The bacterial genetic determinants of Escherichia coli capacity to cause bloodstream infections in humans.

Escherichia coli is both a highly prevalent commensal and a major opportunistic pathogen causing bloodstream infections (BSI). A systematic analysis characterizing the genomic determinants of extra-intestinal pathogenic vs. commensal isolates in human populations, which could inform mechanisms of pathogenesis, diagnostic, prevention and treatment is still lacking. We used a collection of 912 BSI and 370 commensal E. coli isolates collected in France over a 17-year period (2000-2017). We compared their pangenomes, genetic backgrounds (phylogroups, STs, O groups), presence of virulence-associated genes (VAGs) and antimicrobial resistance genes, finding significant differences in all comparisons between commensal and BSI isolates. A machine learning linear model trained on all the genetic variants derived from the pangenome and controlling for population structure reveals similar differences in VAGs, discovers new variants associated with pathogenicity (capacity to cause BSI), and accurately classifies BSI vs. commensal strains. Pathogenicity is a highly heritable trait, with up to 69% of the variance explained by bacterial genetic variants. Lastly, complementing our commensal collection with an older collection from 1980, we predict that pathogenicity continuously increased through 1980, 2000, to 2010. Together our findings imply that E. coli exhibit substantial genetic variation contributing to the transition between commensalism and pathogenicity and that this species evolved towards higher pathogenicity.

Using early detection data to estimate the date of emergence of an epidemic outbreak.

While the first infection of an emerging disease is often unknown, information on early cases can be used to date it. In the context of the COVID-19 pandemic, previous studies have estimated dates of emergence (e.g., first human SARS-CoV-2 infection, emergence of the Alpha SARS-CoV-2 variant) using mainly genomic data. Another dating attempt used a stochastic population dynamics approach and the date of the first reported case. Here, we extend this approach to use a larger set of early reported cases to estimate the delay from first infection to the Nth case. We first validate our framework by running our model on simulated data. We then apply our model using data on Alpha variant infections in the UK, dating the first Alpha infection at (median) August 21, 2020 (95% interpercentile range across retained simulations (IPR): July 23-September 5, 2020). Next, we apply our model to data on COVID-19 cases with symptom onset before mid-January 2020. We date the first SARS-CoV-2 infection in Wuhan at (median) November 28, 2019 (95% IPR: November 2-December 9, 2019). Our results fall within ranges previously estimated by studies relying on genomic data. Our population dynamics-based modelling framework is generic and flexible, and thus can be applied to estimate the starting time of outbreaks in contexts other than COVID-19.

A stochastic analysis of the interplay between antibiotic dose, mode of action, and bacterial competition in the evolution of antibiotic resistance.

The use of an antibiotic may lead to the emergence and spread of bacterial strains resistant to this antibiotic. Experimental and theoretical studies have investigated the drug dose that minimizes the risk of resistance evolution over the course of treatment of an individual, showing that the optimal dose will either be the highest or the lowest drug concentration possible to administer; however, no analytical results exist that help decide between these two extremes. To address this gap, we develop a stochastic mathematical model of bacterial dynamics under antibiotic treatment. We explore various scenarios of density regulation (bacterial density affects cell birth or death rates), and antibiotic modes of action (biostatic or biocidal). We derive analytical results for the survival probability of the resistant subpopulation until the end of treatment, the size of the resistant subpopulation at the end of treatment, the carriage time of the resistant subpopulation until it is replaced by a sensitive one after treatment, and we verify these results with stochastic simulations. We find that the scenario of density regulation and the drug mode of action are important determinants of the survival of a resistant subpopulation. Resistant cells survive best when bacterial competition reduces cell birth and under biocidal antibiotics. Compared to an analogous deterministic model, the population size reached by the resistant type is larger and carriage time is slightly reduced by stochastic loss of resistant cells. Moreover, we obtain an analytical prediction of the antibiotic concentration that maximizes the survival of resistant cells, which may help to decide which drug dosage (not) to administer. Our results are amenable to experimental tests and help link the within and between host scales in epidemiological models.

