Biology subjects

Bonhoeffer, S.

Publications and source records attributed to Bonhoeffer, S..

36 records · Page 2Linked to original sources

bioRxiv2021

Pathogen invasion-dependent tissue reservoirs and plasmid-encoded antibiotic degradation promote within-host plasmid transfer

Many plasmids encode antibiotic resistance genes. Through conjugation, plasmids can be rapidly disseminated. Previous work identified gut-luminal donor/recipient blooms and tissue-lodged plasmid-bearing persister cells of the enteric pathogen Salmonella enterica serovar Typhimurium (S.Tm) that survive antibiotic-therapy in host tissues, as factors promoting plasmid dissemination among Enterobacteriaceae. However, the buildup of tissue reservoirs and their contribution to plasmid spread await experimental demonstration. Here, we asked if re-seeding-plasmid acquisition-invasion cycles by S.Tm could serve to diversify tissue-lodged plasmid reservoirs, and thereby promote plasmid spread. Starting with intraperitoneal mouse infections, we demonstrate that S.Tm cells re-seeding the gut lumen initiate clonal expansion. ESBL plasmid-encoded gut luminal antibiotic degradation by donors can foster recipient survival under beta-lactam antibiotic treatment, enhancing transconjugant formation upon re-seeding. S.Tm transconjugants can subsequently re-enter host tissues introducing the new plasmid into the tissue-lodged reservoir. Population dynamics analyses pinpoint recipient migration into the gut lumen as rate-limiting for plasmid transfer dynamics in our model. Priority effects may be a limiting factor for reservoir formation in host tissues. Overall, our proof-of-principle data indicates that luminal antibiotic degradation and shuttling between the gut lumen and tissue-resident reservoirs can promote the accumulation and spread of plasmids within a host over time.

evolutionary biology↗

bioRxiv2021

The competition dynamics of resistant and sensitive infections

Antimicrobial resistance is a major health problem with complex dynamics. Resistance may occur in an area because treated infections mutated and developed resistance, and the proportion of infections in a population may then increase. We developed a novel and flexible model that captures several features of resistance dynamics and competition. The model is able to account for many antimicrobials and thus can generally explore competition dynamics and their impact on pathogens and bacteria. Unlike simpler models, our nested model allows the population of resistant pathogen to smoothly increase or decrease. Time dependent dynamics are incorporated into difference equations which examines the effects of 12 parameters. This enables us to explicitly include three key competition dynamics: the transmission cost of resistance that occurs between hosts, the fitness cost of resistance that occurs within untreated hosts, and the release of this competition (from the fitness cost) that occurs once a host is treated. For malaria, our results suggest that without competitive release, drug resistance does not emerge. However, once emerged, competitive release has little effect, and the best way to mitigate the spread is to ensure that treatment is very effective.

bioinformatics↗

bioRxiv2021

Epidemiological and evolutionary consequences of CRISPR-Cas reactivity

Adaptive immune systems face a control challenge: they should react with enough strength to clear an infection while avoiding to harm their organism. CRISPR-Cas systems are adaptive immune systems of prokaryotes that defend against fast evolving viruses. Here, we explore the CRISPR-Cas control challenge and look how its reactivity, i.e. its probability to acquire a new resistance, impacts the epidemiological outcome of a phage outbreak and the prokaryotes fitness. We show that in the absence of phage evolution, phage extinction is driven by the probability to acquire at least one resistance. However, when phage evolution is fast, phage extinction is characterised by an epidemiological critical threshold: any reactivity below this critical threshold leads to phage survival whereas any reactivity above it leads to phage extinction. We also show that in the absence of autoimmunity, high levels of reactivity evolve. However, when CRISPR-Cas systems are prone to autoimmune reactions, intermediate levels of reactivity are evolutionarily optimal. These results help explaining why natural CRISPR-Cas systems do not show high levels of reactivity. Author summaryCRISPR-Cas systems are adaptive immune systems that use a complex 3-step molecular mechanism to defend prokaryotes against phages. Viral infections of populations defending with CRISPR-Cas can result in rapid phage extinction or in medium-term phage maintenance. What controls phage fate? Using mathematical modeling, we show that two parameters control this outcome: the phage escape rate and CRISPR-Cas reactivity (i.e. its probability of resistance acquisition upon infection). Furthermore, CRISPR-Cas reactivity impacts host fitness. From this, we derive that 1) CRISPR-Cas reactivity is a key predictor of the efficiency and of the cost of a CRISPR-Cas system, 2) there is an optimal reactivity balancing the cost of autoimmunity and immune efficiency and 3) high phage escape rate selects for higher CRISPR-Cas reactivities.

