steam flow with a volume charge, was used. The internal space of the flow part of a real wet steam turbine has a significant size and allows the formation of a flow with a volume charge of a complex spatial configuration and structure. It is emphasized that in the flow part of the plant of a small volume, in contrast to the turbine flow part, significant technical difficulties arise when organizing the conditions for the occurrence of a wet steam flow with a volume charge. Taking this into account, at the first stage, it was decided to conduct a study of a steam flow with a volume charge flowing into the atmosphere in a laboratory room with a sufficient volume to form its spatial structure. To estimate the value of its dielectric constant, the inductive method (L-method) of determining dielectric properties, in which the substance under study is introduced into the inductive solenoid cell, was chosen. Experiments were conducted and, according to the obtained data, it is possible to make a preliminary assessment of the change in dielectric constant in the presence of a volume electric charge in the steam flow

In recent years, emerging sequencing technologies and computational tools have driven a tidal wave of research on host-associated microbiomes, particularly the gut microbiome. These studies demonstrate numerous connections between the gut microbiome and vital host functions, primarily in humans, model organisms, and domestic animals. As the adaptive importance of the gut microbiome becomes clearer, interest in studying the gut microbiomes of wild populations has increased, in part due to the potential for discovering conservation applications. The study of wildlife gut microbiomes holds many new challenges and opportunities due to the complex genetic, spatial, and environmental structure of wild host populations, and the potential for these factors to interact with the microbiome. The emerging picture of adaptive coevolution in host-microbiome relationships highlights the importance of understanding microbiome variation in the context of host population genetics and landscape heterogeneity across a wide range of host populations. We propose a conceptual framework for understanding wildlife gut microbiomes in relation to landscape variables and host population genetics, including the potential of approaches derived from landscape genetics. We use this framework to review current research, synthesize important trends, highlight implications for conservation, and recommend future directions for research. Specifically, we focus on how spatial structure and environmental variation interact with host population genetics and microbiome variation in natural populations, and what we can learn from how these patterns of covariation differ depending on host ecological and evolutionary traits.

Overcoming resistance.

Natural microbial populations often have complex spatial structures. This can impact their evolution, in particular the ability of mutants to take over. While mutant fixation probabilities are known to be unaffected by sufficiently symmetric structures, evolutionary graph theory has shown that some graphs can amplify or suppress natural selection, in a way that depends on microscopic update rules. We propose a model of spatially structured populations on graphs directly inspired by batch culture experiments, alternating within-deme growth on nodes and migration-dilution steps, and yielding successive bottlenecks. This setting bridges models from evolutionary graph theory with Wright-Fisher models. Using a branching process approach, we show that spatial structure with frequent migrations can only yield suppression of natural selection. More precisely, in this regime, circulation graphs, where the total incoming migration flow equals the total outgoing one in each deme, do not impact fixation probability, while all other graphs strictly suppress selection. Suppression becomes stronger as the asymmetry between incoming and outgoing migrations grows. Amplification of natural selection can nevertheless exist in a restricted regime of rare migrations and very small fitness advantages, where we recover the predictions of evolutionary graph theory for the star graph.

The techniques of optical beam shaping have enabled progress in a broad range of interdisciplinary science and engineering, owing to the unique properties and promising applications of their created structured light. However, the conventional methods, which are based on fully coherent optics approaches, introduce several adverse effects such as speckles noise in the generated beams and susceptible to be disturbed in complex environment (e.g. turbulent atmospheres), because of the sensitive coherent light-matter interaction. To overcome those side effects, a new protocol relied on the partially coherent beam shaping has been developed. By elaborately tailoring the complex spatial coherence structure of a partially coherent beam, the desired beam profile and trajectory with high beam quality and robust propagation feature in complex environment can be generated. In this review, we present an overview of such unconventional partially coherent beam shaping with a focus on the important role of the complex spatial coherence structure engineering. Partially coherent beam shaping not only provides an efficient means for resisting the disadvantages in coherent optics methods but also enables new applications in novel optical imaging and tweezers.