Epidemiological dynamics of bacteriocin competition and antibiotic resistance.

Bacteriocins, toxic peptides involved in the competition between bacterial strains, are extremely diverse. Previous work on bacteriocin dynamics has highlighted the role of non-transitive 'rock-paper-scissors' competition in maintaining the coexistence of different bacteriocin profiles. The focus to date has primarily been on bacteriocin interactions at the within-host scale (i.e. within a single bacterial population). Yet in species such as Streptococcus pneumoniae, with relatively short periods of colonization and limited within-host diversity, ecological outcomes are also shaped by processes at the epidemiological (between-host) scale. Here, we first investigate bacteriocin dynamics and diversity in epidemiological models. We find that in these models, bacteriocin diversity is more readily maintained than in within-host models, and with more possible combinations of coexisting bacteriocin profiles. Indeed, maintenance of diversity in epidemiological models does not require rock-paper-scissors dynamics; it can also occur through a competition-colonization trade-off. Second, we investigate the link between bacteriocin diversity and diversity at antibiotic resistance loci. Previous work has proposed that bacterial duration of colonization modulates the fitness of antibiotic resistance. Due to their inhibitory effects, bacteriocins are a plausible candidate for playing a role in the duration of colonization episodes. We extend the epidemiological model of bacteriocin dynamics to incorporate an antibiotic resistance locus and demonstrate that bacteriocin diversity can indeed maintain the coexistence of antibiotic-sensitive and -resistant strains.

The Population Genomics of Increased Virulence and Antibiotic Resistance in Human Commensal Escherichia coli over 30 Years in France.

Escherichia coli is a commensal species of the lower intestine but is also a major pathogen causing intestinal and extraintestinal infections that is increasingly prevalent and resistant to antibiotics. Most studies on genomic evolution of E. coli used isolates from infections. Here, instead, we whole-genome sequenced a collection of 403 commensal E. coli isolates from fecal samples of healthy adult volunteers in France (1980 to 2010). These isolates were distributed mainly in phylogroups A and B2 (30% each) and belonged to 152 sequence types (STs), the five most frequent being ST10 (phylogroup A; 16.3%), ST73 and ST95 (phylogroup B2; 6.3 and 5.0%, respectively), ST69 (phylogroup D; 4.2%), and ST59 (phylogroup F; 3.9%), and 224 O:H serotypes. ST and serotype diversity increased over time. The O1, O2, O6, and O25 groups used in bioconjugate O-antigen vaccine against extraintestinal infections were found in 23% of the strains of our collection. The increase in frequency of virulence-associated genes and antibiotic resistance was driven by two evolutionary mechanisms. Evolution of virulence gene frequency was driven by both clonal expansion of STs with more virulence genes ("ST-driven") and increases in gene frequency within STs independent of changes in ST frequencies ("gene-driven"). In contrast, the evolution of resistance was dominated by increases in frequency within STs ("gene-driven"). This study provides a unique picture of the phylogenomic evolution of E. coli in its human commensal habitat over 30 years and will have implications for the development of preventive strategies. IMPORTANCE Escherichia coli is an opportunistic pathogen with the greatest burden of antibiotic resistance, one of the main causes of bacterial infections and an increasing concern in an aging population. Deciphering the evolutionary dynamics of virulence and antibiotic resistance in commensal E. coli is important to understand adaptation and anticipate future changes. The gut of vertebrates is the primary habitat of E. coli and probably where selection for virulence and resistance takes place. Unfortunately, most whole-genome-sequenced strains are isolated from pathogenic conditions. Here, we whole-genome sequenced 403 E. coli commensals isolated from healthy French subjects over a 30-year period. Virulence genes increased in frequency by both clonal expansion of clones carrying them and increases in frequency within clones, whereas resistance genes increased by within-clone increased frequency. Prospective studies of E. coli commensals should be performed worldwide to have a broader picture of evolution and adaptation of this species.

Success of prophylactic antiviral therapy for SARS-CoV-2: Predicted critical efficacies and impact of different drug-specific mechanisms of action.

