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bioRxiv2026

Global genomic epidemiology of multi-carbapenemase Enterobacterales

Enterobacterales pathogens that encode multiple carbapenemase genes are an emerging public health threat. The genomic and plasmid context of the multi-carbapenemase producers has not yet been explored at scale. We analysed 1501 publicly-available global genomes that encoded two or more genes from the five prevalent carbapenemase families (IMP, KPC, NDM, OXA-48-like, and VIM). We observed 241 distinct allelic combinations, with NDM/OXA-48-like (25.6%), multiple KPC (25.3%), KPC/NDM (18.8%), and multiple NDM (10.9%) being the most common groups. We found that many genomes were part of high-risk lineages (48.0%). Most genomes carried carbapenemase genes on two plasmids (59.2%) or one plasmid (23.5%), and most genomes encoded different carbapenemase families (56.2%) rather than copies of the same allele (36.2%). We observed multiple mechanisms of multi-carbapenemase evolution including plasmid co-integration, convergence with virulence, and generation of novel allele diversity through duplication. Multi-carbapenemase plasmids had more AMR genes that conferred resistance to more AMR classes compared to plasmids encoding a single carbapenemase. The proportion of cross-class combinations (KPC/NDM and NDM/OXA-48-like) increased significantly over time, with NDM-5/OXA-181 (3.3-fold) and NDM-4/OXA-181 (8.9-fold) combinations enriched. These findings provide valuable insights into the genomic features of multi-carbapenemase Enterobacterales to support the development of effective surveillance programmes.

microbiology↗
bioRxiv2021

Phylodynamic inference for emerging viruses using segregating sites

Epidemiological models are commonly fit to case data to estimate model parameters and to infer unobserved disease dynamics. Epidemiological models have also been fit to viral sequence data using phylodynamic inference approaches that rely on the reconstruction of viral phylogenies. However, especially early on in an expanding viral population, phylogenetic uncertainty can be substantial and methods that require integration over this uncertainty can be computationally intensive. Moreover, these approaches require estimation of parameters associated with models of sequence evolution in addition to the estimation of epidemiological parameters that are of primary interest. Here, we present an alternative approach to phylodynamic inference that can be used early on during the expansion of a viral lineage that circumvents the need for phylogenetic tree reconstruction. Our "tree-free" approach instead relies on quantifying the number of segregating sites observed in sets of sequences over time and using this trajectory of segregating sites to infer epidemiological parameters within a Sequential Monte Carlo (SMC) framework. Using forward simulations, we first show that epidemiological parameters and processes leave characteristic signatures in segregating site trajectories, demonstrating that these trajectories have the potential to be used for interfacing epidemiological models with sequence data. We then show that our proposed approach accurately recovers key epidemiological quantities such as the basic reproduction number and the timing of the index case from mock data simulated under a single-introduction scenario. Finally, we apply our approach to SARS-CoV-2 sequence data from France, estimating a reproductive number of approximately 2.5 to 2.7 under an epidemiological model structure that allows for multiple introductions, consistent with estimates from epidemiological surveillance data. Our findings indicate that "tree-free" statistical inference approaches that rely on simple population genetic summary statistics can be informative of epidemiological parameters and can be used for reconstructing infectious disease dynamics early on in an epidemic or during the expansion of a viral lineage.

evolutionary biology↗
bioRxiv2018

A Mathematical Model Relating Pitocin Use During Labor with Offspring Autism Development in Terms of Oxytocin Receptor Desensitization in the Fetal Brain

This paper develops a mathematical model describing the potential buildup of high oxytocin concentrations in the maternal circulation during labor in terms of continuous Pitocin infusion rate, half-life and maternal weight. Oxytocin override of the degradation of oxytocin by placental oxytocinase is introduced to model the potential transfer of oxytocin from the maternal circulation across the placenta into the fetal circulation, and from there into the brain of the fetus. The desensitization unit D equal to 1.8E6 (pg-min)/ml is employed to establish a desensitization threshold, and by extension; a down-regulation threshold as a function of oxytocin override concentration and continuous Pitocin infusion time, that could be a factor in the subsequent development of autism among offspring. Epidemiological studies by Duke University (S.G. Gregory, 2013), Yale University (O. Weisman, 2015) and Harvard University (A.S. Oberg, 2016) are discussed regarding Pitocin use and offspring autism development for an explanation of the weak correlations they identified. The findings of the Harvard epidemiological study are re-interpreted regarding Pitocin use, and its conclusion questioned. Further evaluations of the findings of these three epidemiological studies are called for to incorporate medical information on quantity of Pitocin used, continuous Pitocin infusion rate, length of labor and maternal weight to determine if a correlation can be established with offspring autism development above an empirically determined desensitization threshold for Pitocin use. Suggestions for research are discussed, including an alternative to continuous Pitocin infusion, pulsatile infusion of Pitocin during labor induction, that may mitigate possible offspring autism development.\n\nHIGHLIGHTSO_LIBuildup of oxytocin (OT) in the maternal circulation mathematically modeled.\nC_LIO_LIRelationship of OT half-life and OT concentration in maternal circulation identified.\nC_LIO_LIOTR Desensitization related to Pitocin infusion time and OT override concentration.\nC_LIO_LIWeak correlations for autism development in epidemiological studies explained.\nC_LIO_LIExamination called for of medical records of Pitocin use in epidemiological studies.\nC_LI\n\nOUTLINE OF PAPERIntroduction\n\nO_LIDevelopment and application of mathematical model - Sections 1 through 6, notably: Figures 1 through 4\nC_LIO_LIDiscussion of oxytocin receptor desensitization as it pertains to mathematical model - Sections 7 and 8\nC_LIO_LIInfluence of mathematical model on interpretation of epidemiological studies - Sections 9 and 10\nC_LIO_LIResearch considerations - Section 11, notably:Subsection 11.1 - Call for detailed epidemiological analysis of selected medical information indicated by mathematical model Subsection 11.2 - Review of pulsatile Pitocin infusion to mitigate possible offspring autism development indicated by mathematical model\nC_LI\n\nO_FIG O_LINKSMALLFIG WIDTH=145 HEIGHT=200 SRC=\"FIGDIR/small/446997_fig1.gif\" ALT=\"Figure 1\">\nView larger version (62K):\norg.highwire.dtl.DTLVardef@1704563org.highwire.dtl.DTLVardef@1db729aorg.highwire.dtl.DTLVardef@41035eorg.highwire.dtl.DTLVardef@442b1f_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Percent Oxytocin Removal by Liver and Kidneys and Percent Oxytocin Remaining in Blood with One-Minute Circulation Time as a Function of Oxytocin Half-Life in the Blood.\n\nC_FIG

