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bioRxiv2024

In vitro neuroendocrine differentiation of BPH-1 cells under beta2 adrenergic receptor stimulation

Epidemiologic and experimental animal studies have shown that stress can alter tumor growth and that adrenergic fibers from the sympathetic nervous system, acting through stromal {beta}2 adrenergic receptors, promote tumor cell survival during the initial phases of cancer development. Recent epidemiological data suggest that beta blocker intake improves survival of prostate cancer patients. Hypertension and high blood pressure have been suggested to increase likelihood of prostate cancer development, suggesting that the use of beta blockers in these patients could reduce prostate cancer risk. Signaling mediated by {beta} adrenergic receptors promotes neuroendocrine differentiation in the LNCaP prostate cancer cell line. Although neuroendocrine cells are a minor population in the epithelial compartment of normal prostate glands, the population of neuroendocrine-like cells that express markers such as SYN, Bcl-2, Dll-3, and AMACR correlates with cancer progression, androgen-independent state, and poor prognosis in prostate cancer patients. As recent studies show that an increase in neuroendocrine cell density occurs before development of benign prostatic hyperplasia, a non-lethal disease affecting the transition zone, in hypertensive rats, we investigated whether neuroendocrine differentiation is associated with benign prostatic hyperplasia. Our results showed that isobutyl-methyl-xanthine, which blocks cAMP degradation, and forskolin, an adenylate cyclase activator, triggered sustained activation of the cAMP/PKA pathway and led to an increase in neuroendocrine differentiation markers in BPH-1 cells, which are a benign prostatic hyperplasia cell line. Similar results were observed by combining the {beta} adrenergic receptors agonist isoproterenol with forskolin. The same treatments reduced BPH-1 cell proliferation rates by 40% and 60%, respectively. Effects on AMACR and SYN expression and cell proliferation were fully reversed by the {beta}2 blocker carvedilol. These data suggest that {beta} adrenergic receptors are linked to the development of neuroendocrine-like cells and benign prostatic hyperplasia. As 20% of the prostate cancer tumors occur in this zone, this observation warrants further investigation.

cancer biology↗

bioRxiv2024

Flood-related cases of leptospirosis in Campinas, Brazil: the interplay between drainage, impermeable areas and social vulnerability

Leptospirosis is an epidemic disease caused by bacteria of the Leptospira genus. Its risk is closely associated with inadequate sanitation and flooding, a common public health challenge in large urban centers together with urban environmental modifications, and socio-economic factors. This retrospective observational research investigated the association between the distribution of leptospirosis cases and three contextual factors --drainage, soil impermeability and social vulnerability--in Campinas city, Sao Paulo, Brazil. We hypothesized that the number of cases will increase in areas that are impermeable and in proximity to drainage systems as well as where social vulnerability is high. We investigated the associations based on 83 autochthonous cases, comparing cases where infection risk was linked to contact with floodwater or mud (n=54) to cases associated with other exposures (n=29). Spatial statistics were used to map disease distribution and investigate the relationship between leptospirosis cases and contextual factors. Our results indicate that the density of leptospirosis increases near drainage systems with risk peaking at 200 m, in areas of greater social vulnerability with increased risk due to floodwater or mud exposure in highly vulnerable regions, and in highly waterproofed urban areas. This study demonstrated that leptospirosis risk remains highly determined by living and working conditions. These findings support targeted strategies to deliver effective prevention, treatment and control interventions in highly populated urban areas of the Global South and similar contexts. Furthermore, combining local contextual environmental information with spatial analysis produces relevant evidence for guiding health public policy and spatial planning and provides precise parameters for future epidemiological models and prevention actions. Author summaryThis study explores the links between environmental and contextual factors that influence the risk of leptospirosis transmission to humans in Campinas, Sao Paulo, Brazil. Leptospirosis is an infectious disease caused by the Leptospira bacteria. We investigated how drainage systems, impermeable soil area, and social vulnerability contribute to disease distribution, using spatial statistics to map spatial conditions for 83 cases associated with water and mud contact and other risks. Our findings highlight how environmental and socio-economic factors intersect to influence public health, shedding light on the role of urban planning and drainage infrastructure in the transmission risk of infectious diseases. This interdisciplinary approach underscores the importance of considering social and environmental contexts when developing public health strategies, aligning with broader global efforts to address diseases linked to urbanization and environmental changes. Our study advances the understanding of how spatial data and environmental factors can guide more precise parameters for epidemiological models, offering insights into disease control interventions. We discuss the role of prevention, flood management, and equitable infrastructure in safeguarding public health, emphasizing how the environment shapes health risks. This research provides practical recommendations for helping decision-makers prioritize areas for intervention to reduce the burden of leptospirosis, particularly in vulnerable communities.

