Pathogen Dynamics Research Articles

Dynamic Programming-Based Approach to Model Antigen-Driven Immune Repertoire Synthesis

This paper presents a novel approach to modeling the repertoire of the immune system and its adaptation in response to the evolutionary dynamics of pathogens associated with their genetic variability. It is based on application of a dynamic programming-based framework to model the antigen-driven immune repertoire synthesis. The processes of formation of new receptor specificity of lymphocytes (the growth of their affinity during maturation) are described by an ordinary differential equation (ODE) with a piecewise-constant right-hand side. Optimal control synthesis is based on the solution of the Hamilton–Jacobi–Bellman equation implementing the dynamic programming approach for controlling Gaussian random processes generated by a stochastic differential equation (SDE) with the noise in the form of the Wiener process. The proposed description of the clonal repertoire of the immune system allows us to introduce an integral characteristic of the immune repertoire completeness or the integrative fitness of the whole immune system. The quantitative index for characterizing the immune system fitness is analytically derived using the Feynman–Kac–Kolmogorov equation.

Serologic Survey of Brucella spp. in Culled Invasive Alien Mammals from El Palmar National Park, Argentina, and in Exposed Consumers.

Brucellosis is a worldwide zoonotic disease caused by Brucella spp. and transmitted from domestic and wild animals to humans. Brucellosis causes important economic losses in livestock, has a significant impact on public health, and may affect the health of wildlife. Hunting and consumption of meat from culled wildlife constitute a risk for Brucella spp. infection in humans and hunting dogs. In El Palmar National Park (EPNP), Argentina, the invasive alien mammals wild boar (Sus scrofa) and axis deer (Axis axis) are controlled, slaughtered in situ, and consumed by hunters, with meat trimmings and offal often fed to dogs. In this study, we evaluated but did not detect anti-Brucella antibodies in wild boar (n=95) and axis deer (n=238) from EPNP or in game consumers, dogs (n=39) and humans (n=61). These results suggest a lack of exposure to Brucella spp. at this site during the study period. Despite negative findings in the sampled location, One Health surveillance across multiple species contributes to our understanding of pathogen dynamics and enables targeted interventions to minimize health risks.

Above‐ and belowground plant pathogens along elevational gradients: patterns and potential mechanisms

Plant pathogens are important for community assembly and ecosystem functioning and respond to a variety of abiotic and biotic factors, which may change along elevational gradients. Thus, elevational gradients are a valuable model system for exploring how environmental, plant community and soil factors influence pathogen communities. Yet, how these factors influence pathogens in natural ecosystems remains poorly understood. We examined the dynamics of plant fungal pathogens along elevational gradients, as well as the mechanisms shaping these dynamics, by combining a field survey on the Tibetan Plateau with a global meta‐analysis. In the field survey, increasing elevation was associated with a decrease in soil fungal pathogen richness but not in foliar fungal disease symptoms. Elevation was primarily related to soil fungal pathogen richness through abiotic factors, whereas no association was found between elevation and foliar fungal diseases. The meta‐analysis confirmed the generality of our field survey results: elevation was associated with a decrease in soil fungal pathogen richness, but it had no consistent relationship with foliar fungal diseases or pathogens. Thus, above‐ and belowground plant pathogen communities showed distinct elevational patterns, providing new insights into underlying mechanisms.

A-276 Unraveling Pathogen Dynamics In Pediatric Meningitis Complications During The Omicron Variant Outbreak In Taiwan