neuroscience↗
bioRxiv2017

Death of the assumption that "latent period" is fixed over the course of a plant disease epidemic

The latent period is a crucial life history trait, particularly for polycyclic plant diseases, because it determines how many complete monocycles could theoretically occur during an epidemic. Many empirical studies have focused on the variation of the latent period with pathogen or host genotype, or changes in response to environmental factors. The focus on these aspects is unsurprising, as these factors classically form the three parts of the epidemiological triangle. Experiments in controlled conditions are generally used to assess pathogenicity and host susceptibility, and also provide the opportunity to measure the distribution of latent periods in epidemiological systems. Once estimated for one or several pairs of host-pathogen genotypes, the mean value of this important trait is usually considered to be fixed and is often used \"as is\" in epidemiological models. We show here that the latent period can display non-negligible variability over the course of a disease epidemic, and that this variability has multiple sources, some of which have complex, antagonistic impacts. We develop arguments for four sources of variation that challenge the implicit assumption that the latent period remains constant: daily fluctuations in leaf temperature, nature of inoculum, host stage or age of host tissues, intra-population competition and selection for aggressiveness traits. We focus on the wheat fungal disease Septoria tritici blotch (Zymoseptoria tritici), making use of empirical datasets collected during the first authors own research projects and a targeted literature review. Such empirical epidemiological knowledge is new and potentially important for modelers. While some studies have demonstrated that the distribution of latent periods around the mean value has consequences for epidemiological dynamics, we show that it might also be important for epidemiological modelers to account for changes in this mean value during an annual epidemic. These results may be of critical importance for improving outbreak forecasting.

epidemiology↗
bioRxiv2017

Tuberculosis outbreak investigation using phylodynamic analysis

The fast evolution of pathogenic viruses has allowed for the development of phylodynamic approaches that extract information about the epidemiological characteristics of viral genomes. Thanks to advances in whole genome sequencing, they can be applied to slowly evolving bacterial pathogens like Mycobacterium tuberculosis.\n\nIn this study, we investigate the epidemiological dynamics underlying two M. tuberculosis outbreaks using phylodynamic methods. The first outbreak occurred in the Swiss city of Bern (1993-2012) and was caused by a drug-susceptible strain belonging to the phylogenetic M. tuberculosis Lineage 4. The second outbreak was caused by a multidrug-resistant (MDR) strain of Lineage 2, imported from the Wat Tham Krabok (WTK) refugee camp in Thailand into California.\n\nThere is little temporal signal in the Bern data set and moderate temporal signal in the WTK data set. We estimate an evolutionary rate of 0.0039 per single nucleotide polymorphism (SNP) per year for Bern and 0.0024 per SNP per year for WTK. Nevertheless, due to its high sampling proportion (90%) the Bern outbreak allows robust estimation of epidemiological parameters despite the poor temporal signal. Conversely, theres much uncertainty in the epidemiological estimates concerning the WTK outbreak, which has a small sampling proportion (9%). Our results suggest that both outbreaks peaked around 1990, although the Bernese outbreak was only detected in 1993, and the WTK outbreak around 2004. Furthermore, individuals were infected for a significantly longer period (around 9 years) in the WTK outbreak than in the Bern outbreak (4-5 years).\n\nOur work highlights both the limitations and opportunities of phylodynamic analysis of outbreaks involving slowly evolving pathogens: (i) estimation of the evolutionary rate is difficult on outbreak time scales and (ii) a high sampling proportion allows quantification of the age of the outbreak based on the sampling times, and thus allows for robust estimation of epidemiological parameters.