ecology↗

bioRxiv2025

Quantifying Metagenomic Strain Associations fromMicrobiomes with Anpan

Genetic and genomic variation among microbial strains can dramatically influence their phenotypes and environmental impact, including on human health. However, inferential methods for quantifying these differences have been lacking. Strain-level metagenomic profiling data has several features that make traditional statistical methods challenging to use, including high dimensionality, extreme variation among samples, and complex phylogenetic relatedness. We present Anpan, a set of quantitative methods addressing three key challenges in microbiome strain epidemiology. First, adaptive filtering designed to interrogate microbial strain gene carriage is combined with linear models to identify strain-specific genetic elements associated with host health outcomes and other phenotypes. Second, phylogenetic generalized linear mixed models are used to characterize the association of sub-species lineages with such phenotypes. Finally, random effects models are used to identify pathways more likely to be retained or lost by outcome-associated strains. We validated our methods by simulation, showing that we achieve more accurate effect size estimation and a lower false positive rate compared to alternative methodologies. We then applied our methods to a dataset of 1,262 colorectal cancer patients, identifying functionally adaptive genes and strong phylogenetic effects associated with CRC status, sometimes complementing and sometimes extending known species-level microbiome CRC biomarkers. Anpans methods have been implemented as a publicly available R library to support microbial community strain and genetic epidemiology in a variety of contexts, environments, and phenotypes.

bioinformatics↗

bioRxiv2025

Integration of mathematical modeling and economics approaches to evaluate strategies for control of Salmonella Dublin in a heifer-raising operation

Salmonella Dublin infections in heifer-raising operations (HROs) cause animal health and economic losses for these operations and represent a pathogen source for dairy farms obtaining replacement heifers from HROs. To improve control of S. Dublin, we (i) developed a mathematical model of S. Dublin transmission on a HRO, (ii) evaluated the vaccine effectiveness and cleaning improvements for controlling the infection, and (iii) evaluated the influence of infection and control strategies on the HROs operating income. We developed a modified Susceptible-Infected-Recovered-Susceptible model of S. Dublin spread in a batch-stocking HRO post-introduction of an index case, with stochasticity introduced through Monte Carlo simulations. Epidemiological outcomes (S. Dublin-induced deaths and abortions during raising and S. Dublin carriers and asymptomatic infections among raised replacement heifers) and operating income per 100-head raised on a HRO over a 2-year simulation were compared between control scenarios. We validated our model against S. Dublin infection data in cattle. Partial rank correlation coefficient and classification trees were used to determine parameter influence on model outcomes. Our model predicts a median of 37 carriers and 92 asymptomatic infections among raised replacement heifers out of 2,330 heifers that departed the operation by the end of the 2-year simulation period, highlighting the critical role of HROs in spreading S. Dublin. Increasing barn floor cleaning frequency (to a maximum of 12x per day) meaningfully reduced the S. Dublin epidemiological outcomes and improved the HROs operating income. Depending on the cost of cleaning, the median operating income increased between 1.2% to 10.6% in the first year when cleaning 12x per day compared to baseline (cleaning 1x per week). In most cost scenarios, predictions do not support the use of vaccination, even when paired with stringent cleaning measures. The developed model is expected to aid efforts to control S. Dublin in HROs.