Abstract Background Taiwan, once celebrated for its remarkable control over COVID-19, faced an unexpected surge in pediatric cases during the first wave of the omicron variant in April 2022. Alarming data revealed acute encephalitis emerging in half of the severe or fatal COVID-19 cases in children within the initial two months of the omicron epidemic. This study delves into the intricate microbiota patterns and viral pathogens contributing to encephalitis complications in affected children. Methods The study enrolled 36 pediatric patients, comprising 11 patients with severe encephalitis, 12 patients with mild encephalitis, and 13 normal controls. Nasal swabs were collected for pathogen detection. For respiratory microbiota analysis, 16S rRNA metagenomic sequencing was employed, while the Illumina Respiratory Pathogen ID/AMR Enrichment Panel was used for respiratory pathogen identification. Results Microbiota analysis of nasal swabs from COVID-19 pediatric patients with meningitis complications revealed prevalent Corynebacterium, Staphylococcus, Dolosigranulum, Acinetobacter, Moraxella, and Streptococcus. Conversely. No predominant bacterial species were detected in the normal control group. Nasal swab respiratory pathogen detection identified coinfections, particularly Herpesviridae viruses such as human herpesvirus 6, Epstein-Barr virus, and Cytomegalovirus in pediatric patients with meningitis complications. Notably, two severe encephalitis cases exhibited coinfections involving rhinovirus, SARS-CoV-2, and Herpesviridae viruses (HHV-6, EBV). In one fatal case, distribution of Streptococcus genes among total geneomes reaches 40.69%, 7 drug resistant genes were detected. Conclusions This study suggests a potential link between latent Herpesviridae virus coinfections (HHV-6, CMV, EBV) and meningitis complications in pediatric patients. Furthermore, the presence of a secondary respiratory virus (rhinovirus) coinfection appears to be associated with severe complications. These findings shed light on the complex pathogen dynamics contributing to the emergence of encephalitis in pediatric COVID-19 cases during the omicron variant outbreak in Taiwan.

Evil and allies: Opportunistic gulls as both spreaders and sentinels of antibiotic‐resistant bacteria in human‐transformed landscapes

Abstract Human‐transformed residuals, especially those derived from human waste (dumps), farmland, and livestock are involved in the emergence of antibiotic‐resistant bacteria (ARB) in the environment. Wildlife can act as vectors of ARB dispersal through different environments, but also as sentinels to detect the early spread and determine ARB sources. The development of integrated monitoring programmes focused on wildlife would help to anticipate the risks of ARB to humans and livestock. We used the yellow‐legged gull (Larus michahellis) as a model species to investigate and monitor the spatial patterns of ARB dispersal across an extensive farmland region located in northeastern Spain (Lleida). By integrating GPS tracking data and ARB clinical testing for 26 individuals within a network analysis framework, we modelled the risk of spatial pathogen spread through faeces during the bacteria‐transmission latency period (16 days after sample collection). Additionally, we created a connectivity network to determine the main sources of ARB in the area, focusing on three main habitats of special risk for infection: dumps, livestock facilities, and irrigation ponds. Seven individuals were infected by Escherichia coli, with one also co‐infected with Listeria monocytogenes and Salmonella spp. Potential pathogen dispersal distances ranged from 1.13 km to 23.13 km from the breeding colony. Our network analyses revealed 54 main nodes (i.e. high‐risk habitats recurrently visited by tracked gulls) and 1182 links among them. Our findings revealed a high degree of connectivity between the breeding area, located in a shallow lake, and nearby dumps, highlighting them as significant contributors to ARB dispersal. Synthesis and applications: The integration of GPS data, pathogen testing and network analyses can shed further light on pathogen dynamics by creating spatial risk maps and identifying ARB sources. In combination with complementary molecular epidemiology techniques within a One Health framework, our approach can emerge as an important tool for monitoring ARB dynamics within highly human‐transformed ecosystems. This may empower managers for the development of targeted ARB monitoring programmes and effective mitigation strategies, ultimately improving both animal and public health.

