scholarly journals Resist or perish: fate of a microbial population subjected to a periodic presence of antimicrobial

2019 ◽  
Author(s):  
Loïc Marrec ◽  
Anne-Florence Bitbol
Keyword(s):  
Antimicrobial Resistance ◽  
Stochastic Model ◽  
Death Rate ◽  
Microbial Population ◽  
Threshold Value ◽  
Microbial Populations ◽  
Population Composition ◽  
Resistance Evolution ◽  
Rare Mutations ◽  
The Impact

AbstractThe evolution of antimicrobial resistance can be strongly affected by variations of antimicrobial concentration. Here, we study the impact of periodic alternations of absence and presence of antimicrobial on resistance evolution in a microbial population, using a stochastic model that includes variations of both population composition and size, and fully incorporates stochastic population extinctions. We show that fast alternations of presence and absence of antimicrobial are inefficient to eradicate the microbial population and strongly favor the establishment of resistance, unless the antimicrobial increases enough the death rate. We further demonstrate that if the period of alternations is longer than a threshold value, the microbial population goes extinct upon the first addition of antimicrobial, if it is not rescued by resistance. We express the probability that the population is eradicated upon the first addition of antimicrobial, assuming rare mutations. Rescue by resistance can happen either if resistant mutants preexist, or if they appear after antimicrobial is added to the environment. Importantly, the latter case is fully prevented by perfect biostatic antimicrobials that completely stop division of sensitive microorganisms. By contrast, we show that the parameter regime where treatment is efficient is larger for biocidal drugs than for biostatic drugs. This sheds light on the respective merits of different antimicrobial modes of action.Author summaryAntimicrobials select for resistance, which threatens to make antimicrobials useless. Understanding the evolution of antimicrobial resistance is therefore of crucial importance. Under what circumstances are microbial populations eradicated by antimicrobials? Conversely, when are they rescued by resistance? We address these questions employing a stochastic model that incorporates variations of both population composition and size. We consider periodic alternations of absence and presence of antimicrobial, which may model a treatment. We find a threshold period above which the first phase with antimicrobial fully determines the fate of the population. Faster alternations strongly select for resistance, and are inefficient to eradicate the microbial population, unless the death rate induced by the treatment is large enough. For longer alternation periods, we calculate the probability that the microbial population gets eradicated. We further demonstrate the different merits of biostatic antimicrobials, which prevent sensitive microbes from dividing, and of biocidal ones, which kill sensitive microbes.

scholarly journals Quantifying the impact of a periodic presence of antimicrobial on resistance evolution in a homogeneous microbial population of fixed size

2018 ◽  
Author(s):  
Loïc Marrec ◽  
Anne-Florence Bitbol
Keyword(s):  
Antimicrobial Resistance ◽  
Microbial Population ◽  
Fitness Landscape ◽  
Resistant Bacteria ◽  
Fixed Size ◽  
Variable Environment ◽  
Resistance Evolution ◽  
Evolution Of Resistance ◽  
The Impact ◽  
Analytical Approaches

AbstractThe evolution of antimicrobial resistance often occurs in a variable environment, as antimicrobial is given periodically to a patient or added and removed from a medium. This environmental variability has a huge impact on the microorganisms’ fitness landscape, and thus on the evolution of resistance. Indeed, mutations conferring resistance often carry a fitness cost in the absence of antimicrobial, which may be compensated by subsequent mutations. As antimicrobial is added or removed, the relevant fitness landscape thus switches from a fitness valley to an ascending landscape or vice-versa.Here, we investigate the effect of these time-varying patterns of selection within a stochastic model. We focus on a homogeneous microbial population of fixed size subjected to a periodic alternation of phases of absence and presence of an antimicrobial that stops growth. Combining analytical approaches and stochastic simulations, we quantify how the time necessary for fit resistant bacteria to take over the microbial population depends on the period of the alternations. We demonstrate that fast alternations strongly accelerate the evolution of resistance, and that a plateau is reached once the period gets sufficiently small. Besides, the acceleration of resistance evolution is stronger for larger populations. For asymmetric alternations, featuring a different duration of the phases with and without antimicrobial, we shed light on the existence of a broad minimum of the time taken by the population to fully evolve resistance. At this minimum, if the alternations are sufficiently fast, the very first resistant mutant that appears ultimately leads to full resistance evolution within the population. This dramatic acceleration of the evolution of antimicrobial resistance likely occurs in realistic situations, and can have an important impact both in clinical and experimental situations.