microbiology↗

bioRxiv2020

Quantifying the influence of space on social group structure

When studying social behaviour, it can be important to determine whether the behaviour being recorded is actually driven by the social preferences of individuals. Many studies of animal social networks therefore attempt to disentangle social preferences from spatial preferences or restrictions. As such, there are a large number of techniques with which to test whether results from network analysis can be explained by random interactions, or interactions driven by similarities in space use. Selecting which of these methods to use will require determining to what extent space might influence social structure. Here we present a simple method (Social Spatial Community Assignment Test) to quantify the similarity between social and spatial group structure. We then apply this method to both simulated and empirical data of social interactions to demonstrate that it can successfully tease apart social and spatial explanations for groups. We first show that it can resolve the relative importance of space and social preferences in three simulated datasets in which interaction patterns are driven purely by space use, purely by social preferences or a mixture of the two. We then apply it to empirical data from a long-term study of free-ranging house mice. We find that while social structure is similar to spatial structure, there is still evidence for individuals possessing social preferences, with the importance of these preferences fluctuating between seasons. Our method provides a robust way of assessing the overlap between spatial and social structure, which will be invaluable to researchers when investigating the underlying drivers of social structure in wild populations.

animal behavior and cognition↗

bioRxiv2020

Evolutionary mechanisms that determine which bacterial genes are carried on plasmids

The evolutionary pressures that determine the location (chromosomal or plasmid-borne) of bacterial genes are not fully understood. We investigate these pressures through mathematical modelling in the context of antibiotic resistance, which is often found on plasmids. Our central finding is that gene location is under positive frequency-dependent selection: the higher the frequency of one form of resistance compared to the other, the higher its relative fitness. This can keep moderately beneficial genes on plasmids, despite occasional plasmid loss. For these genes, positive frequency-dependence leads to a priority effect: whichever form is acquired first - through either mutation or horizontal gene transfer - has time to increase in frequency and thus become difficult to displace. Higher rates of horizontal transfer of plasmid-borne than chromosomal genes therefore predict moderately beneficial genes will be found on plasmids. Gene flow between plasmid and chromosome allows chromosomal forms to arise, but positive frequencydependent selection prevents these from establishing. Further modelling shows this effect is particularly pronounced when genes are shared across a large number of species, suggesting antibiotic resistance genes are often found on plasmids because they are moderately beneficial across many species. We also revisit previous theoretical work - relating to the role of local adaptation in explaining gene location and to plasmid persistence - in light of our findings. Impact StatementBacterial genes can either reside on the chromosome or on plasmids, extra-chromosomal genetic structures which can be transferred from cell to cell. The distribution of genes between plasmid and chromosome is not random: certain types of genes are particularly likely to be plasmid-associated. This includes a number of clinically important traits, such as antibiotic resistance and virulence factors. The evolutionary mechanisms that give rise to this pattern are not well understood. Plasmids are occasionally lost during cell replication and thus less reliably inherited than the chromosome, and genes are free to transition between plasmid and chromosome: so what keeps genes on plasmids? We address this question using a mathematical model. The key prediction from our model is that the relative fitness of chromosomal and plasmid-borne genes depends on their relative frequencies ( positive frequency-dependent selection). In other words, the fitness of a plasmid-borne gene will be higher in a population in which the chromosomal gene is rare (and vice-versa). This positive-frequency dependence can keep moderately beneficial genes on plasmids, despite occasional plasmid loss. This leads to a priority effect: whichever form of the gene (i.e. plasmid-borne or chromosomal) is acquired first has time to increase in frequency and thus become difficult to displace. Therefore, the relative rate of acquiring the gene on the plasmid vs the chromosome predicts where the gene will be found. Further modelling shows this effect is particularly pronounced when genes are beneficial across a large number of species. All together, the hypothesis that emerges from our work is that plasmid-borne genes are moderately beneficial; functional across a large number of species; and rarely acquired through chromosomal mutation. We suggest traits like antibiotic resistance are often found on plasmids because these genes commonly fulfill these criteria.