Modelling the front dynamics of invasive plant pathogens through the analysis of spatial gradients

AbstractVariables of phytopathological interest correlated to the impact of plant diseases, such as incidence and severity, may display a spatial pattern resulting from an underlying, yet unknown gradient. Along the main direction of the gradient the variable assessed at the site level either increases, or decreases. Spatial gradients may also arise because of the movement of a front of invasion, an imaginary moving contour separating areas already infested by a plant pathogen from those still pathogen-free. Adequate geostatistical tools may shed light on gradients directional properties, as well as on the direction the front of invasion is coming from or moving to. Tools currently available for that may be impractical due to the advanced computational and programming skills required for their application. Hence, the goals of this study were: (I) to develop, test and validate a new user-friendly geostatistical tool named DirGrad (Direction of Gradient) aimed at analyzing spatial gradients resulting from the impact of plant diseases; (II) to build an algorithm able to run DirGrad on R, one the most widespread open source software for statistics; and (III) to apply DirGrad for the ex post modelling of the invasion front dynamics. The designed algorithm was successfully validated both in silico and in the field by using data from real case studies such as those of the invasive fungal pathogens Heterobasidion irregulare and Ophiostoma novo-ulmi in a forest stand of Central Italy and across the Swedish island of Gotland, respectively. The algorithm is released as a user-friendly open-source script.

Revealing real-time 3D in vivo pathogen dynamics in plants by label-free optical coherence tomography

Microscopic imaging for studying plant-pathogen interactions is limited by its reliance on invasive histological techniques, like clearing and staining, or, for in vivo imaging, on complicated generation of transgenic pathogens. We present real-time 3D in vivo visualization of pathogen dynamics with label-free optical coherence tomography. Based on intrinsic signal fluctuations as tissue contrast we image filamentous pathogens and a nematode in vivo in 3D in plant tissue. We analyze 3D images of lettuce downy mildew infection (Bremia lactucae) to obtain hyphal volume and length in three different lettuce genotypes with different resistance levels showing the ability for precise (micro) phenotyping and quantification of the infection level. In addition, we demonstrate in vivo longitudinal imaging of the growth of individual pathogen (sub)structures with functional contrast on the pathogen micro-activity revealing pathogen vitality thereby opening a window on the underlying molecular processes.

Functional metagenomics highlights varied infection states with dynamics of pathogens and antibiotic resistance in lower respiratory tract infections

BackgroundAntimicrobial resistance (AMR) amongst pathogenic bacterial species poses significant challenges in treating infections of the lower respiratory tract (LRT), leading to higher hospitalization and mortality rates. MethodsBronchoalveolar lavage fluid (BALF) from 84 clinically adjudicated LRTI patients were subjected to respiratory pathogen ID/AMR (RPIP) enrichment and sequencing followed by Explify and CZID seq data analysis to identify potential LRTI pathogens and associated AMR genes. Patients were categorized as LRTI-WP (with pneumonia) and LRTI-WoP (without pneumonia). FindingsmNGS achieved 100 % pathogen detection compared to 73 % through clinician-used BioFire panel. Predominant pathogens included Acinetobacter baumannii, Klebsiella pneumoniae along with detection of Aspergillus versicolor and Herpes simplex virus. Double and polymicrobial infections were captured, involving non-respiratory pathogens like Rothia mucilaginosa, Escherichia coli, and Moraxella osloensis. AMR detection highlighted macrolide (MPH; ERM) and Sulfonamide (SUL) rich resistome in 60 % of patients followed by extended spectrum beta lactamase (OXA) and tetracycline (TET). LRTI-WP showed high abundance of A. baumannii, majorly associated with MPH whereas K. pneumoniae with beta-lactams was comparable in both groups. Differences in clinical severity may stem from non-respiratory pathogens, newly recognized via mNGS. CZID seq pipeline validated and revealed additional microbes and AMR genes in the cohort. InterpretationThe prevalence of common pathogens like A. baumannii and K. pneumoniae along with the non-respiratory pathogens identified by RPIP-Explify and CZID seq provided an understanding to evaluate the LRTI. mNGS is crucial for precise pathogen and antibiotic resistance detection, vital for combating antibiotic resistance.