Repurposed drugs that are safe and immediately available constitute a first line of defense against new viral infections. Despite limited antiviral activity against SARS-CoV-2, several drugs are being tested as medication or as prophylaxis to prevent infection. Using a stochastic model of early phase infection, we evaluate the success of prophylactic treatment with different drug types to prevent viral infection. We find that there exists a critical efficacy that a treatment must reach in order to block viral establishment. Treatment by a combination of drugs reduces the critical efficacy, most effectively by the combination of a drug blocking viral entry into cells and a drug increasing viral clearance. Below the critical efficacy, the risk of infection can nonetheless be reduced. Drugs blocking viral entry into cells or enhancing viral clearance reduce the risk of infection more than drugs that reduce viral production in infected cells. The larger the initial inoculum of infectious virus, the less likely is prevention of an infection. In our model, we find that as long as the viral inoculum is smaller than 10 infectious virus particles, viral infection can be prevented almost certainly with drugs of 90% efficacy (or more). Even when a viral infection cannot be prevented, antivirals delay the time to detectable viral loads. The largest delay of viral infection is achieved by drugs reducing viral production in infected cells. A delay of virus infection flattens the within-host viral dynamic curve, possibly reducing transmission and symptom severity. Thus, antiviral prophylaxis, even with reduced efficacy, could be efficiently used to prevent or alleviate infection in people at high risk.

From individual-based epidemic models to McKendrick-von Foerster PDEs: a guide to modeling and inferring COVID-19 dynamics.

We present a unifying, tractable approach for studying the spread of viruses causing complex diseases requiring to be modeled using a large number of types (e.g., infective stage, clinical state, risk factor class). We show that recording each infected individual's infection age, i.e., the time elapsed since infection, has three benefits. First, regardless of the number of types, the age distribution of the population can be described by means of a first-order, one-dimensional partial differential equation (PDE) known as the McKendrick-von Foerster equation. The frequency of type i is simply obtained by integrating the probability of being in state i at a given age against the age distribution. This representation induces a simple methodology based on the additional assumption of Poisson sampling to infer and forecast the epidemic. We illustrate this technique using French data from the COVID-19 epidemic. Second, our approach generalizes and simplifies standard compartmental models using high-dimensional systems of ordinary differential equations (ODEs) to account for disease complexity. We show that such models can always be rewritten in our framework, thus, providing a low-dimensional yet equivalent representation of these complex models. Third, beyond the simplicity of the approach, we show that our population model naturally appears as a universal scaling limit of a large class of fully stochastic individual-based epidemic models, where the initial condition of the PDE emerges as the limiting age structure of an exponentially growing population starting from a single individual.

The Effect of Habitat Choice on Evolutionary Rescue in Subdivided Populations.

AbstractEvolutionary rescue is the process by which a population, in response to an environmental change, successfully avoids extinction through adaptation. In spatially structured environments, dispersal can affect the probability of rescue. Here, we model an environment consisting of patches that degrade one after another, and we investigate the probability of rescue by a mutant adapted to the degraded habitat. We focus on the effects of dispersal and of immigration biases. We identify up to three regions delimiting the effect of dispersal on the probability of evolutionary rescue: (i) starting from low dispersal rates, the probability of rescue increases with dispersal; (ii) at intermediate dispersal rates, it decreases; and (iii) at large dispersal rates, it increases again with dispersal, except if mutants are too counterselected in not-yet-degraded patches. The probability of rescue is generally highest when mutant and wild-type individuals preferentially immigrate into patches that have already undergone environmental change. Additionally, we find that mutants that will eventually rescue the population most likely first appear in nondegraded patches. Overall, our results show that habitat choice, compared with the often-studied unbiased immigration scheme, can substantially alter the dynamics of population survival and adaptation to new environments.

On the evolutionary ecology of multidrug resistance in bacteria.