epidemiology↗
bioRxiv2017

Paternal-age-related de novo mutations and risk for five disorders

BackgroundThere are well-established epidemiologic associations between advanced paternal age and increased offspring risk for several psychiatric and developmental disorders. These associations are commonly attributed to age-related de novo mutations. However, the actual magnitude of risk conferred by age-related de novo mutations in the male germline is unknown. Quantifying this risk would clarify the clinical and public health significance of delayed paternity.\n\nMethodsUsing results from large, parent-child trio whole-exome-sequencing studies, we estimated the relationship between paternal-age-related de novo single nucleotide variants (dnSNVs) and offspring risk for five disorders: autism spectrum disorders (ASD), congenital heart disease (CHD), neurodevelopmental disorders with epilepsy (EPI), intellectual disability (ID), and schizophrenia (SCZ). Using Danish national registry data, we then investigated the degree to which the epidemiologic association between each disorder and advanced paternal age was consistent with the estimated role of de novo mutations.\n\nResultsIncidence rate ratios comparing dnSNV-based risk to offspring of 45 versus 25-year-old fathers ranged from 1.05 (95% confidence interval 1.01-1.13) for SCZ to 1.29 (95% CI 1.13-1.68) for ID. Epidemiologic estimates of paternal age risk for CHD, ID and EPI were consistent with the dnSNV effect. However, epidemiologic effects for ASDs and SCZ significantly exceeded the risk that could be explained by dnSNVs alone (p<2e-4 for both comparisons).\n\nConclusionIncreasing dnSNVs due to advanced paternal age confer a small amount of offspring risk for psychiatric and developmental disorders. For ASD and SCZ, epidemiologic associations with delayed paternity largely reflect factors that cannot be assumed to increase with age.

genetics↗
bioRxiv2021

Entangling snakebite dynamics: the spatiotemporal role of rainfall on snake envenoming in Colombia

The role of climate forcing on the population dynamics of infectious diseases has typically been addressed via retrospective analyses of aggregated incidence records over whole political regions. A central question in epidemiology has been whether seasonal and interannual cycles are driven by climate variation, or instead generated by other factors such as poverty or underreporting. Here, we use process-based models to determine the role of rainfall in the dynamics of snakebite, which is one of the main neglected tropical diseases around the world. We combined space-time datasets of snakebite incidence and rainfall for Colombia in combination with stochastic epidemiological models and iterated filtering methods to show the response to rainfall forcing, specifically, modulating the encounter frequency with venomous snakes. We identified six zones with different rainfall patterns to demonstrate that the relationship between rainfall and snakebite incidence was heterogeneous. Rainfall only drives snakebite incidence in regions with marked dry seasons, where rainfall becomes the limiting resource. In addition, the encounter frequency also differs between regions, and it is higher in regions where Bothrops atrox can be found. Our results show how the heterogeneous spatial distribution of snakebite risk seasonality in the country may be related to important traits of venomous snakes natural history. SIGNIFICANCE STATEMENTThe association between seasonal climatic variables and diseases epidemiology has helped to understand disease burden under changing environments. For example, for several tropical zoonotic diseases rainfall has been identified as a critical covariate inducing incidence seasonality. Snakebite envenoming is a disease that affects mostly economically deprived populations, and the availability of treatment is scarce. However, the role of environmental factors on snakebite is still missing in the literature. We formulated an epidemiological model to quantify the role of rainfall on snakebite seasonality throughout Colombia. We found that rainfall has a significant effect on incidence in places with a marked dry season (Caribbean and Orinoco plains), but in areas without dry season (Amazonian basin and southwestern Colombia) incidence exhibits no seasonality. This study is the first epidemiological modeling approach to snakebite and underscores the importance of rainfall as the limiting resource in this system. Thus, it is important to consider the interaction between climate forcing and venomous snakes ecology as determinants of envenomation risk.

ecology↗
bioRxiv2022

Tracking the emergence and dissemination of a blaNDM-23 Gene in a Multi-Drug Resistance Plasmid of Klebsiella pneumoniae

ObjectivesSince the discovery of blaNDM-1, NDM beta-lactamases have become one of the most widespread carbapenemases worldwide. To date, 28 different NDM variants have been reported but some, such as blaNDM-23, have not been characterized in detail yet. Here, we describe the emergence of a novel blaNDM-23 allele from a blaNDM-1 ancestor and the multidrug resistant plasmid that has disseminated it through a K. pneumoniae ST407 clone in several Spanish hospitals. MethodsBetween 2016 and 2019, 1,972 isolates were collected in an epidemiological survey for ESBL-producing Klebsiella pneumoniae in the Comunitat Valenciana (Spain). Three carbapenem resistant strains failed to be detected by CPE screening tests. These isolates carried a blaNDM-23 gene. To characterize this gene, its emergence, and dissemination, we performed antimicrobial susceptibility tests, hybrid sequencing with Illumina and Nanopore technologies, and phylogenetic analyses. ResultsThe MICs of the blaNDM-23 variant were identical to those of the blaNDM-1. The blaNDM-23 variant was found in 14 isolates in a 97 Kb non-mobilizable, multidrug-resistant plasmid carrying 19 resistance genes for 9 different antimicrobial families. In this plasmid, the blaNDM-23 gene is located in the variable region of a complex class-1 integron with a singular genetic environment. The short genetic distance between blaNDM-23-producing isolates reflects a 5-year-long clonal dispersion involving several hospitals and interregional spread. ConclusionsWe have characterized the genomic and epidemiological contexts in the emergence and community spread of a new blaNDM-23 allele in an MDR-plasmid of Klebsiella pneumoniae. TweetGenomic, epidemiologic and phylogenetic analyses of the emergence of a new NDM allele provide information on the rapid changes underlying the spread of antimicrobial resistance genes and strains in Klebsiella pneumoniae. ImportanceAt a time when antimicrobial resistance has become one of the biggest concerns worldwide, the emergence of novel alleles and extremely drug-resistant plasmids are a threat to public health worldwide. More so when they produce carbapenem resistance in one of the most problematic pathogens in clinical settings, such as Klebsiella pneumoniae. Here, we have used genomic epidemiology to describe the emergence of a novel NDM-23 allele and identify it in a MDR plasmid that has been disseminated through a K. pneumoniae ST407 clone in several hospitals in a Spanish region. By means of bioinformatic and phylogenetic analyses, we have been able to trace the evolutionary and epidemiological route of the new allele, the hosting plasmid, and the strain that carried both of them from Pakistan to Spain. A better understanding of the NDM-producing K. pneumoniae populations and its plasmids has made evident the spread of this clone through the region, enhancing the importance of genomic surveillance in the control of antimicrobial resistance.