Endoplasmic reticulum homeostasis in plant–pathogen interactions: new scenarios for an old story

Abstract The endoplasmic reticulum (ER) is a specialized organelle that connects almost all subcellular structures from the plasma membrane to the nucleus. The ER is involved in secretory protein synthesis, folding, and processing. Evidence has emerged that the ER is at the frontier of the battle between plant hosts and pathogens. Its structural and functional homeostasis is crucial for the survival of plant cells. Pathogens secrete effectors to take over normal functions of the ER, while host plants fight back to activate ER stress responses. Exciting advances have been made in studies on host plant–pathogen dynamics during the past decades, namely some new players involved have been recently resolved from both pathogens and hosts. In this review, we summarize advances in identifying structural characteristics of the key pathways and effectors targeting the ER. Newly identified ER-phagy receptors and components downstream of inositol-requiring 1 (IRE1) will be described. Future studies will be envisaged to further our understanding of the missing parts in this dynamic frontier.

Respiratory pathogen dynamics in community fever cases: Jiangsu Province, China (2023–2024)

BackgroundRespiratory infectious diseases have the highest incidence among infectious diseases worldwide. Currently, global monitoring of respiratory pathogens primarily focuses on influenza and coronaviruses. This study included influenza and other common respiratory pathogens to establish a local respiratory pathogen spectrum. We investigated and analyzed the co-infection patterns of these pathogens and explored the impact of lifting non-pharmaceutical interventions (NPIs) on the transmission of influenza and other respiratory pathogens. Additionally, we used a predictive model for infectious diseases, utilizing the commonly used An autoregressive comprehensive moving average model (ARIMA), which can effectively forecast disease incidence.MethodsFrom June 2023 to February 2024, we collected influenza-like illness (ILI) cases weekly from the community in Xuanwu District, Nanjing, and obtained 2046 samples. We established a spectrum of respiratory pathogens in Nanjing and analysed the age distribution and clinical symptom distribution of various pathogens. We compared age, gender, symptom counts, and viral loads between individuals with co-infections and those with single infections. An autoregressive comprehensive moving average model (ARIMA) was constructed to predict the incidence of respiratory infectious diseases.ResultsAmong 2046 samples, the total detection rate of respiratory pathogen nucleic acids was 53.37% (1092/2046), with influenza A virus 479 cases (23.41%), influenza B virus 224 cases (10.95%), and HCoV 95 cases (4.64%) being predominant. Some pathogens were statistically significant in age and number of symptoms. The positive rate of mixed infections was 6.11% (125/2046). There was no significant difference in age or number of symptoms between co-infection and simple infection. After multiple iterative analyses, an ARIMA model (0,1,4), (0,0,0) was established as the optimal model, with an R2 value of 0.930, indicating good predictive performance.ConclusionsThe spectrum of respiratory pathogens in Nanjing, Jiangsu Province, was complex in the past. The primary age groups of different viruses were different, causing various symptoms, and the co-infection of viruses did not correlate with the age and gender of patients. The ARIMA model estimated future incidence, which plateaued in subsequent months.

Turning the needle into the haystack: Culture-independent amplification of complex microbial genomes directly from their native environment.

High-throughput sequencing (HTS) has revolutionized microbiology, but many microbes exist at low abundance in their natural environment and/or are difficult, if not impossible, to culture in the laboratory. This makes it challenging to use HTS to study the genomes of many important microbes and pathogens. In this review, we discuss the development and application of selective whole genome amplification (SWGA) to allow whole or partial genomes to be sequenced for low abundance microbes directly from complex biological samples. We highlight ways in which genomic data generated by SWGA have been used to elucidate the population dynamics of important human pathogens and monitor development of antimicrobial resistance and the emergence of potential outbreaks. We also describe the limitations of this method and propose some potential innovations that could be used to improve the quality of SWGA and lower the barriers to using this method across a wider range of infectious pathogens.