Resistance against different antibiotics appears on the same bacterial strains more often than expected by chance, leading to high frequencies of multidrug resistance. There are multiple explanations for this observation, but these tend to be specific to subsets of antibiotics and/or bacterial species, whereas the trend is pervasive. Here, we consider the question in terms of strain ecology: explaining why resistance to different antibiotics is often seen on the same strain requires an understanding of the competition between strains with different resistance profiles. This work builds on models originally proposed to explain another aspect of strain competition: the stable coexistence of antibiotic sensitivity and resistance observed in a number of bacterial species. We first identify a partial structural similarity in these models: either strain or host population structure stratifies the pathogen population into evolutionarily independent sub-populations and introduces variation in the fitness effect of resistance between these sub-populations, thus creating niches for sensitivity and resistance. We then generalise this unified underlying model to multidrug resistance and show that models with this structure predict high levels of association between resistance to different drugs and high multidrug resistance frequencies. We test predictions from this model in six bacterial datasets and find them to be qualitatively consistent with observed trends. The higher than expected frequencies of multidrug resistance are often interpreted as evidence that these strains are out-competing strains with lower resistance multiplicity. Our work provides an alternative explanation that is compatible with long-term stability in resistance frequencies.

Characterisation of vaccine breakthrough infections of SARS-CoV-2 Delta and Alpha variants and within-host viral load dynamics in the community, France, June to July 2021.

We compared PCR results from SARS-CoV-2-positive patients tested in the community in France from 14 June to 30 July 2021. In asymptomatic individuals, Cq values were significantly higher in fully vaccinated than non-fully vaccinated individuals (effect size: 1.7; 95% CI: 1-2.3; p < 10-6). In symptomatic individuals and controlling for time since symptoms, the difference vanished (p = 0.26). Infections with the Delta variant had lower Cq values at symptom onset than with Alpha (effect size: -3.32; 95% CI: -4.38 to -2.25; p < 10-6).

Early assessment of diffusion and possible expansion of SARS-CoV-2 Lineage 20I/501Y.V1 (B.1.1.7, variant of concern 202012/01) in France, January to March 2021.

The emergence of SARS-CoV-2 variant 20I/501Y.V1 (VOC-202012/1 or GR/501Y.V1) is concerning given its increased transmissibility. We reanalysed 11,916 PCR-positive tests (41% of all positive tests) performed on 7-8 January 2021 in France. The prevalence of 20I/501Y.V1 was 3.3% among positive tests nationwide and 6.9% in the Paris region. Analysing the recent rise in the prevalence of 20I/501Y.V1, we estimate that, in the French context, 20I/501Y.V1 is 52-69% more transmissible than the previously circulating lineages, depending on modelling assumptions.

Success of Escherichia coli O25b:H4 Sequence Type 131 Clade C Associated with a Decrease in Virulence.

Escherichia coli O25b:H4 sequence type 131 (ST131), which is resistant to fluoroquinolones and which is a producer of CTX-M-15, is globally one of the major extraintestinal pathogenic E. coli (ExPEC) lineages. Phylogenetic analyses showed that multidrug-resistant ST131 strains belong to clade C, which recently emerged from clade B by stepwise evolution. It has been hypothesized that features other than multidrug resistance could contribute to this dissemination since other major global ExPEC lineages (ST73 and ST95) are mostly antibiotic susceptible. To test this hypothesis, we compared early biofilm production, presence of ExPEC virulence factors (VFs), and in vivo virulence in a mouse sepsis model in 19 and 20 epidemiologically relevant strains of clades B and C, respectively. Clade B strains were significantly earlier biofilm producers (P < 0.001), carriers of more VFs (P = 4e-07), and faster killers of mice (P = 2e-10) than clade C strains. Gene inactivation experiments showed that the H30-fimB and ibeART genes were associated with in vivo virulence. Competition assays in sepsis, gut colonization, and urinary tract infection models between the most anciently diverged strain (B1 subclade), one C1 subclade strain, and a B4 subclade recombining strain harboring some clade C-specific genetic events showed that the B1 strain always outcompeted the C1 strain, whereas the B4 strain outcompeted the C1 strain, depending on the mouse niches. All these findings strongly suggest that clade C evolution includes a progressive loss of virulence involving multiple genes, possibly enhancing overall strain fitness by avoiding severe infections, even if it comes at the cost of a lower colonization ability.

