Why There Are No Essential Genes on Plasmids

Mobile genetic elements such as plasmids are important for the evolution of prokaryotes. It has been suggested that there are differences between functions coded for by mobile genes and those in the "core” genome and that these differences can be seen between plasmids and chromosomes. In particular, it has been suggested that essential genes, such as those involved in the formation of structural proteins or in basic metabolic functions, are rarely located on plasmids. We model competition between genotypically varying bacteria within a single population to investigate whether selection favors a chromosomal location for essential genes. We find that in general, chromosomal locations for essential genes are indeed favored. This is because the inheritance of chromosomes is more stable than that for plasmids. We define the "degradation” rate as the rate at which chance genetic processes, for example, mutation, deletion, or translocation, render essential genes nonfunctioning. The only way in which plasmids can be a location for functioning essential genes is if chromosomal genes degrade faster than plasmid genes. If the two degradation rates are equal, or if plasmid genes degrade faster than chromosomal genes, functioning essential genes will be found only on chromosome

Is antimicrobial resistance evolution accelerating?

Globally, antimicrobials are a main pillar of medical, veterinary, and agriculture interventions [1,2]. In all cases, resistance of microbes against antimicrobials is prevalent. The problem is exacerbated by the drying up of the antibiotic pipeline, as economic incentives to develop new drugs are very limited. In antifungals, the range of available compounds is also low with only 4 main classes of drugs available to treat fungal infections in humans and 6 main classes used in agriculture, with 1 class, the azoles, used in both [1]. The problem of drug resistance evolution has been observed early on in the antibiotic era [3,4]. Ultimately, however, the introduction of each antimicrobial resulted in resistance evolution in target and nontarget microbes. In realization of this problem, some antibiotics such as daptomycin were even developed with avoiding resistance evolution in mind, yet it took only 2 years from the introduction of daptomycin until resistance was recorded [4]. But how fast is resistance evolving? Here, we want to discuss how fast resistance emerges after the introduction of antimicrobials. We base this on widely cited data in the literature for antibiotics ([4–7]; see also Fig 1A, based on [8]) and compared this to data on antifungal resistance [9,10]. Replotting the antibiotic data (Fig 1B), by displaying the time from introduction to resistance emergence over the year of introduction, suggests that the evolution of antibiotic resistance is accelerating over time. The same trend can be observed for antifungals (Fig 1C and 1D). In the following, we focus on (1) the quality of the underlying data and (2) possible explanations for this pattern of accelerating resistance

Can high-risk fungicides be used in mixtures without selecting for fungicide resistance?

Fungicide mixtures produced by the agrochemical industry often contain low-risk fungicides, to which fungal pathogens are fully sensitive, together with high-risk fungicides known to be prone to fungicide resistance. Can these mixtures provide adequate disease control while minimizing the risk for the development of resistance? We present a population dynamics model to address this question. We found that the fitness cost of resistance is a crucial parameter to determine the outcome of competition between the sensitive and resistant pathogen strains and to assess the usefulness of a mixture. If fitness costs are absent, then the use of the high-risk fungicide in a mixture selects for resistance and the fungicide eventually becomes nonfunctional. If there is a cost of resistance, then an optimal ratio of fungicides in the mixture can be found, at which selection for resistance is expected to vanish and the level of disease control can be optimized

The Red Queen and the persistence of linkage-disequilibrium oscillations in finite and infinite populations