ecology↗

bioRxiv2025

Trends and characteristics of multidrug resistant MRSA in Norway 2008-2020

Infections caused by multidrug-resistant (MDR) bacteria are recognized as a critical One Health concern which poses a significant threat to public health, leading to increased morbidity and mortality across both high- and low-income countries. In this study, we investigated the epidemiology and molecular mechanisms of multidrug-resistant methicillin-resistant Staphylococcus aureus (MDR-MRSA) strains identified in Norway from 2008 to 2020, in order to gain a better understanding of the evolution and dissemination of multidrug resistance in S. aureus. A total of 452 MDR-MRSA strains isolated from 429 individuals were analyzed from a dataset of 23,412 MRSA strains. Methods included epidemiological characterization, antimicrobial susceptibility testing (AST) and genetic analysis of a selection of strains using nanopore sequencing to identify antimicrobial resistance (AMR) genes and mutations, as well as their location on plasmids, SCCmec and other mobile genetic elements (MGEs). The study revealed an overall increasing trend in MDR-MRSA strains, with healthcare-associated strains being more prevalent among MDR-MRSA compared to the overall MRSA population. Significant heterogeneity in spa-types and clonal complexes exhibiting multidrug resistance was observed, with high resistance rates against multiple antibiotic groups, particularly erythromycin, ciprofloxacin/norfloxacin, tetracycline, gentamicin, and clindamycin in addition to cefoxitin. The predominant MDR-MRSA clones included t1476/CC8, t127/CC1, t189/CC188 and t030, t037/CC239. A broad range of AMR genes and mutations were detected, linked to a wide variety of MGEs, highlighting the complex mechanisms of resistance development and dissemination within the MRSA population. This study highlights the rising challenge posed by MDR-MRSA strains, and reveals the multifactorial nature of AMR in S. aureus, thus emphasizing the importance of continued surveillance, antibiotic stewardship and infection control measures, as well as global cooperation, in order to combat the spread of these multidrug-resistant pathogens. Author SummaryIn our study, we explored the landscape of multidrug-resistant methicillin-resistant Staphylococcus aureus (MDR-MRSA) in Norway from 2008 to 2020. This research is possible because it draws on a robust national surveillance system that has been active for over a decade, aimed at preventing the establishment of these dangerous pathogens in our healthcare facilities. While the overall incidence of MDR-MRSA was relatively low, we noticed an upward trend in the number of these resistant strains over time. This pattern, along with shifts in the molecular profiles of the strains, suggests that certain MDR-MRSA clones have become well-established and are spreading globally. One of the most important findings was that the majority of MDR-MRSA strains were acquired abroad. This indicates that international travel and migration are significant contributors to the spread of these resistant strains, particularly from regions like Asia and Africa. This underscores the necessity for global collaboration in surveillance and antibiotic stewardship to combat the threat posed by these pathogens. Additionally, we found that a high proportion of MDR-MRSA strains were associated with healthcare settings, primarily isolated from patients during hospital admissions. This is concerning, as it suggests that the most resistant strains are often found in hospitals, where vulnerable patients are at risk. The high antibiotic exposures in these environments likely contributes to the selection and spread of these resistant clones. Interestingly, we discovered that many of the MDR-MRSA strains were detected in asymptomatic carriers rather than in clinical infections. This could be due to the strains being acquired abroad and subsequently identified through routine screening in healthcare settings. The overall potential implications for public health are however significant, especially since the resistance profiles of these strains can severely limit treatment options. By utilizing advanced nanopore sequencing technology, we were able to delve deeper into the genetic elements responsible for antibiotic resistance, highlighting the extensive heterogeneity of resistance mechanisms among the MDR-MRSA strains. We found that resistance genes are primarily located on plasmids and other mobile genetic elements, which enhances their potential for spread among different strains. This complexity of resistance mechanisms and the adaptive strategies employed by MRSA highlight the ongoing battle against antibiotic resistance. In conclusion, our study sheds light on the evolving landscape of MDR-MRSA, emphasizing the need for continued vigilance and coordinated efforts to mitigate the spread of these resistant strains.

microbiology↗

bioRxiv2025

First detection of Crimean Congo Hemorrhagic Fever antibodies in cattle and wild fauna of southern continental France: investigation of explicative factors

Crimean-Congo Haemorrhagic Fever (CCHF) is a tick-borne zoonosis with significant public health implications due to its expanding geographic distribution and potential for severe outcomes in humans. This study represents the first serological survey conducted in mainland France to detect antibodies against the Crimean-Congo Haemorrhagic Fever Virus (CCHFV) in domestic and wild fauna, providing critical insights into the viruss circulation. We analyzed 8,609 cattle sera and 2,182 wildlife sera collected across the French Mediterranean region from 2008 to 2022, using enzyme-linked immunosorbent assays (ELISA) and pseudo-plaque reduction neutralization tests (PPRNT) for antibody detection and confirmation. Seropositivity was detected in both cattle (2.04%) and wildlife (2.25%), with higher rates observed in specific regions such as Pyrenees-Orientales and Hautes-Pyrenees. These findings reveal spatial clusters of CCHFV circulation and suggest the existence of enzootic transmission cycles involving local tick vectors and animal hosts. Our multivariate analysis identified key factors influencing seropositivity, including animal age, habitat characteristics, and potential wildlife interactions. The presence of natural open habitats and coniferous forests was significantly associated with higher seropositivity in cattle, while sex and geographical variability played a role in wildlife seroprevalence. These findings highlight the importance of environmental and anthropogenic factors in shaping the dynamics of CCHFV transmission. This study demonstrates that CCHFV is actively circulating in parts of mainland France, emphasizing the need for enhanced surveillance and integrated approaches to monitor zoonotic pathogens. It also raises questions about the role of additional tick vectors, such as Hyalomma lusitanicum, in the transmission cycle. These results contribute to a better understanding of CCHF epidemiology and offer valuable guidance for public health strategies to mitigate the risks associated with this emerging disease. Author SummaryCrimean-Congo Haemorrhagic Fever (CCHF) is a severe disease spread by ticks that affects humans and animals. Although the disease is largely distributed in parts of Europe, Africa, and Asia, its presence in France has been uncertain. My study investigated whether the virus causing this disease is circulating in southern mainland France by testing blood samples from domestic animals, like cattle, and wild animals, such as deer and wild boars, for signs of previous infection. I found evidence of the virus in several regions, particularly in the Pyrenees-Orientales, Hautes-Pyrenees and Alpes-maritimes, suggesting that the virus is indeed circulating among animals and ticks in some parts of mainland France. By studying where infected animals were found and considering factors such as age, habitat, and environmental conditions, I identified that older animals seem to have been more often exposed to the virus, as well as animals frequenting open environments favorable to ticks. These findings are important because they show that the disease could potentially spread to new areas and affect human populations. My work highlights the need for ongoing monitoring of ticks and animals for CCHFV epidemiological surveillance and the protection of public health.