Viral genetic variation accounts for a third of variability in HIV-1 set-point viral load in Europe.

HIV-1 set-point viral load-the approximately stable value of viraemia in the first years of chronic infection-is a strong predictor of clinical outcome and is highly variable across infected individuals. To better understand HIV-1 pathogenesis and the evolution of the viral population, we must quantify the heritability of set-point viral load, which is the fraction of variation in this phenotype attributable to viral genetic variation. However, current estimates of heritability vary widely, from 6% to 59%. Here we used a dataset of 2,028 seroconverters infected between 1985 and 2013 from 5 European countries (Belgium, Switzerland, France, the Netherlands and the United Kingdom) and estimated the heritability of set-point viral load at 31% (CI 15%-43%). Specifically, heritability was measured using models of character evolution describing how viral load evolves on the phylogeny of whole-genome viral sequences. In contrast to previous studies, (i) we measured viral loads using standardized assays on a sample collected in a strict time window of 6 to 24 months after infection, from which the viral genome was also sequenced; (ii) we compared 2 models of character evolution, the classical "Brownian motion" model and another model ("Ornstein-Uhlenbeck") that includes stabilising selection on viral load; (iii) we controlled for covariates, including age and sex, which may inflate estimates of heritability; and (iv) we developed a goodness of fit test based on the correlation of viral loads in cherries of the phylogenetic tree, showing that both models of character evolution fit the data well. An overall heritability of 31% (CI 15%-43%) is consistent with other studies based on regression of viral load in donor-recipient pairs. Thus, about a third of variation in HIV-1 virulence is attributable to viral genetic variation.

Correction: Viral genetic variation accounts for a third of variability in HIV-1 set-point viral load in Europe.

[This corrects the article DOI: 10.1371/journal.pbio.2001855.].

Evolution of antibiotic resistance is linked to any genetic mechanism affecting bacterial duration of carriage.

Understanding how changes in antibiotic consumption affect the prevalence of antibiotic resistance in bacterial pathogens is important for public health. In a number of bacterial species, including Streptococcus pneumoniae, the prevalence of resistance has remained relatively stable despite prolonged selection pressure from antibiotics. The evolutionary processes allowing the robust coexistence of antibiotic sensitive and resistant strains are not fully understood. While allelic diversity can be maintained at a locus by direct balancing selection, there is no evidence for such selection acting in the case of resistance. In this work, we propose a mechanism for maintaining coexistence at the resistance locus: linkage to a second locus that is under balancing selection and that modulates the fitness effect of resistance. We show that duration of carriage plays such a role, with long duration of carriage increasing the fitness advantage gained from resistance. We therefore predict that resistance will be more common in strains with a long duration of carriage and that mechanisms maintaining diversity in duration of carriage will also maintain diversity in antibiotic resistance. We test these predictions in S. pneumoniae and find that the duration of carriage of a serotype is indeed positively correlated with the prevalence of resistance in that serotype. These findings suggest heterogeneity in duration of carriage is a partial explanation for the coexistence of sensitive and resistant strains and that factors determining bacterial duration of carriage will also affect the prevalence of resistance.

Large Variations in HIV-1 Viral Load Explained by Shifting-Mosaic Metapopulation Dynamics.

The viral population of HIV-1, like many pathogens that cause systemic infection, is structured and differentiated within the body. The dynamics of cellular immune trafficking through the blood and within compartments of the body has also received wide attention. Despite these advances, mathematical models, which are widely used to interpret and predict viral and immune dynamics in infection, typically treat the infected host as a well-mixed homogeneous environment. Here, we present mathematical, analytical, and computational results that demonstrate that consideration of the spatial structure of the viral population within the host radically alters predictions of previous models. We study the dynamics of virus replication and cytotoxic T lymphocytes (CTLs) within a metapopulation of spatially segregated patches, representing T cell areas connected by circulating blood and lymph. The dynamics of the system depend critically on the interaction between CTLs and infected cells at the within-patch level. We show that for a wide range of parameters, the system admits an unexpected outcome called the shifting-mosaic steady state. In this state, the whole body's viral population is stable over time, but the equilibrium results from an underlying, highly dynamic process of local infection and clearance within T-cell centers. Notably, and in contrast to previous models, this new model can explain the large differences in set-point viral load (SPVL) observed between patients and their distribution, as well as the relatively low proportion of cells infected at any one time, and alters the predicted determinants of viral load variation.