evolutionary biology↗

bioRxiv2020

Ecological effects of stress drive bacterial evolvability under sub-inhibitory antibiotic treatments

1Stress is thought to increase mutation rate and thus to accelerate evolution. In the context of antibiotic resistance, sub-inhibitory treatments could then lead to enhanced evolvability, thereby fueling the adaptation of pathogens. Conducting a meta-analysis of published experimental data as well as our own experiments, we found that the increase in mutation rates triggered by antibiotic treatments is often canceled out by reduced population size, resulting in no overall increase in genetic diversity. A careful analysis of the effect of ecological factors on genetic diversity revealed that the potential for regrowth during recovery phase after treatment plays a crucial role in evolvability and is the main predictor of increased genetic diversity in experimental data.

evolutionary biology↗

bioRxiv2020

Estimating seroprevalence with imperfect serological tests: a cutoff-free approach

Large-scale serological testing in the population is essential to determine the true extent of the current SARS-CoV-2 pandemic. Serological tests measure antibody responses against pathogens and use predefined cutoff levels that dichotomize the quantitative test measures into sero-positives and negatives and use this as a proxy for past infection. With the imperfect assays that are currently available to test for past SARS-CoV-2 infection, the fraction of seropositive individuals in serosurveys is a biased estimator of the cumulative incidence and is usually corrected to account for the sensitivity and specificity. Here we use an inference method -- referred to as mixture-model approach -- for the estimation of the cumulative incidence that does not require to define cutoffs by integrating the quantitative test measures directly into the statistical inference procedure. We confirm that the mixture model outperforms the methods based on cutoffs, leading to less bias and error in estimates of the cumulative incidence. We illustrate how the mixture model can be used to optimize the design of serosurveys with imperfect serological tests. We also provide guidance on the number of control and case sera that are required to quantify the tests ambiguity sufficiently to enable the reliable estimation of the cumulative incidence. Lastly, we show how this approach can be used to estimate the cumulative incidence of classes of infections with an unknown distribution of quantitative test measures. This is a very promising application of the mixture-model approach that could identify the elusive fraction of asymptomatic SARS-CoV-2 infections. An R-package implementing the inference methods used in this paper is provided. Our study advocates using serological tests without cutoffs, especially if they are used to determine parameters characterizing populations rather than individuals. This approach circumvents some of the shortcomings of cutoff-based methods at exactly the low cumulative incidence levels and test accuracies that we are currently facing in SARS-CoV-2 serosurveys. Author SummaryAs other pathogens, SARS-CoV-2 elicits antibody responses in infected people that can be detected in their blood serum as early as a week after the infection until long after recovery. The presence of SARS-CoV-2 specific antibodies can therefore be used as a marker of past infection, and the prevalence of seropositive people, i.e. people with specific antibodies, is a key measure to determine the extent of the SARS-CoV-2 pandemic. The serological tests, however, are usually not perfect, yielding false positive and false negative results. Here we exploit an approach that refrains from classifying people as seropositive or negative, but rather compares the antibody level of an individual to that of confirmed cases and controls. This approach leads to more reliable estimates of cumulative incidence, especially for the low prevalence and low test accuracies that we face during the current SARS-CoV-2 pandemic. We also show how this approach can be extended to infer the presence of specific types of cases that have not been used for validating the test, such as people that underwent a mild or asymptomatic infection.