microbiology↗
bioRxiv2023

Outbreak reconstruction with a slowly evolving multi-host pathogen: a comparative study of three existing methods on Mycobacterium bovis outbreaks

In a multi-host system, understanding host-species contribution to transmission is key to appropriately targeting control and preventive measures. Outbreak reconstruction methods aiming to identify who-infected-whom by combining epidemiological and genetic data could contribute to achieving this goal. However, the majority of these methods remain untested on realistic simulated multi-host data. Mycobacterium bovis is a slowly evolving multi-host pathogen and previous studies on outbreaks involving both cattle and wildlife have identified observation biases. Indeed, contrary to cattle, sampling wildlife is difficult. The aim of our study was to evaluate and compare the performances of three existing outbreak reconstruction methods (seqTrack, outbreaker2 and TransPhylo) on M. bovis multi-host data simulated with and without biases. Extending an existing transmission model, we simulated 30 bTB outbreaks involving cattle, badgers and wild boars and defined six sampling schemes mimicking observation biases. We estimated general and specific to multi-host systems epidemiological indicators. We tested four alternative transmission scenarios changing the mutation rate or the composition of the epidemiological system. The reconstruction of who-infected-whom was sensitive to the mutation rate and seqTrack reconstructed prolific super-spreaders. TransPhylo and outbreaker2 poorly estimated the contribution of each host-species and could not reconstruct the presence of a dead-end epidemiological host. However, the host-species of cattle (but not badger) index cases was correctly reconstructed by seqTrack and outbreaker2. These two specific indicators improved when considering an observation bias. We found an overall poor performance for the three methods on simulated biased and unbiased bTB data. This seemed partly attributable to the low evolutionary rate characteristic of M. bovis leading to insufficient genetic information, but also to the complexity of the simulated multi-host system. This study highlights the importance of an integrated approach and the need to develop new outbreak reconstruction methods adapted to complex epidemiological systems and tested on realistic multi-host data. Author summarySome pathogens like the one responsible for bovine tuberculosis can infect multiple species. Identifying which species transmitted and to which other species in such an outbreak presents a unique challenge, especially when difficult to observe wildlife species are concerned. One way to tackle this issue would be to reconstruct who-infected-whom in an outbreak and then identify the role each species played. However, methods that enable this type of reconstruction have not been tested in the context of transmission between unevenly observed species. Moreover, the pathogen responsible for bovine tuberculosis evolves slowly, which further complicates the reconstruction of who-infected-whom. We thus simulated realistic and complex bovine tuberculosis outbreaks on which we tested three widely used methods. We found poor performances for all three tested methods, which highlights the need to develop new methods adapted to outbreaks involving multiple species. Our results also underline the need to combine multiple types of methods and data sources in addition to the reconstruction of who-infected-whom, such as the reconstruction of phylogenetic trees or identifying possible infectious contacts through investigations, when studying an outbreak.

evolutionary biology↗
bioRxiv2023

Widespread dissemination of ESBL-producing Salmonella enterica serovar Infantis exhibiting intermediate fluoroquinolone resistance and harboring blaCTX-M-65-positive pESI-like megaplasmids in Chile