ecology↗

bioRxiv2025

Explainable machine learning for health disparities: type 2 diabetes in the All of Us research program

Type 2 diabetes (T2D) is a disease with high morbidity and mortality and a disproportionate impact on minority groups. Machine learning (ML) is increasingly used to characterize T2D risk factors; however, it has not been used to study T2D health disparities. Our objective was to use explainable ML methods to discover and characterize T2D health disparity risk factors. We applied SHapley Additive exPlanations (SHAP), a new class of explainable ML methods that provide interpretability to ML classifiers, to this end. ML classifiers were used to model T2D risk within and between self-identified race and ethnicity (SIRE) groups, and SHAP values were calculated to quantify the effect of T2D risk factors. We then stratified SHAP values by SIRE to quantify the effect of T2D risk factors on prevalence differences between groups. We found that ML classifiers (random forest, lightGBM, and XGBoost) accurately modeled T2D risk and recaptured the observed prevalence differences between SIRE groups. SHAP analysis showed the top seven most important T2D risk factors for all SIRE groups were the same, with the order of importance for features differing between groups. SHAP values stratified by SIRE showed that income, waist circumference, and education best explain the higher prevalence of T2D in the Black or African American group, compared to the White group, whereas income, education and triglycerides best explain the higher prevalence of T2D in the Hispanic or Latino group. This study demonstrates that explainable ML can be used to elucidate health disparity risk factors and quantify their group-specific effects. Author SummaryWhile machine learning (ML) methods hold great promise for epidemiological studies, their practical utility is limited by interpretability. Increasingly complex ML models are great at predicting disease risk, but how they arrive at a given prediction is often obscured by model complexity. Explainable ML is an emerging discipline that seeks to render ML models more transparent by elucidating how and why input features contribute to output predictions. This study reports a novel application of explainable ML to epidemiology, focusing on type 2 diabetes (T2D) as a paradigm of health disparities. We found that ML classifiers were able to accurately model T2D disparities, for a large cohort of Black, Hispanic, and White Americans, and explainable ML revealed which risk factors contributed to the observed disparities and how. The results demonstrate that explainable ML can be a powerful tool for the discovery and characterization of health disparity risk factors.

bioinformatics↗

bioRxiv2025

Influenza A Virus Interferes With Respiratory Syncytial Virus in Mice and Reconstituted Human Airway Epithelium

Epidemiological studies suggest that respiratory syncytial virus (RSV) and influenza A virus (IAV) might interfere with each other. Viral interference mainly relies on interferon production elicited by a first virus that reduces the replication of a second virus. In this paper, we first investigated the interactions between RSV-A2 and influenza A(H1N1)pdm09 in BALB/c mice infected with each single virus or both viruses simultaneously or sequentially before, at the peak of interferon elicited by each virus or after that peak. IAV reduced by almost 3.0 logs the replication of RSV administered at the peak of interferon induced by influenza, but the opposite was not true. However, IAV-infected mice challenged with RSV or the vehicle lost more weight and had a lower survival rate compared to single infections. Interferon expression, cytokine levels and pulmonary inflammation were almost similar between groups. Disease worsening was attributed to an aggravation of IAV-induced pulmonary congestion following intranasal instillation of fluid (with or without RSV). In human airway epithelia, IAV also interfered with RSV replication. Viral interference was dependent on the timing and sequence of infections, but not on differential interferon susceptibilities. Overall, our results help to understand the mechanisms of interaction between two major respiratory viruses. ImportanceRespiratory syncytial virus and influenza virus may interfere with each other based on epidemiological studies. It is suggested that a first virus may induce the production of interferon and interfere with the replication of a second unrelated virus. Our data showed that influenza A virus interferes with respiratory syncytial virus replication in mouse lungs, but the opposite was not observed. In reconstituted human airway epithelia, viral interference was dependent on the timing and sequence of infections, but not on differential interferon susceptibilities. Understanding the mechanisms of interaction between respiratory viruses may help the development of prophylactic or therapeutic modalities.