scholarly journals Molecular Analysis of Surfactant-Driven Microbial Population Shifts in Hydrocarbon-Contaminated Soil

2000 ◽  
Vol 66 (7) ◽  
pp. 2959-2964 ◽  
Author(s):  
Gregory M. Colores ◽  
Richard E. Macur ◽  
David M. Ward ◽  
William P. Inskeep
Keyword(s):  
Gel Electrophoresis ◽  
Microbial Population ◽  
Denaturing Gradient Gel Electrophoresis ◽  
Microbial Populations ◽  
Alcohol Ethoxylate ◽  
Rrna Gene ◽  
Mineralization Kinetics ◽  
Gradient Gel Electrophoresis ◽  
The Impact ◽  
Parallel Cultivation

ABSTRACT We analyzed the impact of surfactant addition on hydrocarbon mineralization kinetics and the associated population shifts of hydrocarbon-degrading microorganisms in soil. A mixture of radiolabeled hexadecane and phenanthrene was added to batch soil vessels. Witconol SN70 (a nonionic, alcohol ethoxylate) was added in concentrations that bracketed the critical micelle concentration (CMC) in soil (CMC′) (determined to be 13 mg g−1). Addition of the surfactant at a concentration below the CMC′ (2 mg g−1) did not affect the mineralization rates of either hydrocarbon. However, when surfactant was added at a concentration approaching the CMC′ (10 mg g−1), hexadecane mineralization was delayed and phenanthrene mineralization was completely inhibited. Addition of surfactant at concentrations above the CMC′ (40 mg g−1) completely inhibited mineralization of both phenanthrene and hexadecane. Denaturing gradient gel electrophoresis of 16S rRNA gene segments showed that hydrocarbon amendment stimulatedRhodococcus and Nocardia populations that were displaced by Pseudomonas and Alcaligenespopulations at elevated surfactant levels. Parallel cultivation studies revealed that the Rhodococcus population can utilize hexadecane and that the Pseudomonas andAlcaligenes populations can utilize both Witconol SN70 and hexadecane for growth. The results suggest that surfactant applications necessary to achieve the CMC alter the microbial populations responsible for hydrocarbon mineralization.

scholarly journals Adapt or Perish: Evolutionary Rescue in a Gradually Deteriorating Environment

Genetics ◽  
2020 ◽  
Vol 216 (2) ◽  
pp. 573-583
Author(s):  
Loïc Marrec ◽  
Anne-Florence Bitbol
Keyword(s):  
Antimicrobial Resistance ◽  
Carrying Capacity ◽  
Microbial Population ◽  
Stochastic Simulations ◽  
Extinction Time ◽  
Resistance Evolution ◽  
Mutation Probability ◽  
Evolutionary Rescue ◽  
Population Sizes ◽  
Environment Degradation

We investigate the evolutionary rescue of a microbial population in a gradually deteriorating environment, through a combination of analytical calculations and stochastic simulations. We consider a population destined for extinction in the absence of mutants, which can survive only if mutants sufficiently adapted to the new environment arise and fix. We show that mutants that appear later during the environment deterioration have a higher probability to fix. The rescue probability of the population increases with a sigmoidal shape when the product of the carrying capacity and of the mutation probability increases. Furthermore, we find that rescue becomes more likely for smaller population sizes and/or mutation probabilities if the environment degradation is slower, which illustrates the key impact of the rapidity of environment degradation on the fate of a population. We also show that our main conclusions are robust across various types of adaptive mutants, including specialist and generalist ones, as well as mutants modeling antimicrobial resistance evolution. We further express the average time of appearance of the mutants that do rescue the population and the average extinction time of those that do not. Our methods can be applied to other situations with continuously variable fitnesses and population sizes, and our analytical predictions are valid in the weak-to-moderate mutation regime.

scholarly journals Adapt or perish: Evolutionary rescue in a gradually deteriorating environment