What drives parallel evolution?: How population size and mutational variation contribute to repeated evolution.

Parallel evolution is the repeated evolution of the same phenotype or genotype in evolutionarily independent populations. Here, we use evolve-and-resequence experiments with bacteria and yeast to dissect the drivers of parallel evolution at the gene level. A meta-analysis shows that parallel evolution is often rare, but there is a positive relationship between population size and the probability of parallelism. We present a modeling approach to estimate the contributions of mutational and selective heterogeneity across a genome to parallel evolution. We show that, for two experiments, mutation contributes between ∼10 and 45%, respectively, of the variation associated with selection. Parallel evolution cannot, therefore, be interpreted as a phenomenon driven by selection alone; it must also incorporate information on heterogeneity in mutation rates along the genome. More broadly, the work discussed here helps lay the groundwork for a more sophisticated, empirically grounded theory of parallel evolution.

An evolutionary model to predict the frequency of antibiotic resistance under seasonal antibiotic use, and an application to Streptococcus pneumoniae.

The frequency of resistance to antibiotics in Streptococcus pneumoniae has been stable over recent decades. For example, penicillin non-susceptibility in Europe has fluctuated between 12% and 16% without any major time trend. In spite of long-term stability, resistance fluctuates over short time scales, presumably in part due to seasonal fluctuations in antibiotic prescriptions. Here, we develop a model that describes the evolution of antibiotic resistance under selection by multiple antibiotics prescribed at seasonally changing rates. This model was inspired by, and fitted to, published data on monthly antibiotics prescriptions and frequency of resistance in two communities in Israel over 5 years. Seasonal fluctuations in antibiotic usage translate into small fluctuations of the frequency of resistance around the average value. We describe these dynamics using a perturbation approach that encapsulates all ecological and evolutionary forces into a generic model, whose parameters quantify a force stabilizing the frequency of resistance around the equilibrium and the sensitivity of the population to antibiotic selection. Fitting the model to the data revealed a strong stabilizing force, typically two to five times stronger than direct selection due to antibiotics. The strong stabilizing force explains that resistance fluctuates in phase with usage, as antibiotic selection alone would result in resistance fluctuating behind usage with a lag of three months when antibiotic use is seasonal. While most antibiotics selected for increased resistance, intriguingly, cephalosporins selected for decreased resistance to penicillins and macrolides, an effect consistent in the two communities. One extra monthly prescription of cephalosporins per 1000 children decreased the frequency of penicillin-resistant strains by 1.7%. This model emerges under minimal assumptions, quantifies the forces acting on resistance and explains up to 43% of the temporal variation in resistance.

Evidence for parasite-mediated selection during short-lasting toxic algal blooms.

Parasites play a role in the control of transient algal blooms, but it is not known whether parasite-mediated selection results in coevolution of the host and the parasites over this short time span. We investigated the presence of coevolution between the toxic dinoflagellate Alexandrium minutum and two naturally occurring endoparasites during blooms lasting a month in two river estuaries, using cross-inoculation experiments across time and space. Higher parasite abundance was associated with a large daily reduction in relative A. minutum abundances, demonstrating strong parasite-mediated selection. There was genetic variability in infectivity in both parasite species, and in resistance in the host. We found no evidence for coevolution in one estuary; however, in the other estuary, we found high genetic diversity in the two parasite species, fluctuations in infectivity and suggestion that the two parasites are well adapted to their host, as in 'Red Queen' dynamics. Thus, coevolution is possible over the short time span of a bloom, but geographically variable, and may feedback on community dynamics.