microbiology↗

bioRxiv2025

Quantifying viral pandemic potential from experimental transmission studies

In an effort to avert future pandemics, surveillance studies aimed at identifying zoonotic viruses at high risk of spilling over into humans act to monitor the viral chatter at the animal-human interface. These studies are hampered, however, by the diversity of zoonotic viruses and the limited tools available to assess pandemic risk. Methods currently in use include the characterization of candidate viruses using in vitro laboratory assays and experimental transmission studies in animal models. However, transmission experiments yield relatively low-resolution outputs that are not immediately translatable to projections of viral dynamics at the level of a host population. To address this gap, we present an analytical framework to extend the use of measurements from experimental transmission studies to generate more quantitative risk assessments. Specifically, we use within-host viral titer data from index and contact animals to estimate parameters relevant to transmission, including an estimate of transmissibility. We then extended this model to estimate epidemiological parameters, such as the basic reproductive number and generation interval. To illustrate these approaches, we present them in the context of two influenza A virus (IAV) ferret transmission experiments: one using influenza A/California/07/2009 (Cal/2009) and one using influenza A/Hong Kong/1/1968 (Hong Kong/1968). Despite estimating broadly similar transmissibilities for Cal/2009 and Hong Kong/1968, we conclude that Cal/2009 has a higher pandemic potential. This difference in pandemic potential seems to be primarily driven by its more robust within-host replication. Our results critically depend on several assumptions, namely that the within-host viral dynamics in humans and those in the model animal used (here, ferrets) share quantitative similarities and that viral transmissibility between model animals reflects viral transmissibility between humans. The methods we present to assess relative pandemic risk across viral isolates can be used to improve quantitative risk assessment of other emerging viruses of pandemic concern. Author summaryPandemic viruses often originate in animal reservoirs. Surveillance studies at the animal-human interface try to identify which of the many viruses circulating in animals could cause a pandemic in humans. Experimental transmission studies in animal models are one such method used to characterize potential pandemic viruses. However, only simple outcomes usually get reported from these experiments, such as the fraction of animals that become infected in such an experiment. Here, we develop a framework to extract more information from these experiments. Specifically, we use data on the virus population within infected ferrets to extrapolate information about transmission. We then simulate many transmission events to estimate epidemiological parameters commonly used in public health to quantify pandemic potential. Our methods therefore allow us to use commonly gathered data from experimental transmission studies to estimate epidemic dynamics in humans. This set of methods can be used to compare the relative pandemic risk of animal viruses before they emerge in humans.

microbiology↗

bioRxiv2025

Disentangling the impact of abiotic and biotic environmental factors and dispersal dynamics on bacterial pangenome fluidity

Understanding how pangenomes originate and evolve is crucial for predicting evolutionary trajectories and uncovering ecological interactions of bacterial pathogens. Pangenome fluidity has been attributed to adaptive evolution, yet the underlying ecological drivers for bacterial pathogens persisting in natural reservoirs remain poorly understood. Listeria monocytogenes (Lm), a foodborne pathogen causing fatal listeriosis, serves as an ideal model for investigating the ecological mechanisms underlying pangenome fluidity in bacterial pathogens due to its high evolutionary divergence, broad ecological versatility, and significant public health concern. Through pangenome analysis of 177 Lm isolates representing three evolutionary lineages (I, II, and III) that we isolated from soils across the United States, we found that substantial genome variation was strongly associated with climatic factors (e.g. precipitation and temperature), soil properties (e.g. aluminum, pH, and molybdenum), and bacterial community composition, particularly Nitrospirae, Planctomycetes, Acidobacteria, and Cyanobacteria. These factors exerted selective pressure across many gene functions, with pronounced effects on genes involved in cell envelope synthesis, defense mechanisms, and replication, recombination, and repair. Among Lm lineages occupying varied habitats, distinct pangenome properties were observed. Lineage III exhibited a highly fluid pangenome, which was attributed to local adaptation to nutrient-limited conditions and strong dispersal limitation. In contrast, lineage I maintained a conserved pangenome, likely due to frequent homogenizing dispersal. Consistent with these dispersal patterns, we identified an elevated risk of soil-to-human transmission in lineage I, evidenced by epidemiological links between three soil-derived and 17 clinical isolates. Collectively, this study reveals the pivotal role of environmental selection imposed by both abiotic factors and bacterial communities in governing the adaptive pangenome evolution in bacterial pathogens. It also highlights significant differences in pangenome flexibility, ecological niches, and transmission dynamics across lineages of the same pathogen species, underscoring the need for tailored source tracking strategies. AUTHOR SUMMARYStudying the full set of genes found in different strains of a bacterium (i.e. pangenome) helps us understand how bacterial pathogens develop and adapt to changes in the environment. Here, we focused on Listeria monocytogenes (Lm), a pathogen capable of spreading through food and surviving in diverse environments, to understand how environmental factors and the way that bacteria move across locations can influence the pangenome content in this important bacterium. By analyzing the genomes of 177 Lm strains representing three evolutionary lineages (I, II, and III) collected from soils across the United States, we found that variation in climate, soil chemistry, and surrounding bacteria (e.g., Nitrospirae) was closely linked to genetic differences among strains. These environmental conditions seemed to affect genes that help build the cell envelop, protect the bacteria from harm, and fix damaged DNA. We also observed different levels of genome flexibility across Lm lineages which were found to be related to how they move across different locations. Lineage III showed evidence of barriers to spreading, which may enhance genetic differentiation across populations, leading to a more flexible pangenome. In contrast, lineage I appeared to spread more readily and was epidemiologically linked to human clinical cases, which may facilitate genetic exchange that reduce pangenome diversity. This study shows that both non-living environmental conditions--like precipitation and pH--and nearby groups of bacteria play a big role in shaping how bacterial pathogens change their genes to survive. It also highlights that different subtypes of the same pathogen can have different gene flexibility and spread in different ways, calling for specific biocontrol measures.