immunology↗

bioRxiv2020

Estimating the rate of plasmid transfer in liquid mating cultures

Plasmids are important vectors for the spread of genes among diverse populations of bacteria. However, there is no standard method to determine the rate at which they spread horizontally via conjugation. Here, we compare commonly used methods on simulated and experimental data, and show that the resulting conjugation rate estimates often depend strongly on the time of measurement, the initial population densities, or the initial ratio of donor to recipient populations. Differences in growth rate, e.g. induced by sub-lethal antibiotic concentrations or temperature, can also significantly bias conjugation rate estimates. We derive a new end-point measure to estimate conjugation rates, which extends the well-known Simonsen method to include the effects of differences in population growth and conjugation rates from donors and transconjugants. We further derive analytical expressions for the parameter range in which these approximations remain valid. We present an easy to use R package and web interface which implement both new and previously existing methods to estimate conjugation rates. The result is a set of tools and guidelines for accurate and comparable measurement of plasmid conjugation rates. HighlightsO_LIWe present an R package and Shiny app to estimate conjugation rates from experimental data C_LIO_LIWe highlight the diversity of existing measures used to estimate conjugation rates C_LIO_LIWe show these measures are biased for populations with differing growth and conjugation rates from donors or transconjugants C_LIO_LIWe develop a new end-point measure which accounts for such differences in growth and conjugation rates C_LI

microbiology↗

bioRxiv2019

Microbial metabolically cohesive consortia and ecosystem functioning

Recent theory and experiments have reported a reproducible tendency for the coexistence of microbial species under controlled environmental conditions. This observation has been explained in the context of competition for resources and metabolic complementarity given that, in microbial communities, many excreted by-products of metabolism may also be resources. Microbial communities therefore play a key role in promoting their own stability and in shaping the niches of the constituent taxa. We suggest that an intermediate level of organisation between the species and the community level may be pervasive, where tightly-knit metabolic interactions create discrete consortia that are stably maintained. We call these units Metabolically Cohesive Consortia (MeCoCos) and we discuss the environmental context in which we expect their formation, and the ecological and evolutionary consequences of their existence. We argue that the ability to identify MeCoCos would open new avenues to link the species-, community-, and ecosystem-level properties, with consequences for our understanding of microbial ecology and evolution, and an improved ability to predict ecosystem functioning in the wild.

microbiology↗

bioRxiv2019

Extended-spectrum beta-lactamase antibiotic resistance plasmids have diverse transfer rates and can be spread in the absence of selection

Horizontal gene transfer, mediated by conjugative plasmids, is a major driver of the global spread of antibiotic resistance. However, the relative contributions of factors that underlie the spread of clinically relevant plasmids are unclear. Here, we quantified conjugative transfer dynamics of Extended Spectrum Beta-Lactamase (ESBL) producing plasmids in the absence of antibiotics. We showed that clinical Escherichia coli strains natively associated with ESBL-plasmids conjugate efficiently with three distinct E. coli strains and one Salmonella enterica serovar Typhimurium strain, reaching final transconjugant frequencies of up to 1% within 24 hours in vitro. The variation of final transconjugant frequencies varied among plasmids, donors and recipients and was better explained by variation in conjugative transfer efficiency than by variable clonal expansion. We identified plasmid-specific genetic factors, specifically the presence/absence of transfer genes, that influenced final transconjugant frequencies. Finally, we investigated plasmid spread within the mouse intestine, demonstrating qualitative agreement between plasmid spread in vitro and in vivo. This suggests a potential for the prediction of plasmid spread in the gut of animals and humans, based on in vitro testing. Altogether, this may allow the identification of resistance plasmids with high spreading potential and help to devise appropriate measures to restrict their spread.

microbiology↗

bioRxiv2019

Stochastic gene expression influences the selection of antibiotic resistance mutations