2020 ◽  
Author(s):  
Loïc Marrec ◽  
Anne-Florence Bitbol
Keyword(s):  
Antimicrobial Resistance ◽  
Carrying Capacity ◽  
Microbial Population ◽  
Stochastic Simulations ◽  
Extinction Time ◽  
Resistance Evolution ◽  
Mutation Probability ◽  
Evolutionary Rescue ◽  
Population Sizes ◽  
Environment Degradation

AbstractWe investigate the evolutionary rescue of a microbial population in a gradually deteriorating environment, through a combination of analytical calculations and stochastic simulations. We consider a population destined for extinction in the absence of mutants, which can only survive if mutants sufficiently adapted to the new environment arise and fix. We show that mutants that appear later during the environment deterioration have a higher probability to fix. The rescue probability of the population increases with a sigmoidal shape when the product of the carrying capacity and of the mutation probability increases. Furthermore, we find that rescue becomes more likely for smaller population sizes and/or mutation probabilities if the environment degradation is slower, which illustrates the key impact of the rapidity of environment degradation on the fate of a population. We also show that our main conclusions are robust across various types of adaptive mutants, including specialist and generalist ones, as well as mutants modeling antimicrobial resistance evolution. We further express the average time of appearance of the mutants that do rescue the population and the average extinction time of those that do not. Our methods can be applied to other situations with continuously variable fitnesses and population sizes, and our analytical predictions are valid in the weak-to-moderate mutation regime.

Agrichemical impact on growth and survival of non-target apple phyllosphere microorganisms

2007 ◽  
Vol 53 (1) ◽  
pp. 45-55 ◽  
Author(s):  
Monika Walter ◽  
Christopher Miles Frampton ◽  
Kirsty Sarah Helen Boyd-Wilson ◽  
Patricia Harris-Virgin ◽  
Nicholas William Waipara
Keyword(s):  
Microbial Population ◽  
Field Research ◽  
Apple Orchard ◽  
Microbial Populations ◽  
Indicator Organisms ◽  
In Planta ◽  
Growth And Survival ◽  
Before And After ◽  
Population Counts ◽  
The Impact

The impact of conventional agrichemicals commonly used in New Zealand apple production on non-target, culturable phyllosphere microbial populations was studied in the laboratory (agar, leaf, and seedling assays) and field (apple orchard). Morphologically distinct bacteria (three), yeasts (five), and filamentous microfungi (two) were used as indicator species. The agar assay showed that agrichemical toxicity to microorganisms was dependent on product type, product rate, and organism studied. While the fungicides metiram and captan stopped or severely reduced growth of nearly all microorganisms studied, the insecticides tebufenozide and lufenuron and the fungicide nitrothal-isopropyl showed the least amount of microbial toxicity, each affecting 2 of the 10 indicator organisms studied. In the leaf assay a single agrichemical application at field rate either reduced or increased microbial population counts, again depending on product and microorganism. Repeated agrichemical applications, however, reduced microbial population numbers from 10- to 10 000-fold in planta. Further field research validated these findings, although differences in microbial numbers before and after agrichemical applications were less dramatic. In the orchard, total organism numbers recovered within 2–6 days, but species richness (sum of recognizable taxonomic units) declined. Agrichemicals clearly affected non-target, culturable surface microorganisms. The importance of diversity and stability of microbial populations for disease control still needs to be established.Key words: apple, microbial abundance, microbial richness, 6-benzyladenine, buprofezin, captan, cyprodinil, difenoconazole, dithianon, dodine, kresoxim-methyl, lufenuron, metiram, myclobutanil, nitrothal-isopropyl, tebufenozide, triadimefon.

scholarly journals Evolution of Pseudomonas aeruginosa Antimicrobial Resistance and Fitness under Low and High Mutation Rates

2016 ◽  
Vol 60 (3) ◽  
pp. 1767-1778 ◽  
Author(s):  
Gabriel Cabot ◽  
Laura Zamorano ◽  
Bartolomé Moyà ◽  
Carlos Juan ◽  
Alfonso Navas ◽  
...  
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Antimicrobial Resistance ◽  
Efflux Pump ◽  
Polymerase Activity ◽  
Chronic Infections ◽  
Content Type ◽  
Genomic Deletions ◽  
Resistance Evolution ◽  
Biological Cost ◽  
The Impact