genomics↗

bioRxiv2025

Identification of ESBL-producing enterobacterales from vegetable plants in a rural area of Madagascar: insights into AMR transmission at the human-animal-environment-plant interface

Extended-spectrum b-lactamases (ESBL)-producing enterobacterales are considered a key indicator for antimicrobial resistance (AMR) epidemiological surveillance in animal, human and environment compartments. In this study we aim to investigate the presence and genetic diversity of ESBL-producing enterobacterales on vegetable plants. We isolated beta-lactam resistant enterobacterales from several vegetable plants and sequenced their whole genome. Utilizing cutting-edge genomic and phylogenetic methods, we sought to i) characterize their resistome and plasmidome, ii) investigate the genetic structure of the plant-isolated strains and iii) determine their relationships with strains from other reservoirs. Among the 22 strains collected from vegetable plants, 6 showed resistance to beta-lactam antibiotics, with 5 of them identified as ESBL producers. Resistome analysis indicated the presence of multidrug-resistant (MDR) strains containing multiple antibiotic resistance genes (ARGs). Importantly, no host-specific lineages were identified among the plant-isolated ESBL-producing E. coli (ESBL-Ec). Instead, these strains exhibited genetic and epidemiological connections with strains isolated from animals, humans, and the environment, suggesting transfer of ESBL-Ec between plants and other sources in rural Madagascar. These findings suggest that vegetable plants are contaminated as a result of human activities, posing a potential risk of human and animal exposure to antibiotic-resistant bacteria and genes.

genomics↗

bioRxiv2025

Navigating sampling bias in discrete phylogeographic analysis: assessing the performance of an adjusted Bayes factor

Bayesian phylogeographic inference is widely used in molecular epidemiological studies to reconstruct the dispersal history of pathogens. Discrete phylogeographic analysis treats geographic locations as discrete traits and infers lineage transition events among them, and is typically followed by a Bayes factor (BF) test to assess the statistical support. In the standard BF (BFstd) test, the relative abundance of the involved trait states is not considered, which can be problematic in the case of unbalanced sampling. Existing methods to correct sampling bias in discrete phylogeographic analyses using continuous-time Markov chain (CTMC) model, often require additional epidemiological information to balance the sampling effort among locations. As such data is not necessarily available, alternative approaches that rely solely on available genomic data are needed. In this perspective, we assess the performance of a modification of the BFstd, the adjusted Bayes factor (BFadj), which incorporates information on the relative abundance of samples by location when inferring support for transition events and root location inference without requiring additional data. Using a simulation framework, we assess the statistical performance of BFstd and BFadj under varying levels of sampling bias, estimating their type I and type II error rates. Our results show that BFadj complements the BFstd by reducing type I errors at the cost increasing type II errors for inferred transition events, while improving type I and type II errors in root location inference. Our findings provide guidelines for implementing the complementary BFadj to detect and mitigate sampling bias in discrete phylogeographic inference using CTMC modelling.

evolutionary biology↗

bioRxiv2025

Common misspecification of the generation interval leads to reproduction number underestimation in phylodynamic inference

Generation intervals are distributions that describe the time between infection and onward transmission. They are a key epidemiological quantity because, together with the reproduction number R, they determine the population-level growth rate of a pathogen and its doubling time. Conversely, when fitting epidemiological models to data, assumed generation intervals impact R inference. This is well-known from studies that have used case data for R inference, with many studies emphasizing the importance of choosing an accurate distribution for the generation interval. In phylodynamic inference of R, the generation interval distribution is often not explicitly mentioned, and the impact of generation interval misspecification has been studied less. Here, we explore the impact of a commonly assumed (but rarely empirically accurate) exponential generation interval distribution on the estimation of R in phylodynamic inference. Using simulations and inference on these simulated datasets, we find that during the early exponential growth of an epidemic, if the generation interval is assumed to be exponentially distributed when it actually has a lower variance, then estimates of R will be biased low. Furthermore, uncertainty in the biased R estimates will be small. Our work highlights the importance of acknowledging implicit generation interval assumptions in phylodynamic inference and points to the need for methodological developments in phylodynamic inference to provide greater flexibility in the specification of accurate generation intervals.

evolutionary biology↗

bioRxiv2025

Malaria parasite population genomics during an elimination program in Eastern Myanmar