Reconstructing prevalence dynamics of wildlife pathogens from pooled and individual samples

Reconstructing prevalence dynamics of wildlife pathogens from pooled and individual samples

Efficient concentration of viral nucleic acid in wastewater through surfactant releasing and a two-step magnetic bead extraction and purification

Wastewater-based epidemiology (WBE) is a valuable complement to clinical monitoring, allowing for effective surveillance of viral infections in populations, and tracking the presence and the epidemiological dynamics of various infectious pathogens in communities. However, virus loads are usually low-abundant in wastewater, and current virus concentration methods for WBE are laborious and time-consuming with low recovery efficiency. To address these challenges, we have developed a magnetic bead-based semi-automated method involving extraction and purification to directly concentrate viral nucleic acids from sewage within 55 min. Prior to concentration, 0.5 % LDS was introduced to pretreat wastewater to inactivate viruses and release viral nucleic acids from both liquid and solid fractions to improve recovery. Under optimal conditions, the concentration method combined with reverse transcription-quantitative polymerase chain reaction (RT-qPCR) can detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA added exogenously in wastewater as low as 4.9 copies/mL within 2.5 h, with an average recovery rate exceeding 80 %. Testing real sewages proved the applicability of the method to detect multiple viruses in different sewages. Additionally, variants of SARS-CoV-2 were successfully identified by multiplex amplicon sequencing in two samples. In conclusion, the new method could provide a much more efficient way for WBE of pathogenic viruses in various sewages.

Quantifying the seasonal reproductive cycle in three species of Malagasy fruit bats with implications for pathogen and population dynamics.

Bats (order Chiroptera) are hosts for highly virulent zoonotic pathogens. Many bats demonstrate seasonally varying antiviral responses, including antibody responses which have been observed to peak during the nutritionally depleted dry-season and female gestation periods, suggesting some impact of resource deficits on bat virus immunity. Given the frequent overlap in these energetically demanding periods, it is likely that endocrinological changes associated with pregnancy might partially explain the aforementioned pattern in antibody dynamics. Regardless, we know little about the seasonality of reproduction in many fruit bat species, despite the importance of reproductive biology to informing conservation management (e.g. population viability) and disease dynamics. Here, we aimed to elucidate the reproductive biology of three species of endemic fruit bat native to the island of Madagascar: Pteropus rufus, Eidolon dupreanum, and Rousettus madagascarensis. To do so, we leveraged plasma samples collected in part with a longitudinal field study, from 2018 to 2020. We adapted three standard reproductive assays previously validated in humans to quantify seasonal changes in reproductive hormones for bats and applied a mixture model approach to determine hormone cutoffs for pregnancy. As expected, we found that pregnant females showed the highest levels of estradiol and progesterone and adult males the highest levels of testosterone. Additionally, female P. rufus and R. madagascariensis showed clear seasonality in reproduction with peaks in estradiol and progesterone in August and October, respectively. Seasonality was less clearly discernible in the female E. dupreanum and male data. In general, we found that the commercially available assays were successful in quantifying endocrinological hormones for bats; when paired with histological embryo sections or field data, these offer a powerful tool to elucidate bat reproductive calendars.

Navigating the Landscape of Tick Diversity: Integrating Molecular Approaches for Enhanced Control Measures.

The emergence and spread of infectious diseases, particularly zoonotic diseases originating from wildlife, pose significant threats to global health and economy. This review examines the pivotal role of ticks as vectors in transmitting pathogens to humans, livestock, and wildlife and the use of molecular techniques in their identification. Tick infestations result in economic losses through reduced animal productivity, anemia, and quality deterioration of hides. Furthermore, ticks serve as reservoirs for a wide range of pathogens including viruses, bacteria, fungi, protozoa, and nematodes, contributing to the transmission of diseases such as Crimean-Congo hemorrhagic fever, tick-borne encephalitis, and African swine fever among others. The interface between wildlife, livestock, and humans facilitates the transmission of zoonotic pathogens, exacerbated by nomadic and pastoralist lifestyles that promote interactions between wildlife and domestic animals. This movement of animals across landscapes enhances the dispersion of tick vectors, increasing the risk of pathogen exposure for diverse populations. Historically, tick identification in sub-Saharan Africa has relied on morphological characteristics despite limitations such as species overlap and variability. Molecular techniques offer a more precise means of species identification, providing critical data for effective tick and pathogen management strategies. Integrating molecular approaches into tick research enhances our understanding of tick diversity, distribution patterns, and pathogen dynamics. This knowledge is essential for developing targeted interventions to mitigate the impact of tick-borne diseases on public and veterinary health worldwide.