BackgroundMultidrug-resistant (MDR) Salmonella Infantis has disseminated worldwide, mainly linked to the consumption of poultry products. Evidence shows dissemination of this pathogen in Chile; however, studies are primarily limited to phenotypic data or involve few isolates. As human cases of Salmonella Infantis infections have substantially increased in recent years, a better understanding of its molecular epidemiology and antimicrobial-resistance profiles are required to inform effective surveillance and control measures. MethodsWe sequenced 396 Salmonella Infantis genomes and analyzed them with all publicly available genomes of this pathogen from Chile (440 genomes in total), representing isolates from environmental, food, animal, and human sources obtained from 2009 to 2022. Based on bioinformatic and phenotypic methods, we assessed the population structure, dissemination among different niches, and AMR profiles of Salmonella Infantis in the country. FindingsThe genomic and phylogenetic analyses showed that Salmonella Infantis from Chile comprised several clusters of highly related isolates dominated by sequence type 32. The HC20_343 cluster grouped an important proportion of all isolates. The latter was the only cluster associated with pESI-like megaplasmids, and up to 12 acquired AMR genes/mutations predicted to result in an MDR phenotype. Accordingly, antimicrobial-susceptibility testing revealed a strong concordance between the AMR genetic determinants and their matching phenotypic expression, indicating that a significant proportion of HC20_343 isolates produce extended- spectrum {beta}-lactamases and have intermediate fluoroquinolone resistance. HC20_343 Salmonella Infantis were spread among environmental, animal, food, and human niches, showing a close relationship between isolates from different years and sources, and a low intra-source genomic diversity. InterpretationOur findings show a widespread dissemination of MDR Salmonella Infantis from the HC20_343 cluster in Chile. The high proportion of isolates with resistance to first-line antibiotics and the evidence of active transmission between the environment, animals, food, and humans highlight the urgency of improved surveillance and control measures in the country. As HC20_343 isolates predominate in the Americas, our results suggest a high prevalence of ESBL- producing Salmonella Infantis with intermediate fluoroquinolone resistance in the continent. FundingAgencia de Investigacion y Desarrollo de Chile (ANID) through FONDECYT de Postdoctorado Folio 3230796 and Folio 3210317, FONDECYT Regular Folio 1231082, and ANID - Millennium Science Initiative Program - ICN2021_044. Research in contextO_ST_ABSEvidence before the studyC_ST_ABSIn the last decade, emergent multidrug-resistant Salmonella Infantis has spread worldwide, primarily linked to poultry product consumption. However, in most countries from the Americas Region, such as Chile, the extent of the dissemination of emergent Salmonella Infantis and its molecular epidemiology remains unknown. In May and September 2023, an online search was conducted using the Google engine and the PMC database with the terms "Salmonella," "Infantis," and "Chile," with no language restrictions. We assessed the results to select those presenting antimicrobial resistance, epidemiologic, or genomic data directly associated with isolates from Chile (13 studies). The selected studies showed that the prevalence of Salmonella Infantis in poultry-meat production systems, its resistance to different antibiotics, and the number of human cases of infection caused by this serovar have increased since 2014-2016. However, these reports were limited to phenotypic data or involved the genomic analysis of a few isolates (<50) obtained from the same source. No study has assessed the genomic epidemiology of the Salmonella Infantis population at the country level. Added value of this studyHere, we present the first large-scale genomic epidemiology analysis of Salmonella Infantis in Chile, including isolates from environmental, food, animal, and human sources obtained from 2009 to 2022. We found that Salmonella Infantis in Chile is divided into several clusters of highly related isolates and that only a single cluster, the HC20_343, was associated with multiple antimicrobial-resistance determinants and pESI-like megaplasmids. We also report that isolates from this cluster are widespread among most sources, including irrigation water, poultry, food, and human cases. Detection of AMR determinants coupled with antimicrobial- susceptibility testing indicated that most HC20_343 isolates are ESBL-producers and have intermediate resistance to ciprofloxacin. Population structure analysis of this foodborne pathogen evidenced an active transmission of MDR Salmonella Infantis between different niches. This study reveals the widespread dissemination of MDR Salmonella Infantis in Chile. Implications of all the available evidenceThe evidence indicates that emerging Salmonella Infantis from the HC20_343 cluster is spreading among various niches, including irrigation water, poultry, and food, causing human infections in Chile. Its resistance to first-line antibiotics used for treating salmonellosis in individuals with a higher risk of severe or invasive infections is concerning. Currently, most surveillance and control efforts to reduce salmonellosis in Chile are focused on the poultry industry, and the study of outbreaks does not include whole-genome sequence analyses. Our findings highlight the urgent necessity to improve the surveillance and control measures to include agricultural waters to prevent contamination of produce and the further dissemination of resistance genes in the environment. As the HC20_343 cluster is highly prevalent in the Americas, further research involving large-scale genomic population analyses would shed light on the extent of the dissemination and transmission routes of emergent Salmonella Infantis in the continent and may contribute to informing surveillance and control policies.

genomics↗
bioRxiv2024

Evolution of Virulence in Emerging Epidemics: From Theory to Experimental Evolution and Back

The experimental validation of theoretical predictions is a crucial step in demonstrating the predictive power of a model. While quantitative validations are common in infectious diseases epidemiology, experimental microbiology primarily focuses on the evaluation of a qualitative match between model predictions and experiments. In this study, we develop a method to deepen the quantitative validation process with a polymorphic viral population. We analyse the data from an experiment carried out to monitor the evolution of the temperate bacteriophage {lambda} spreading in continuous cultures of Escherichia coli. This experimental work confirmed the influence of the epidemiological dynamics on the evolution of transmission and virulence of the virus. A variant with larger propensity to lyse bacterial cells was favoured in emerging epidemics (when the density of susceptible cells was large), but counter-selected when most cells were infected. Although this approach qualitatively validated an important theoretical prediction, no attempt was made to fit the model to the data nor to further develop the model to improve the goodness of fit. Here, we show how theoretical analysis - including calculations of the selection gradients - and model fitting can be used to estimate key parameters of the phage life cycle and yield new insights on the evolutionary epidemiology of the phage {lambda}. First, we show that modelling explicitly the infected bacterial cells which will eventually be lysed improves the fit of the transient dynamics of the model to the data. Second, we carry out a theoretical analysis that yields useful approximations that capture at the onset and at the end of an epidemic the effects of epidemiological dynamics on selection and differentiation across distinct life stages of the virus. Finally, we estimate key phenotypic traits characterizing the two strains of the virus used in our experiment such as the rates of prophage reactivation or the probabilities of lysogenization. This study illustrates the synergy between experimental, theoretical and statistical approaches; and especially how interpreting the temporal variation in the selection gradient and the differentiation across distinct life stages of a novel variant is a powerful tool to elucidate the evolutionary epidemiology of emerging infectious diseases.

evolutionary biology↗
bioRxiv2024

Torque Teno Sus Virus 1: A Potential Surrogate Pathogen to Study Pig-Transmitted Transboundary Animal Diseases