BackgroundWe investigated parasite population genomics during an intensive malaria elimination program in Kayin State (Myanmar), in which malaria posts were used for rapid detection and treatment of malaria cases, while mass drug administration (MDA) was used in villages with high submicroscopic reservoirs. MethodsWe collected 5014 dried blood spots from Plasmodium falciparum infected patients from 413 malaria posts, over 58-months (November 2015 - August 2020), and sequenced 2270 parasite genomes, each with geographic references (latitude and longitude). We used identity-by-descent (IBD) relationships to examine how control efforts impact parasite population structure. FindingsParasites were genetically depauperate: 1726 single-genotype infections comprised 166 unique genomes ([≥]90% IBD), while nine families ([≥]45% IBD) accounted for 62{middle dot}5% of parasites sampled. We observed localized, temporally stable transmission of unique parasite genotypes, identifying transmission chains. Parasite relatedness was positively correlated up to approximately 20 km revealing the scale of parasite subpopulations. Kelch13 diversity was stable from 2016-2019, but only one predominant clonal genotype (kelch13-R561H) remained in 2020. MDA resulted in parasite founder effects, providing genomic evidence for the efficacy of this malaria control tool. InterpretationOur genomic data show that parasite population size decreased over the study period, and we observed regional distribution of parasite genotypes, which can define operational units for parasite control. One parasite genotype (kelch13-R561H) from the north rose to high frequency in 2020, because transmission was halted elsewhere in the control area. Future surveillance will reveal whether this genotype spreads to neighboring regions. Genetic drift may have a stronger impact on parasite population structure than selection in low-transmission elimination settings. FundingNational Institutes of Health; Wellcome Trust; Global Fund to Fight against AIDS, Tuberculosis and Malaria; The Bill and Melinda Gates Foundation. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSThis study describes the genomic analysis of malaria parasites collected during a malaria elimination program targeting four townships (Myawaddy, Kawkareik, Hlaingbwe, and Hpapun) in Kayin State, eastern Myanmar. We searched PubMed on May 10, 2025, using the terms: "malaria", "transmission", "drug resistance", "southeast Asia" and "genetic surveillance" without any date or language restrictions. Our search yielded 58 publications, including reports on declined immunity to malaria and increased drug resistance in southeast Asia; genetic diversity and population structure shaped by mass drug administration, transmission intensity, human movement and mosquito ecology; and the spread of kelch13 and pfcrt mutations conferring dihydroartemimsinin-piperaquine resistance in east southeast Asia countries. Added value of this studyWe sequenced and analyzed 2270 Plasmodium falciparum whole genomes (each with geographic location) collected between November 2015 and August 2020 in Kayin State. We described fine-scale molecular epidemiology changes during intense malaria elimination interventions. We observed localized, temporally stable transmission of parasite genotypes, with different parasite families and kelch13 haplotypes predominating in different subregions over the 5-year period. We found kelch13 haplotypes with local or regional origin, but none originating from eastern southeast Asia. The genomic data indicated decreasing parasite population size, but we observed no selection towards drug-resistance parasites. In 2020, only one predominant lineage (kelch13-R561H) remained in our studying region, consistent with genetic drift in a pre-elimination setting. Implications of all the available evidenceThe parasite population was genetically depauperate, with a spatially localized and temporally stable distribution of parasite lineages, and declining population size. In this situation genetic drift may play a heightened role in parasite epidemiology. Consistent with this, parasites carrying kelch13-R561H rose to high frequency in 2020, because transmission was eliminated in all regions except the north of Kayin State, where parasites bearing this kelch13 genotype have predominated since 2017. Future surveillance will determine whether this parasite genotype is transmitted to neighboring regions, and thus related to changes in P. falciparum drug resistance dynamics on the Thailand Myanmar border. These data illustrate how genetic drift in small populations can result in instantaneous changes in the resistance status of parasite populations in near elimination settings.

genomics↗

bioRxiv2025

Biological causes and impacts of rugged tree landscapes in phylodynamic inference

Phylodynamic analysis has been instrumental in elucidating epidemiological and evolutionary dynamics of pathogens. Bayesian phylodynamics integrates out phylogenetic uncertainty, which is typically substantial in phylodynamic datasets due to limited genetic diversity. Phylodynamic inference does not, however, scale with modern datasets, partly due to difficulties in traversing tree space. Here, we characterize tree space and landscape in phylodynamic inference and assess its impacts on analysis difficulty and key biological estimates. By running extensive Bayesian analyses of 15 classic large phylodynamic datasets and carefully analyzing the posterior samples, we find that the posterior tree landscape is diffuse yet rugged, leading to widespread tree sampling problems that usually stem from sequences in a small part of the tree. We develop clade-specific diagnostics to show that a few sequences--including putative recombinants and recurrent mutants--frequently drive the ruggedness and sampling problems, although existing data-quality tests show limited power to detect them. The sampling problems can significantly impact phylodynamic inferences or distort major biological conclusions; the impact is usually stronger on "local" estimates (e.g., introduction history) associated with particular clades than on "global" parameters (e.g., demographic trajectory) governed by general tree shape. We evaluate existing and newly-developed MCMC diagnostics, and offer strategies for optimizing phylodynamic analysis settings and mitigating sampling problem impacts. Our findings highlight the need and directions to develop efficient traversal over rugged tree landscapes, ultimately advancing scalable and reliable phylodynamics. Significance StatementBayesian phylodynamics is central to epidemiological studies, but exploring the vast and complex tree space is computationally challenging. Phylodynamic datasets comprise many highly similar sequences, sampled through time, creating a uniquely structured landscape of optimal trees. Here, we show that phylodynamic tree landscapes are often highly rugged, with multiple peaks separated by difficult-to-cross valleys. These features lead to widespread sampling problems which are often driven by a few sequences. These problems can significantly impact phylodynamic estimates, especially those associated with particular clades, distorting biological conclusions. We develop diagnostics to identify problematic sequences and provide solutions to mitigate their impacts. We offer strategies to optimize phylodynamic analysis workflows and to develop algorithms for navigating rugged landscapes, thereby advancing infectious disease investigation.