<p>Abstract</p> <p>Background</p> <p>The Red Queen Hypothesis (RQH) suggests that the coevolutionary dynamics of host-parasite systems can generate selection for increased host recombination. Since host-parasite interactions often have a strong genetic basis, recombination between different hosts can increase the fraction of novel and potentially resistant offspring genotypes. A prerequisite for this mechanism is that host-parasite interactions generate persistent oscillations of linkage disequilibria (LD).</p> <p>Results</p> <p>We use deterministic and stochastic models to investigate the persistence of LD oscillations and its impact on the RQH. The standard models of the Red Queen dynamics exhibit persistent LD oscillations under most circumstances. Here, we show that altering the standard model from discrete to continuous time or from simultaneous to sequential updating results in damped LD oscillations. This suggests that LD oscillations are structurally not robust. We then show that in a stochastic regime, drift can counteract this dampening and maintain the oscillations. In addition, we show that the amplitude of the oscillations and therefore the strength of the resulting selection for or against recombination are inversely proportional to the size of the (host) population.</p> <p>Conclusion</p> <p>We find that host parasite-interactions cannot generally maintain oscillations in the absence of drift. As a consequence, the RQH can strongly depend on population size and should therefore not be interpreted as a purely deterministic hypothesis.</p

Bacterial growth properties at low optical densities

A method for accurate quantification of growth rate and yield of bacterial populations at low densities was developed with a modified version of a stepwise linear model for fitting growth curves based on optical density measurements, and adapted to measurements at low optical densities in 96-well microtiter plates. The method can be used for rapid and precise estimates of growth rate and yield, based on optical density measurements of large numbers of cultures of Escherichia coli. E. coli B lines were serially propagated at low glucose concentration during a long-term evolution experiment. Growth rate and yield of populations sampled from each of 12 lines that evolved for 20,000 generations under these conditions and two ancestral clones was measured. Populations were grown at three different glucose concentrations. Consistent with earlier findings, statistical analysis showed that both exponential growth rate and yield per unit of glucose differed significantly between the three glucose concentrations tested. Significant adaptation of the evolved populations to the nutrient conditions in which they evolved for 20,000 generations was observe

High Epitope Expression Levels Increase Competition between T Cells

Both theoretical predictions and experimental findings suggest that T cell populations can compete with each other. There is some debate on whether T cells compete for aspecific stimuli, such as access to the surface on antigen-presenting cells (APCs) or for specific stimuli, such as their cognate epitope ligand. We have developed an individual-based computer simulation model to study T cell competition. Our model shows that the expression level of foreign epitopes per APC determines whether T cell competition is mainly for specific or aspecific stimuli. Under low epitope expression, competition is mainly for the specific epitope stimuli, and, hence, different epitope-specific T cell populations coexist readily. However, if epitope expression levels are high, aspecific competition becomes more important. Such between-specificity competition can lead to competitive exclusion between different epitope-specific T cell populations. Our model allows us to delineate the circumstances that facilitate coexistence of T cells of different epitope specificity. Understanding mechanisms of T cell coexistence has important practical implications for immune therapies that require a broad immune response

Neuraminidase Inhibitor Resistance in Influenza: Assessing the Danger of Its Generation and Spread

Neuraminidase Inhibitors (NI) are currently the most effective drugs against influenza. Recent cases of NI resistance are a cause for concern. To assess the danger of NI resistance, a number of studies have reported the fraction of treated patients from which resistant strains could be isolated. Unfortunately, those results strongly depend on the details of the experimental protocol. Additionally, knowing the fraction of patients harboring resistance is not too useful by itself. Instead, we want to know how likely it is that an infected patient can generate a resistant infection in a secondary host, and how likely it is that the resistant strain subsequently spreads. While estimates for these parameters can often be obtained from epidemiological data, such data is lacking for NI resistance in influenza. Here, we use an approach that does not rely on epidemiological data. Instead, we combine data from influenza infections of human volunteers with a mathematical framework that allows estimation of the parameters that govern the initial generation and subsequent spread of resistance. We show how these parameters are influenced by changes in drug efficacy, timing of treatment, fitness of the resistant strain, and details of virus and immune system dynamics. Our study provides estimates for parameters that can be directly used in mathematical and computational models to study how NI usage might lead to the emergence and spread of resistance in the population. We find that the initial generation of resistant cases is most likely lower than the fraction of resistant cases reported. However, we also show that the results depend strongly on the details of the within-host dynamics of influenza infections, and most importantly, the role the immune system plays. Better knowledge of the quantitative dynamics of the immune response during influenza infections will be crucial to further improve the results