A method is developed to estimate harbor seiche at Marina di Carrara, Italy, from the properties of wind-generated incident waves outside the harbor. A linear model of the spatial structure of amplified seiche modes is combined with empirical estimates of the response of each mode to variable incident wave forcing. These empirical coefficients parameterize the complex nonlinear transfer of energy from wind waves to lower frequency seiche. As at other small harbors (<1 km2 surface area) on ocean coasts, and consistent with previous analyses at Carrara, the observed seiche is relatively energetic at several periods between about 1 and 15 min that are highly amplified theoretically, and the spatial structure of modeled and observed seiches agree as well. The longest seiche (≈15 min) mode is almost spatially uniform within the harbor and dominates with low-energy, short-period incident wind waves (measured 1 km offshore of the harbor). Increased wave energy and longer periods excite shorter period (1–3 min) seiche modes with more complex spatial structure, including small areas of high amplification, which have led to operational issues. The energy in each of the six most energetic seiche modes is related in this paper empirically to offshore incident wind wave height and peak period, allowing detailed predictions of harbor seiche from routine wind wave forecasts. The approach appears applicable to relatively small, shallow harbors with reflective quay walls, in which the exterior harbor mouth is exposed, and the interior sheltered from energetic wind-generated waves.

Image style transfer is a hot issue in the field of computer vision. It refers to the process that an image (content image) has a specified artistic style (style image) through the algorithm, which has an important application value. The complex spatial structure in the process of style transfer will make the details blurred and the local structure of the image distorted. In this paper, a new feature detection network is introduced for feature extraction of style content and style content, which has fewer simple parameters. In the transformation network, the adaptive Instance standardization layer is added after the convolution layer to improve the retention ability of the spatial structure of the content image. Large convolution kernels are replaced to reduce the number of model parameters while maintaining the same receptive field. The network model built in this paper can realize the rapid migration of various styles, strengthen the structural characteristics of images, and improve the detail effect of stylized images significantly.

Direct measurements of temporal evolution of the ion energy distribution functions (IEDFs) in a pulsed magnetized hydrogen plasma with a complex spatial structure are presented. The results show a remarkable split of the curves into two peaks of energy that draw away as the spatial structure expands until they reach a complete separation. The behavior is explained in terms of × drift velocity plasma dynamics and its influence on the IEDFs, with consequences for ion temperature measurements. The development of a criterion to calculate the ion temperatures allows us to obtain the temporal evolution of this parameter for the three ion species, and .