Pseudomonas aeruginosa, a major cause of nosocomial and chronic infections, is considered a paradigm of antimicrobial resistance development. However, the evolutionary trajectories of antimicrobial resistance and the impact of mutator phenotypes remain mostly unexplored. Therefore, whole-genome sequencing (WGS) was performed in lineages of wild-type and mutator (ΔmutS) strains exposed to increasing concentrations of relevant antipseudomonal agents. WGS provided a privileged perspective of the dramatic effect of mutator phenotypes on the accumulation of random mutations, most of which were transitions, as expected. Moreover, a frameshift mutagenic signature, consistent with error-prone DNA polymerase activity as a consequence of SOS system induction, was also seen. This effect was evidenced for all antibiotics tested, but it was higher for fluoroquinolones than for cephalosporins or carbapenems. Analysis of genotype versus phenotype confirmed expected resistance evolution trajectories but also revealed new pathways. Classical mechanisms included multiple mutations leading to AmpC overexpression (ceftazidime), quinolone resistance-determining region (QRDR) mutations (ciprofloxacin),oprDinactivation (meropenem), and efflux pump overexpression (ciprofloxacin and meropenem). Groundbreaking findings included gain-of-function mutations leading to the structural modification of AmpC (ceftazidime), novel DNA gyrase (GyrA) modification (ciprofloxacin), and the alteration of the β-lactam binding site of penicillin-binding protein 3 (PBP3) (meropenem). A further striking finding was seen in the evolution of meropenem resistance, selecting for specific extremely large (>250 kb) genomic deletions providing a growth advantage in the presence of the antibiotic. Finally, fitness and virulence varied within and across evolved antibiotic-resistant populations, but mutator lineages showed a lower biological cost for some antibiotics.

The impact of climate temperature on counts, recovery, and death rates due to SARS-CoV-2 in South Africa. (Preprint)

2020 ◽  
Author(s):  
Neven Chetty ◽  
Bamise Adeleye ◽  
Abiola Olawale Ilori
Keyword(s):  
South Africa ◽  
South African ◽  
Death Rate ◽  
Western Cape ◽  
Warm Climate ◽  
The South ◽  
Recovery Rates ◽  
Death Rates ◽  
Online Resource ◽  
The Impact