Pilot-Scale Evaluation of Poultryponics: Insights into Nitrogen Utilization and Food Pathogen Dynamics

Pilot-Scale Evaluation of Poultryponics: Insights into Nitrogen Utilization and Food Pathogen Dynamics

Cascading effects of mammal host community composition on tick vector occurrence at the urban human–wildlife interface

AbstractHabitat fragmentation and host community composition are implicated as key drivers of changing tick populations and tick‐borne pathogen dynamics, altering infection risk through coupled socioecological pathways that mediate interactions between tick vectors, vertebrate hosts, and humans. Patterns of host diversity may be particularly idiosyncratic across urbanized landscapes, due to trade‐offs between extreme fragmentation that reduces habitat suitability and access, and human activities that artificially increase resource availability for wildlife. We used camera and hair trap surveys and tick sampling to identify links between landscape composition and configuration, the mammalian host community, and the presence of three tick vector species at a human–wildlife interface in New York City, an emerging area within an endemic region for several tick‐borne diseases. We found that human infrastructures, such as the presence of fences in yards, could affect mammal host community composition by changing the “hardness” of edges between urban greenspaces and residential areas. We identified yard‐ and broader landscape‐level features associated with the presence of urban mammal species, and identified cascading effects of host community composition on tick distribution in yards, suggesting management implications for the mitigation of human exposure to tick‐borne pathogens. In particular, we identified a possible role of ubiquitous mesomammals, such as raccoons (Procyon lotor), in transporting Amblyomma americanum ticks between parks and neighboring residential yards, and confirmed the key role of white‐tailed deer (Odocoileus virginianus) for introducing Ixodes scapularis ticks into yards. Our results challenge assumptions that biodiversity loss in human‐modified areas always increases the risk for tick‐borne diseases. Instead, we found many residential sites had higher mammal species richness and higher detection of low reservoir competent (“dilution”) hosts for Borrelia burgdorferi, such as opossums (Didelphis virginiana), than paired forested greenspaces. Our study highlights the importance of disentangling the mechanisms mediating tick‐borne disease hazard as a critical first step toward reducing urban tick‐borne disease risk.

Regional diversity and leaf microbiome interactions of the fungal maize pathogen Exserohilum turcicum in Switzerland: A metagenomic analysis.

The spread and adaptation of fungal plant pathogens in agroecosystems are facilitated by environmental homogeneity. Metagenomic sequencing of infected tissues allowed us to monitor eco-evolutionary dynamics and interactions between host, pathogen and plant microbiome. Exserohilum turcicum, the causal agent of northern corn leaf blight (NCLB) in maize, is distributed in multiple clonal lineages throughout Europe. To characterize regional pathogen diversity, we conducted metagenomic DNA sequencing on 241 infected leaf samples from the highly susceptible Swiss maize landrace Rheintaler Ribelmais, collected over 3 years (2016-2018) from an average of 14 agricultural farms within the Swiss Rhine Valley. All major European clonal lineages of E. turcicum were identified. Lineages differ by their mating types which indicates potential for sexual recombination and rapid evolution of new pathogen strains, although we found no evidence of recent recombination. The associated eukaryotic and prokaryotic leaf microbiome exhibited variation in taxonomic diversity between years and locations and is likely influenced by local weather conditions. A network analysis revealed distinct clusters of eukaryotic and prokaryotic taxa that correlates with the frequency of E. turcicum sequencing reads, suggesting causal interactions. Notably, the yeast genus Metschnikowia exhibited a strongly negative association with E. turcicum, supporting its known potential as biological control agent against fungal pathogens. Our findings show that metagenomic sequencing is a useful tool for analysing the role of environmental factors and potential pathogen-microbiome interactions in shaping pathogen dynamics and evolution, suggesting their potential for effective pathogen management strategies.