Foreword. Preface. Contributors. Part I Concepts and Methods in Bacterial Population Genetics. 1 The Coalescent of Bacterial Populations. 1.1 Background and Motivation. 1.2 Population Reproduction Models. 1.3 Time and the Effective Population Size. 1.4 The Genealogy of a Sample of Size n. 1.5 From Coalescent Time to Real Time. 1.6 Mutations. 1.7 Demography. 1.8 Recombination and Gene Conversion. 1.9 Summary. 2 Linkage, Selection, and the Clonal Complex. 2.1 Introduction Historical Overview. 2.2 Recombination, Linkage, and Substructure. 2.3 Neutrality versus Selection. 2.4 Clustering Techniques. 3 Sequence-Based Analysis of Bacterial Population Structures. 3.1 Introduction. 3.2 Alignments. 3.3 Phylogenetic Methods. 3.4 Measures of Uncertainty. 3.5 Beyond the Tree Model. 4 Genetic Recombination and Bacterial Population Structure. 4.1 Introduction. 4.2 Constraints on LGT. 4.3 Infl uences of LGT on Sequence Analyses. 4.4 The Detection of Individual LGT Events. 4.5 The Estimation of Homologous Recombination Rates. 4.6 Properly Accounting for LGT During Sequence Analyses. 4.7 Questions Relating Directly to LGT. 5 Statistical Methods for Detecting the Presence of Natural Selection in Bacterial Populations. 5.1 Introduction. 5.2 Natural Selection. 5.3 Statistical Methods for Detecting the Presence of Natural Selection. 5.4 Statistical Methods for Bacterial Populations. 5.5 An Example. 5.6 Discussion and Perspective. 6 Demographic Infl uences on Bacterial Population Structure. 6.1 Bacterial Population Size. 6.2 Measures of Genetic Diversity. 6.3 The Concept of Effective Population Size. 6.4 Inferring Past Demography from Genetic Sequence Data. 6.5 Population Subdivision. 6.6 What is a Bacterial Population? 6.7 Conclusion. 7 Population Genomics of Bacteria. 7.1 Introduction. 7.2 Classical Bacterial Population Genetics. 7.3 The Genomics Era. 7.4 Bacterial Population Genomics. 7.5 Next-Gen Bacterial Population Genomics. 7.6 Next-Gen Genomics Technology. 7.7 Next-Gen Genomic Data Analysis. 7.8 Conclusions/Future Prospects. 8 The Use of MLVA and SNP Analysis to Study the Population Genetics of Pathogenic Bacteria. 8.1 Introduction. 8.2 MLVA and Other DNA Fragment-Based Methods. 8.3 SNP and DNA Sequence-Based Methods. 8.4 Conclusion. Part II Population Genetics of Select Bacterial Pathogens. 9 Population Genetics of Bacillus: Phylogeography of Anthrax in North America. 9.1 Introduction. 9.2 History of Anthrax in North America. 9.3 The Anthrax Districts after 1944. 9.4 Molecular Genotyping of B. anthracis. 9.5 Genotypes within the Anthrax Districts in North America. 9.6 Phylogenetic Resolution within the WNA Lineage. 9.7 Phylogeographic Resolution within the Ames Lineage. 9.8 Additional B. anthracis Genotypes in North America. 9.9 Conclusions. 10 Population Genetics of Campylobacter. 10.1 Introduction. 10.2 Human Infection. 10.3 Genetic Structure. 10.4 Models of Campylobacter Evolution. 10.5 Clades and Species. 10.6 Conclusion. 11 Population Genetics of Enterococcus. 11.1 Introduction. 11.2 Antibiotic Resistance. 11.3 Vancomycin Resistance. 11.4 VRE: A Zoonosis or Not? 11.5 Population Structure and Genetic Evolution: Similarities and Differences Between E. faecium and E. faecalis. 11.6 What Is Driving GD in E. faecium and E. faecalis? 11.7 The Accessory Genome of E. faecium and E. faecalis. 11.8 Summary, Conclusions, and Future Perspectives. 12 Population Biology of Lyme Borreliosis Spirochetes. 12.1 Introduction. 12.2 Genome Organization of LB Spirochetes. 12.3 Genotyping of LB Spirochetes and Phylogenetic Tools. 12.4 Population Biology and Evolution of LB Spirochetes. 12.5 Do LB Species Exist? 12.6 Future Research Avenues. 13 Population Genetics of Neisseria meningitidis. 13.1 Introduction. 13.2 A Brief History of Typing of Meningococci. 13.3 Species Separation. 13.4 Sampling Strategies. 13.5 The Clonal Complexes of Meningococci. 13.6 Forces Shaping the Meningococcal Metalineage. 13.7 Virulence, a Mysterious Trait. 13.8 Population Effect of Meningococcal Vaccines. 13.9 Antibiotic Resistance and Meningococcal Lineages. 13.10 Concluding Remarks. 14 Population Genetics of Pathogenic Escherichia coli. 14.1 Introduction. 14.2 E. coli Population Genetics: Clonal or not Clonal? 14.3 The E. coli Phylogenetic Structure. 14.4 The Evolutionary History of a Host-Specifi c Obligate Pathogen: The Shigella and EIEC Case Study. 14.5 What Makes You an Opportunistic Pathogen? 14.6 The Virulence Resistance Trade-off. 14.7 Concluding Remarks. 15 Population Genetics of Salmonella: Selection for Antigenic Diversity. 15.1 Introduction. 15.2 Generation Timescale Diversifi cation. 15.3 Antigenic Diversity in Salmonella. 15.4 Why Are Diverse H and O Antigens Maintained in Salmonella? 15.5 Conclusions. 16 Population Genetics of Staphylococcus. 16.1 Introduction. 16.2 Overview of The Staphylococcal Population Structure. 16.3 Staphylococcal Population Structure in Specific Disease Contexts. 16.4 Origin and Maintenance of Staphylococcal Genetic Variation. 16.5 Macroevolutionary Considerations and Concluding Remarks. Appendix 1 Diversity and Differentiation. 17 Population Genetics of Streptococcus. 17.1 Habitats, Transmission, and Disease. 17.2 Classical Strain Typing. 17.3 Multilocus Sequence Typing (MLST) Based on Housekeeping Genes. 17.4 Species Boundaries and Gene Flow. 17.5 Niche-driving Genes. 17.6 Bacterial Population Dynamics and Selection. 17.7 Machinery of Genetic Change, Revisited. 18 Population Genetics of Vibrios. 18.1 Introduction. 18.2 V. cholerae. 18.3 V. parahaemolyticus . 18.4 V. vulnificus. 18.5 Conclusions. References. Index.