Bacteria can resist antibiotics by expressing enzymes that remove or deactivate drug molecules. Here, we study the effects of gene expression stochasticity on efflux and enzymatic resistance. We construct an agent-based model that stochastically simulates multiple biochemical processes in the cell and we observe the growth and survival dynamics of the cell population. Resistance-enhancing mutations are introduced by varying parameters that control the enzyme expression or efficacy. We find that stochastic gene expression can cause complex dynamics in terms of survival and extinction for these mutants. Regulatory mutations, which augment the frequency and duration of resistance gene transcription, can provide limited resistance by increasing mean expression. Structural mutations, which modify the enzyme or efflux efficacy, provide most resistance by improving the binding affinity of the resistance protein to the antibiotic; increasing the enzymes catalytic rate alone does not contribute to resistance. Overall, we identify conditions where regulatory mutations are selected over structural mutations, and vice versa. Our findings show that stochastic gene expression is a key factor underlying efflux and enzymatic resistances and should be taken into consideration in future antibiotic research.

evolutionary biology↗

bioRxiv2018

Cancer-Induced Immunosuppression can enable Effectiveness of Immunotherapy through Bistability Generation: a mathematical and computational Examination

Cancer immunotherapies rely on how interactions between cancer and immune system cells are constituted. The more essential to the emergence of the dynamical behavior of cancer growth these are, the more effectively they may be used as mechanisms for interventions. Mathematical modeling can help unearth such connections, and help explain how they shape the dynamics of cancer growth. Here, we explored whether there exist simple, consistent properties of cancer-immune system interaction (CISI) models that might be harnessed to devise effective immunotherapy approaches. We did this for a family of three related models of increasing complexity. To this end, we developed a base model of CISI, which captures some essential features of the more complex models built on it. We find that the base model and its derivates can plausibly reproduce biological behavior that is consistent with the notion of an immunological barrier. This behavior is also in accord with situations in which the suppressive effects exerted by cancer cells on immune cells dominate their proliferative effects. Under these circumstances, the model family may display a pattern of bistability, where two distinct, stable states (a cancer-free, and a full-grown cancer state) are possible. Increasing the effectiveness of immune-caused cancer cell killing may remove the basis for bistability, and abruptly tip the dynamics of the system into a cancer-free state. Additionally, in combination with the administration of immune effector cells, modifications in cancer cell killing may be harnessed for immunotherapy without the need for resolving the bistability. We use these ideas to test immunotherapeutic interventions in silico in a stochastic version of the base model. This bistability-reliant approach to cancer interventions might offer advantages over those that comprise gradual declines in cancer cell numbers.

cancer biology↗

bioRxiv2018

Antibiotic treatment protocols revisited: The challenges of a conclusive assessment by mathematical modeling

Hospital-acquired bacterial infections lead to prolonged hospital stays and increased mortality. The problem is exacerbated by antibiotic resistant strains that delay or impede effective treatment. To ensure a successful therapy and to manage antibiotic resistance, treatment protocols that draw on several different antibiotics might be used. This includes the administration of drug cocktails to individual patients (\"combination therapy\") but also the random assignment of drugs to different patients (\"mixing\") and a regular switch in the default drug used in the hospital from drug A to drug B and back (\"cycling\"). For the past 20 years, mathematical models have been used to assess the prospects of antibiotic combination therapy, mixing, and cycling. But while tendencies in their ranking across studies have emerged, the picture remains surprisingly inconclusive and incomplete. In this article, we review existing modeling studies and demonstrate by means of examples how methodological factors complicate the emergence of a consistent picture. These factors include the choice of the criterion by which the effects of the protocols are compared, the model implementation, and its analysis. We thereafter discuss how progress can be made and suggest future modeling directions.

evolutionary biology↗

bioRxiv2017

Effective polyploidy causes phenotypic delay and influences bacterial evolvability