Mobile genetic elements define the non-random structure of the Salmonella enterica serovar Typhi pangenome.

Bacterial relatedness measured using select chromosomal loci forms the basis of public health genomic surveillance. While approximating vertical evolution through this approach has proven exceptionally valuable for understanding pathogen dynamics, it excludes a fundamental dimension of bacterial evolution-horizontal gene transfer. Incorporating the accessory genome is the logical remediation and has recently shown promise in expanding epidemiological resolution for enteric pathogens. Employing k-mer-based Jaccard index analysis, and a novel genome length distance metric, we computed pangenome (i.e., core and accessory) relatedness for the globally important pathogen Salmonella enterica serotype Typhi (Typhi), and graphically express both vertical (homology-by-descent) and horizontal (homology-by-admixture) evolutionary relationships in a reticulate network of over 2,200 U.S. Typhi genomes. This analysis revealed non-random structure in the Typhi pangenome that is driven predominantly by the gain and loss of mobile genetic elements, confirming and expanding upon known epidemiological patterns, revealing novel plasmid dynamics, and identifying avenues for further genomic epidemiological exploration. With an eye to public health application, this work adds important biological context to the rapidly improving ways of analyzing bacterial genetic data and demonstrates the value of the accessory genome to infer pathogen epidemiology and evolution.IMPORTANCEGiven bacterial evolution occurs in both vertical and horizontal dimensions, inclusion of both core and accessory genetic material (i.e., the pangenome) is a logical step toward a more thorough understanding of pathogen dynamics. With an eye to public, and indeed, global health relevance, we couple contemporary tools for genomic analysis with decades of research on mobile genetic elements to demonstrate the value of the pangenome, known and unknown, annotated, and hypothetical, for stratification of Salmonella enterica serovar Typhi (Typhi) populations. We confirm and expand upon what is known about Typhi epidemiology, plasmids, and antimicrobial resistance dynamics, and offer new avenues of exploration to further deduce Typhi ecology and evolution, and ultimately to reduce the incidence of human disease.

Population dynamics of the Lyme disease bacterium, Borrelia burgdorferi, during rapid range expansion in New York State.

Recent changes in climate and human land-use have resulted in alterations of the geographic range of many species, including human pathogens. Geographic range expansion and population growth of human pathogens increase human disease risk. Relatively little empirical work has investigated the impact of range changes on within-population variability, a contributor to both colonization success and adaptive potential, during the precise time in which populations are colonized. This is likely due to the difficulties of collecting appropriate natural samples during the dynamic phase of migration and colonization. We systematically collected blacklegged ticks (Ixodes scapularis) across New York State (NY), USA, between 2006 and 2019, a time period coinciding with a rapid range expansion of ticks and their associated pathogens including Borrelia burgdorferi, the etiological agent of Lyme disease. These samples provide a unique opportunity to investigate the population dynamics of human pathogens as they expand into novel territory. We observed that founder effects were short-lived, as gene flow from long-established populations brought almost all B. burgdorferi lineages to newly colonized populations within just a few years of colonization. By 7 years post-colonization, B. burgdorferi lineage frequency distributions were indistinguishable from long-established sites, indicating that local B. burgdorferi populations experience similar selective pressures despite geographic separation. The B. burgdorferi lineage dynamics elucidate the processes underlying the range expansion and demonstrate that migration into, and selection within, newly colonized sites operate on different time scales.