BACKGROUND The impact of climate temperature on the counts (number of positive COVID-19 cases reported), recovery, and death rates of COVID-19 cases in South Africa's nine provinces was investigated. The data for confirmed cases of COVID-19 were collected for March 25 and June 30, 2020 (14 weeks) from South Africa's Government COVID-19 online resource, while the daily provincial climate temperatures were collected from the website of the South African Weather Service. Our result indicates that a higher or lower climate temperature does not prevent or delay the spread and death rates but shows significant positive impacts on the recovery rates of COVID-19 patients. Thus, it indicates that the climate temperature is unlikely to impose a strict limit on the spread of COVID-19. There is no correlation between the cases and death rates, an indicator that no particular temperature range is closely associated with a faster or slower death rate of COVID-19 patients. As evidence from our study, a warm climate temperature can only increase the recovery rate of COVID-19 patients, ultimately impacting the death and active case rates and freeing up resources quicker to enable health facilities to deal with those patients' climbing rates who need treatment. OBJECTIVE This study aims to investigate the impact of climate temperature variation on the counts, recovery, and death rates of COVID-19 cases in all South Africa's provinces. The findings were compared with those of countries with comparable climate temperature values. METHODS The data for confirmed cases of COVID-19 were collected for March 25 and June 30 (14 weeks) for South African provinces, including daily counts, death, and recovery rates. The dates were grouped into two, wherein weeks 1-5 represent the periods of total lockdown to contain the spread of COVID-19 in South Africa. Weeks 6-14 are periods where the lockdown was eased to various levels 4 and 3. The daily information of COVID-19 count, death, and recovery was obtained from South Africa's Government COVID-19 online resource (https://sacoronavirus.co.za). Daily provincial climate temperatures were collected from the website of the South African Weather Service (https://www.weathersa.co.za). The provinces of South Africa are Eastern Cape, Western Cape, Northern Cape, Limpopo, Northwest, Mpumalanga, Free State, KwaZulu-Natal, Western Cape, and Gauteng. Weekly consideration was given to the daily climate temperature (average minimum and maximum). The recorded values were considered, respectively, to be in the ratio of death-to-count (D/C) and recovery-to-count (R/C). Descriptive statistics were performed for all the data collected for this study. The analyses were performed using the Person’s bivariate correlation to analyze the association between climate temperature, death-to-count, and recovery-to-count ratios of COVID-19. RESULTS The results showed that higher climate temperatures aren't essential to avoid the COVID-19 from being spread. The present results conform to the reports that suggested that COVID-19 is unlike the seasonal flu, which does dissipate as the climate temperature rises [17]. Accordingly, the ratio of counts and death-to-count cannot be concluded to be influenced by variations in the climate temperatures within the study areas. CONCLUSIONS The study investigates the impact of climate temperature on the counts, recovery, and death rates of COVID-19 cases in all South Africa's provinces. The findings were compared with those of countries with comparable climate temperatures as South Africa. Our result indicates that a higher or lower climate temperature does not prevent or delay the spread and death rates but shows significant positive impacts on the recovery rates of COVID-19 patients. Warm climate temperatures seem not to restrict the spread of the COVID-19 as the count rate was substantial at every climate temperatures. Thus, it indicates that the climate temperature is unlikely to impose a strict limit on the spread of COVID-19. There is no correlation between the cases and death rates, an indicator that there is no particular temperature range of the climatic conditions closely associated with a faster or slower death rate of COVID-19 patients. However, other shortcomings in this study's process should not be ignored. Some other factors may have contributed to recovery rates, such as the South African government's timely intervention to announce a national lockout at the early stage of the outbreak, the availability of intensive medical care, and social distancing effects. Nevertheless, this study shows that a warm climate temperature can only help COVID-19 patients recover more quickly, thereby having huge impacts on the death and active case rates.

scholarly journals Empirical Stochastic Model of Multi-GNSS Measurements

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4566
Author(s):  
Dominik Prochniewicz ◽  
Kinga Wezka ◽  
Joanna Kozuchowska
Keyword(s):  
Stochastic Model ◽  
Density Ratio ◽  
Functional Model ◽  
Optimal Solution ◽  
Global Navigation Satellite Systems ◽  
Unknown Parameters ◽  
Short Baseline ◽  
Stochastic Parameters ◽  
Dependent Model ◽  
The Impact

The stochastic model, together with the functional model, form the mathematical model of observation that enables the estimation of the unknown parameters. In Global Navigation Satellite Systems (GNSS), the stochastic model is an especially important element as it affects not only the accuracy of the positioning model solution, but also the reliability of the carrier-phase ambiguity resolution (AR). In this paper, we study in detail the stochastic modeling problem for Multi-GNSS positioning models, for which the standard approach used so far was to adopt stochastic parameters from the Global Positioning System (GPS). The aim of this work is to develop an individual, empirical stochastic model for each signal and each satellite block for GPS, GLONASS, Galileo and BeiDou systems. The realistic stochastic model is created in the form of a fully populated variance-covariance (VC) matrix that takes into account, in addition to the Carrier-to-Noise density Ratio (C/N0)-dependent variance function, also the cross- and time-correlations between the observations. The weekly measurements from a zero-length and very short baseline are utilized to derive stochastic parameters. The impact on the AR and solution accuracy is analyzed for different positioning scenarios using the modified Kalman Filter. Comparing the positioning results obtained for the created model with respect to the results for the standard elevation-dependent model allows to conclude that the individual empirical stochastic model increases the accuracy of positioning solution and the efficiency of AR. The optimal solution is achieved for four-system Multi-GNSS solution using fully populated empirical model individual for satellite blocks, which provides a 2% increase in the effectiveness of the AR (up to 100%), an increase in the number of solutions with errors below 5 mm by 37% and a reduction in the maximum error by 6 mm compared to the Multi-GNSS solution using the elevation-dependent model with neglected measurements correlations.

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