Whether mutations in bacteria exhibit a noticeable delay before expressing their corresponding mutant phenotype was discussed intensively in the 1940s-50s, but the discussion eventually waned for lack of supportive evidence and perceived incompatibility with observed mutant distributions in fluctuation tests. Phenotypic delay in bacteria is widely assumed to be negligible, despite lack of direct evidence. Here we revisited the question using recombineering to introduce antibiotic resistance mutations into E. coli at defined time points and then tracking expression of the corresponding mutant phenotype over time. Contrary to previous assumptions, we found a substantial median phenotypic delay of 3-4 generations. We provided evidence that the primary source of this delay is multifork replication causing cells to be effectively polyploid, whereby wild-type gene copies transiently mask the phenotype of recessive mutant gene copies in the same cell. Using modeling and simulation methods, we explored the consequences of effective polyploidy for mutation rate estimation by fluctuation tests and sequencing-based methods. For recessive mutations, despite the substantial phenotypic delay, the per-copyor per-genome mutation rate is accurately estimated. However, the per-cell rate cannot be estimated by existing methods. Finally, with a mathematical model, we showed that effective polyploidy increases the frequency of costly recessive mutations in the standing genetic variation, and thus their potential contribution to evolutionary adaptation, while drastically reducing the chance that de novo recessive mutations can rescue populations facing a harsh environmental change such as antibiotic treatment. Overall, we have identified phenotypic delay and effective polyploidy as previously overlooked but essential components in bacterial evolvability, including antibiotic resistance evolution.\n\nAuthor summaryWhat is the time delay between the occurrence of a genetic mutation in a bacterial cell and manifestation of its phenotypic effect? We show that antibiotic resistance mutations in E.coli show a remarkably long phenotypic delay of 3-4 bacterial generations. The primary underlying mechanism of this delay is effective polyploidy. In a polyploid cell with multiple chromosomes, once a mutation arises on one of the chromosomes, the presence of non-mutated, wild-type gene copies on other chromosomes may mask the phenotype of the mutation. One implication of this finding is that conventional methods to determine the mutation rates of bacteria do not detect polyploidy and thus underestimate their potential for adaptation. More generally, the effect that a new mutation may become useful only in the \"grand-children of the grand-children\" suggests that pre-existing mutations are more important for surviving sudden environmental catastrophe.

evolutionary biology↗

bioRxiv2017

Death and population dynamics affect mutation rate estimates and evolvability under stress in bacteria

The stress-induced mutagenesis paradigm postulates that in response to stress, bacteria increase their genome-wide mutation rate, in turn increasing the chances that a descendant is able to withstand the stress. This has implications for antibiotic treatment: exposure to sub-inhibitory doses of antibiotics has been reported to increase bacterial mutation rates, and thus probably the rate at which resistance mutations appear and lead to treatment failure.\n\nMeasuring mutation rates under stress, however, is problematic, because existing methods assume there is no death. Yet sub-inhibitory stress levels may induce a substantial death rate. Death events need to be compensated by extra replication to reach a given population size, thus giving more opportunities to acquire mutations. We show that ignoring death leads to a systematic overestimation of mutation rates under stress.\n\nWe developed a system using plasmid segregation to measure death and growth rates simultaneously in bacterial populations. We use it to replicate classical experiments reporting antibiotic-induced mutagenesis. We found that a substantial death rate occurs at the tested sub-inhibitory concentrations, and taking this death into account lowers and sometimes removes the signal for stress-induced mutagenesis. Moreover even when antibiotics increase mutation rate, sub-inhibitory treatments do not increase genetic diversity and evolvability, again because of effects of the antibiotics on population dynamics.\n\nBeside showing that population dynamic is a crucial but neglected parameter affecting evolvability, we provide better experimental and computational tools to study evolvability under stress, leading to a re-assessment of the magnitude and significance of the stress-induced mutagenesis paradigm.

evolutionary biology↗

bioRxiv2017

Dissecting HIV Virulence: Heritability Of Setpoint Viral Load, CD4+ T Cell Decline And Per-Parasite Pathogenicity