Understanding the epidemiology and transmission dynamics of transboundary animal diseases (TADs) among wild pigs (Sus scrofa) will aid in preventing the introduction or containment of TADs among wild populations. Given challenges associated with studying TADs in free-ranging populations, a surrogate pathogen system for in situ studies of pathogen dynamics would be ideal to elucidate how pathogens may circulate and be maintained within populations, how they may spillover into domestic populations, and how disease spread may be mitigated. We assessed the suitability of Torque teno sus virus 1 (TTSuV1) to serve as a surrogate pathogen for molecular epidemiological studies in wild pigs by investigating the prevalence, persistence, correlation with host health status and genetic variability at two study areas: Archbolds Buck Island Ranch in Florida and Savannah River Site in South Carolina. We then conducted a molecular epidemiological case study within the Archbolds Buck Island Ranch site to determine how analysis of this pathogen could inform transmission studies. Prevalence was high at both study areas (40%, n = 190). Phylogenetic analyses revealed high levels of genetic variability within and between study sites. Our case study showed that pairwise host relatedness and geographic distance were highly correlated to pairwise viral genetic similarity. Molecular epidemiological analyses revealed a distinct pattern of direct transmission from pig to pig occurring within and between family groups. Our results suggest TTSuV1 is highly suitable for molecular epidemiological analyses and will be useful for future in situ studies of transmission dynamics in wild pigs.

ecology↗
bioRxiv2024

Genomic Characterisation of Group B Streptococcus from Argentina: Insights into Prophage Diversity, Virulence Factors and Antibiotic Resistance Genes

Group B Streptococcus (GBS) is a commensal bacterium that can cause severe infections in infants and adults with comorbidities. Resistance and reduced susceptibility to antibiotics is continually on the rise, and vaccines remain in-development. Prophages have been reported to contribute to GBS evolution and pathogenicity. However, no studies are available to date on prophage contribution to the epidemiology of GBS isolates from humans in South America. In the context of an Argentinian multicentric study, we had previously phenotypically characterised 365 human GBS isolates from invasive disease, urinary infections and maternal colonisation. These isolates had been whole genome sequenced and their prophage presence bioinformatically determined. In this study, we genomically characterised the isolates and analysed the prophage content in the context of the epidemiological data. The phylogenetic analysis of the 365 genomes with 103 GBS from public databases revealed that Argentinian GBS were related to isolates from around the world. The most prevalent lineages, independently of the isolate source, were CC23/Ia and CC12/Ib. Genes encoding virulence factors involved in immune response evasion, tissue damage and adherence to host tissues and invasion were found in all of the genomes in accordance with previously described lineage distribution. According to the prevalent capsular types and the distribution of specific virulence factors in Argentinian GBS, over 95% coverage would be expected from the vaccines currently under development. Antibiotic resistance determinants (ARDs) to at least one antibiotic class were found in 90% of the genomes, including novel mutations in pbp2x, while more than 15% carried ARDs to 3 or more classes. GBS collected from urinary infections carried a significantly higher proportion of ARDs to multiple antibiotic classes than the rest of the isolates. A total of 454 prophages were found among the 468 genomes analysed, which were classified into 23 prophage types. Prophage presence exhibited variations based on GBS clonal complex and capsular type. A possible association between an increased GBS pathogenicity and the carriage of prophages with integrase type GBSInt8 and/or the presence of genes that encode the Phox Homology domain has been observed. The highest prevalence of prophages per genome was found in lineages CC17/III and CC19/III, while the lowest amount was observed in CC12/Ib. Overall, the highest density of prophages, virulence factors and ARDs determinants was found in CC19 isolates, mostly of capsular type III, independently of the isolates source. This is the first analysis of the human-associated GBS population in South America based on whole genome sequencing data, which will make a significant contribution to future studies on the global GBS population structure. Data summarySupplementary results and figures can be found in Supplementary Material 1. Supplementary tables can be found in Supplementary Material 2. All datasets analysed in this study are detailed in the Supplementary Materials. Metadata about the genomes analysed can be found in the microreact project created for this study: https://microreact.org/project/gbs-pangenomic-analysis. Impact statementIn Latin America studies on the epidemiology of Group B Streptococcus (GBS) are scarce, especially those describing clonal complex and serotype distribution, and the role of prophages in GBS epidemiology has not been studied. This article addresses the first genomic characterisation of the human-isolated GBS population in Latin America based on whole genome sequencing data, with special focus on the analysis of prophage content. We determine the clonal complex and serotype distribution of 365 GBS isolates collected from clinical samples in an Argentinian multicenter study and analyse the presence of prophages and virulence and antibiotic resistance determinants in the context of the epidemiological data. Through these analyses, we were able to determine how GBS population structure in Argentina differs from other parts of the world and to predict the potential coverage of the in-development GBS vaccines. We also found a possible association between the carriage of certain types of prophages and an increased GBS pathogenicity. In the context of increased global efforts to develop new strategies to prevent GBS infections through vaccine development, this study makes a significant contribution to our understanding of the global GBS population structure.

bioinformatics↗
bioRxiv2025

Untangling the effects of within-host and between-host natural selection in viruses with applications to evolution of HIV-1 co-receptor usage