evolutionary biology↗

bioRxiv2025

Rift Valley fever virus dynamics in a transhumant cattle system in The Gambia

Rift Valley fever (RVF) is a zoonotic disease of global concern, driven by environmental conditions, vector activity, and livestock mobility. Although RVF has been reported in The Gambia, its epidemiology remains poorly understood. This study developed a compartmental model to study RVF dynamics in the cattle population of the country. The model incorporated seasonally dynamic transmission parameters reflecting transhumant movement and ecological differences between two distinct ecoclimatic regions: the Sahelian area and the Gambia river. Parameterised using serological data linked to household survey data, the model predicted endemic RVF virus (RVFV) circulation within The Gambia, and captured temporal infection trends that closely match empirical data. Weak decay rates of seropositivity were required to match predicted and observed age-seroprevalence. Results indicated sustained RVFV transmission during the dry season in the Gambia river eco-region, with a high risk of seasonal virus introductions to the Sahelian eco-region at the start of the wet season via the returning transhumant cattle. Our study highlighted the role of livestock mobility in RVFV epidemiology in The Gambia and the need for targeted control strategies that might include, for example, targeted cattle vaccination or application of topical insecticide treatments for transhumant herds.

ecology↗

bioRxiv2025

Exact Expectation of Complete Spatial Randomness for Nearest Neighbor G(r): A Scalable Alternative to Permutations

Spatial analysis is becoming increasingly important for studies, from epidemiology to tissue biology, as technologies advance and experimental costs decrease. However, the widespread use of spatial metrics such as Nearest Neighbor G(r) is affected by the fact that biological systems rarely satisfy the assumption of stationarity, which is required to appropriately use theoretical complete spatial randomness (CSR) measures. As a result researchers often use computationally expensive permutations to empirically estimate CSR for subsets of points or cells. Here, we present closed form analytical solutions for both the mean and variance of the sample-specific CSR for Nearest Neighbor G(r) to allow for fast and reproducible calculation without permutations. Using a multiplex immunofluorescence sample of clear cell renal cell carcinoma, we show that the theoretical G(r) for cytotoxic T cells overestimates CSR at low radii (due to spatial constraints between cells) while drastically underestimating CSR at radii between 20 and 90 pixels. In a simulated sample of 30 points, our analytical solution for the mean is identical to the average of G(r) measured on all 142,506 unique combinations of 5 marked (or positive) points. On the real clear cell renal cell carcinoma sample, our exact CSR is similar in speed to estimating CSR with 1000 permutations while our optimized Rcpp implementation is [~]30x faster and consuming [~]20x less memory than 1000 permutations. This permutation-free approach dramatically enhances computational efficiency and reproducibility, enabling scalable and reproducible analysis for studies in epidemiology, multiplex immunofluorescence, spatial transcriptomics, and related fields where accurate, sample-specific null expectations are important for comparisons.

bioinformatics↗

bioRxiv2025

The macroecology of immunity: predominant influence of climate on invertebrate immune response

The immune system is the primary defense against parasites. With the ever-increasing rate of disease, epidemiologic models considering geographic variation in immune responses could prove useful. Despite increasing interest in the macroecology of parasitism and infectious diseases, we know little about the macroecology of immune responses. Host characteristics, parasite exposure, and environmental factors can all affect immunity, but how these factors interact to shape spatial variation in the strength of immune responses remains unexplored. We captured odonates (dragonflies and damselflies) and their conspicuous ectoparasitic mites across a geographic area spanning the temperate and boreal forest biomes in eastern Canada. We then conducted immune response bioassays on 1,237 individuals from 63 odonate species. We used linear regressions and structural equation models to relate immune responses to host body size, parasite load, pH, temperature, and precipitation while accounting for evolutionary relationships among host species. We found significant differences in the strength of immune response among host individuals, and this variation was best explained by climatic conditions, specifically decreasing with precipitation and, to a lesser degree, temperature. While host species significantly differed in immune response strength, we found no effect of host body size, evolutionary relationships among hosts, or parasitism on immune response. Our study investigating the drivers of immune response across dozens of species spread across two biomes is the most comprehensive to date. Climatic conditions have a strong influence on host immune response, regardless of host characteristics or parasitism rates. In this specific case, strong immune responses were associated with low levels of annual precipitation, which could relate to the role of cuticular melanin content in desiccation resistance, and the melanin-based encapsulation response being a byproduct of this adaptation. A spatially-explicit understanding of the biological processes affecting immunity could improve epidemiological models of disease risk that inform disease management globally.

immunology↗