Pathogen strains may differ in virulence because they attain different loads in their hosts, or because they induce different disease-causing mechanisms independent of their load. In evolutionary ecology, the latter is referred to as \"per-parasite pathogenicity\". Using viral load and CD4+ T cell measures from 2014 HIV-1 subtype B infected individuals enrolled in the Swiss HIV Cohort Study, we investigated if virulence -- measured as the rate of decline of CD4+ T cells -- and per-parasite pathogenicity are heritable from donor to recipient. We estimated heritability by donor-recipient regressions applied to 196 previously identified transmission pairs, and by phylogenetic mixed models applied to a phylogenetic tree inferred from HIV pol sequences. Regressing the CD4+ T cell declines and per-parasite pathogenicities of the transmission pairs did not yield heritability estimates significantly different from zero. With the phylogenetic mixed model, however, our best estimate for the heritability of the CD4+ T cell decline is 17% (5%-30%), and that of the per-parasite pathogenicity is 17% (4%-29%). Further, we confirm that the set-point viral load is heritable, and estimate a heritability of 29% (12%-46%). Interestingly, the pattern of evolution of all these traits differs significantly from neutrality, and is most consistent with stabilizing selection for the set-point viral load, and with directional selection for the CD4+ T cell decline and the per-parasite pathogenicity. Our analysis shows that the viral genetype affects virulence mainly by modulating the per-parasite pathogenicity, while the indirect effect via the set-point viral load is minor.

evolutionary biology↗

bioRxiv2017

Antibiotic Resistance Detection Is Essential For Gonorrhoea Point-Of-Care Testing: A Mathematical Modelling Study

Antibiotic resistance is threatening to make gonorrhoea untreatable. Point-of-care (POC) tests that detect resistance promise individually tailored treatment, but might lead to more treatment and higher levels of resistance. We investigate the impact of POC tests on antibiotic-resistant gonorrhoea. We used data about the prevalence and incidence of gonorrhoea in men who have sex with men (MSM) and heterosexual men and women (HMW) to calibrate a mathematical gonorrhoea transmission model. With this model, we simulated four clinical pathways for the diagnosis and treatment of gonorrhoea: POC test with (POC + R) and without (POC - R) resistance detection, culture, and nucleic acid amplification tests (NAATs). We calculated the proportion of resistant infections, cases averted after 5 years, and compared how fast resistant infections spread in the populations. The proportion of resistant infections after 30 years is lowest for POC + R (median MSM: 0.18%, HMW: 0.12%), and increases for culture (MSM: 1.19%, HWM: 0.13%), NAAT (MSM: 100%, HMW: 99.27%), and POC - R (MSM: 100%, HMW: 99.73%). NAAT leads to 36 366 (median MSM) and 1 228 (median HMW) observed cases after 5 years. When compared with NAAT, POC + R results in most cases averted after 5 years (median MSM: 3 353, HMW: 118 per 100 000 persons). POC tests that detect resistance with intermediate sensitivity slow down resistance spread more than NAAT. POC tests with very high sensitivity for the detection of resistance are needed to slow down resistance spread more than using culture. POC with high sensitivity to detect antibiotic resistance can keep gonorrhoea treatable longer than culture or NAAT. POC tests without reliable resistance detection should not be introduced because they can accelerate the spread of antibiotic-resistant gonorrhoea.

epidemiology↗

bioRxiv2016

Modeling antimicrobial cycling and mixing: Differences arising from an individual-based versus a population-based perspective

BackgroundIn order to manage bacterial infections in hospitals in the face of antibiotic resistance, the two treatment protocols \"mixing\" and \"cycling\" have received considerable attention both from modelers and clinicians. However, the terms are not used in exactly the same way by both groups.\n\nObjectivesWe aim to investigate a model that comes closer to clinical practice and compare the predictions to the standard model.\n\nMethodsWe set up two deterministic models, implemented as a set of differential equations, for the spread of bacterial infections in a hospital. Following the traditional approach, the first model takes a population-based perspective. The second model, in contrast, takes the drug use of individual patients into account.\n\nResultsThe alternative model can indeed lead to different predictions than the standard model. We provide examples for which in the new model, the opposite strategy maximizes the number of uninfected patients or minimizes the rate of spread of double resistance.\n\nConclusionsWhile the traditional models provide valuable insight, care is needed in the interpretation of results.

epidemiology↗