The fitness of a pathogen can be characterised at multiple levels corresponding to different stages of its life cycle, including replication within target cells (replicative capacity), immune evasion, and the capacity to infect new hosts (transmissibility). Pathogens evolve towards greater fitness within hosts, which can potentially compromise transmissibility. Typically, epidemiological studies for measuring pathogen fitness compute a single selection coefficient for pathogen variants that encompasses all stages of the pathogen life cycle. To address this problem, we develop a population-genetic model and software for the inference of multi-level selection coefficients which represent relative within-host fitness and transmissibility. Our approach builds on population genetic models previously applied to macro-evolution, whereby speciation and extinction rates are state-dependent. We relate the state-dependence in speciation and extinction rates to competing within- and between-host selection. Evolution of beneficial phenotypes within hosts may occur rapidly (higher speciation), but reduce rates of dispersal between hosts (higher extinction). This new coalescent-based method also accounts for highly nonlinear-population dynamics and variable sampling through time making it suitable for epidemiological studies. We derive the equilibrium proportion of a variant with multi-level selection under mutation-selection balance and show how this can be used in combination with the coalescent to greatly increase accuracy and precision of estimated selection coefficients. We characterise the accuracy of this method using a variety of birth-death and coalescent simulations before analysing a real dataset of HIV-1 genetic sequences from a large population-based sample of 4056 people living with HIV in Zambia. We quantified the multi-level relative fitness underlying HIV-1 co-receptor switching. In accordance with previous observations, we estimate that the relative transmissibility of X4-tropic virus is 9%, while within-host switching to X4-tropism is approximately seven-fold higher than switching to R5-tropism, indicating a strong within-host and between-host tradeoff of co-receptor usage. Our methods are available in an open-source R package musseco, providing a general-purpose tool for inferring multi-level selection coefficients for epidemiological sequence data. Author SummaryThe fitness of a pathogen can be characterised at each stage of its life cycle. However, epidemiological studies measuring pathogen fitness typically estimate a single value encompassing the entire life cycle. Pathogens evolve towards greater fitness within hosts, but this can compromise the capacity to infect new hosts. Our new method quantifies relative fitness at both levels. This method accounts for rapidly changing pathogen populations and variable sampling through time making it suitable for epidemiological studies. We tested our method using simulations and demonstrate its application by analysing real HIV-1 genetic sequences from people living with HIV in Zambia. This method sheds new light on long-standing questions about the relative fitness of HIV-1 variants which utilise different host-cell co-receptors. Using our new method, we estimate that the capacity of X4 variants to infect new people is just 9% of that of R5 variants, while within-host switching from R5 to X4 usage is seven times greater than the reverse switch. This indicates a strong within-host and between-host trade-off in co-receptor usage.

evolutionary biology↗
bioRxiv2025

denim: an R package for deterministic compartmental models with flexible dwell time distributions

O_LICompartmental models are widely used for dynamical systems where states are discrete such as in infectious diseases epidemiology with the so-called SIR or Susceptible-Infectious-Recovered framework. For mathematical simplicity, rates of transition between compartments are generally assumed to be independent of the dwell time (or secondary time scale in survival analysis): they are either constant or dependent on the epidemiological time (or primary time scale in survival analysis) only, either directly (e.g. environmental or behavioral forcings in epidemiological models) or indirectly through dependence on other variables of the system (e.g. the force of infection in epidemiological models). In some domains of application, this memoryless assumption leads to distributions of dwelling times that are incompatible with those observed on data which can lead to serious problems since the model predictions are highly sensitive on the exact shape of these distributions. C_LIO_LIHere we propose a deterministic, continuous-variable, numerical modelling approach that allows full flexibility on the dwell time distributions. The accompanying denim package provides a user-friendly interface to implement our proposed method through a dedicated language for model definition. C_LIO_LIThe package is open-source and available on CRAN. With more detailed data on the clinical process of infections becoming available, the denim package will be extremely useful for building more realistic epidemiological models providing more accurate projections. C_LI The denim package is publicly available for download on CRAN (https://cran.r-project.org/web/packages/denim/index.html) and GitHub (https://github.com/thinhong/denim). Additional documentation can be found on the denim webpage (https://drthinhong.com/denim/). Bug reports can be submitted via Github Issue.

bioinformatics↗
bioRxiv2017

Bayesian Reconstruction of Transmission within Outbreaks using Genomic Variants

Pathogen genome sequencing can reveal details of transmission histories and is a powerful tool in the fight against infectious disease. In particular, within-host pathogen genomic variants identified through heterozygous nucleotide base calls are a potential source of information to identify linked cases and infer direction and time of transmission. However, using such data effectively to model disease transmission presents a number of challenges, including differentiating genuine variants from those observed due to sequencing error, as well as the specification of a realistic model for within-host pathogen population dynamics.\n\nHere we propose a new Bayesian approach to transmission inference, BadTrIP (BAyesian epiDemiological TRansmission Inference from Polymorphisms), that explicitly models evolution of pathogen populations in an outbreak, transmission (including transmission bottlenecks), and sequencing error. BadTrIP enables the inference of host-to-host transmission from pathogen sequencing data and epidemiological data. By assuming that genomic variants are unlinked, our method does not require the computationally intensive and unreliable reconstruction of individual haplotypes. Using simulations we show that BadTrIP is robust in most scenarios and can accurately infer transmission events by efficiently combining information from genetic and epidemiological sources; thanks to its realistic model of pathogen evolution and the inclusion of epidemiological data, BadTrIP is also more accurate than existing approaches. BadTrIP is distributed as an open source package (https://bitbucket.org/nicofmay/badtrip) for the phylogenetic software BEAST2.\n\nWe apply our method to reconstruct transmission history at the early stages of the 2014 Ebola outbreak, showcasing the power of within-host genomic variants to reconstruct transmission events.\n\nAuthor SummaryWe present a new tool to reconstruct transmission events within outbreaks. Our approach makes use of pathogen genetic information, notably genetic variants at low frequency within host that are usually discarded, and combines it with epidemiological information of host exposure to infection. This leads to accurate reconstruction of transmission even in cases where abundant within-host pathogen genetic variation and weak transmission bottlenecks (multiple pathogen units colonising a new host at transmission) would otherwise make inference difficult due to the transmission history differing from the pathogen evolution history inferred from pathogen isolets. Also, the use of within-host pathogen genomic variants increases the resolution of the reconstruction of the transmission tree even in scenarios with limited within-outbreak pathogen genetic diversity: within-host pathogen populations that appear identical at the level of consensus sequences can be discriminated using within-host variants. Our Bayesian approach provides a measure of the confidence in different possible transmission histories, and is published as open source software. We show with simulations and with an analysis of the beginning of the 2014 Ebola outbreak that our approach is applicable in many scenarios, improves our understanding of transmission dynamics, and will contribute to finding and limiting sources and routes of transmission, and therefore preventing the spread of infectious disease.

genetics↗
bioRxiv2018

Assessing the durability and efficiency of landscape-based strategies to deploy plant resistance to pathogens

Genetically-controlled plant resistance can reduce the damage caused by pathogens. However, pathogens have the ability to evolve and overcome such resistance. This often occurs quickly after resistance is deployed, resulting in significant crop losses and a continuing need to develop new resistant cultivars. To tackle this issue, several strategies have been proposed to constrain the evolution of pathogen populations and thus increase genetic resistance durability. These strategies mainly rely on varying different combinations of resistance sources across time (crop rotations) and space. The spatial scale of deployment can vary from multiple resistance sources occurring in a single cultivar (pyramiding), in different cultivars within the same field (cultivar mixtures) or in different fields (mosaics). However, experimental comparison of the efficiency (i.e. ability to reduce disease impact) and durability (i.e. ability to limit pathogen evolution and delay resistance breakdown) of landscape-scale deployment strategies presents major logistical challenges.\n\nTherefore, we developed a spatially explicit stochastic model able to assess the epidemiological and evolutionary outcomes of the four major deployment options described above, including both qualitative resistance (i.e. major genes) and quantitative resistance traits against several components of pathogen aggressiveness: infection rate, latent period duration, propagule production rate, and infectious period duration. This model, implemented in the R package landsepi, provides a new and useful tool to assess the performance of a wide range of deployment options, and helps investigate the effect of landscape, epidemiological and evolutionary parameters.\n\nThis article describes the model and its parameterisation for rust diseases of cereal crops, caused by fungi of the genus Puccinia. To illustrate the model, we use it to assess the epidemiological and evolutionary potential of the combination of a major gene and different traits of quantitative resistance. The comparison of the four major deployment strategies described above will be the objective of future studies.\n\nAuthor summaryThere are many recent examples which demonstrate the evolutionary potential of plant pathogens to overcome the resistances deployed in agricultural landscapes to protect our crops. Increasingly, it is recognised that how resistance is deployed spatially and temporally can impact on rates of pathogen evolution and resistance breakdown. Such deployment strategies are mainly based on the combination of several sources of resistance at different spatiotemporal scales. However, comparison of these strategies in a predictive sense is not an easy task, owing to the logistical difficulties associated with experiments involving the spread of a pathogen at large spatio-temporal scales. Moreover, both the durability of a strategy and the epidemiological protection it provides to crops must be assessed since these evaluation criteria are not necessarily correlated. Surprisingly, no current simulation model allows a thorough comparison of the different options. Here we describe a spatio-temporal model able to simulate a wide range of deployment strategies and resistance sources. This model, implemented in the R package landsepi, facilitates assessment of both epidemiological and evolutionary outcomes across simulated scenarios. In this work, the model is used to investigate the combination of different sources of resistance against fungal diseases such as rusts of cereal crops.

evolutionary biology↗
bioRxiv2018

Evolution of pathogen traits in response to quantitative host resistance in heterogeneous environments

We model the evolutionary epidemiology of spore-producing plant pathogens in heterogeneous environments sown with several cultivars carrying quantitative resistances. The model explicitly tracks the infection-age structure and genetic composition of the pathogen population. Each strain is characterized by pathogenicity traits describing its infection efficiency and a time-varying sporulation curve taking into account lesion ageing. We first derive a general expression of the basic reproduction number [R]0 for fungal pathogens in heterogeneous environments. We show that evolutionary attractors of the model coincide with local maxima of [R]0 only if the infection efficiency is the same on all host types. We then study how three basic resistance characteristics (pathogenicity trait targeted, resistance effectiveness, and adaptation cost) in interaction with the deployment strategy (proportion of fields sown with a resistant cultivar) (i) lead to pathogen diversification at equilibrium and (ii) shape the transient dynamics from evolutionary and epidemiological perspectives. We show that quantitative resistance impacting only the sporulation curve will always lead to a monomorphic population, while dimorphism (i.e. pathogen diversification) can occur with resistance altering infection efficiency, notably with high adaptation cost and proportion of R cultivar. Accordingly, the choice of quantitative resistance genes operated by plant breeders is a driver of pathogen diversification. From an evolutionary perspective, the emergence time of the evolutionary attractor best adapted to the R cultivar tends to be shorter when the resistance impacts infection efficiency than when it impacts sporulation. In contrast, from an epidemiological perspective, the epidemiological control is always higher when the resistance impacts infection efficiency. This highlights the difficulty of defining deployment strategies of quantitative resistance maximising at the same time epidemiological and evolutionary outcomes.

evolutionary biology↗