Dustin Brisson

Forest fragmentation and connectivity define how mammalian hosts of the blacklegged tick (Ixodes scapularis Say, Acari: Ixodidae) utilize the landscape, ultimately guiding the spatial spread of associated pathogens. Mammalian hosts also exhibit pathogen-specific reservoir competencies, fueling continued debate as to their role as both drivers of tick density and moderators of pathogens maintained in the environment. This analysis examines the role of forest connectivity as a proxy for species richness and white-tailed deer (Odocoileus virginianus, Zimmermann) density in the spread of the human pathogenic Ap-ha and non-pathogenic Ap-v1 genotypes of Anaplasma phagocytophilum (Foggie, Rickettsiales: Anaplasmataceae). The prevalence of each genotype is used to examine the ecological mechanisms of amplification and dilution, as they each utilize specific mammalian hosts with varying reservoir competencies. We calculated patch- and landscape-level forest connectivity using the Sinuous Connection Reduction indices at 808 tick sampling sites located within 88 wildlife management units in New York State. The relationship between connectivity metric values, tick sampling, and genotyping results was then assessed using zero-inflated Poisson and Tweedie distribution generalized linear mixed models. Our analysis revealed that white-tailed deer density was positively related to I. scapularis density, Ap-v1 prevalence, and Ap-v1 entomological risk. Conversely, white-tailed deer density was negatively related to Ap-ha prevalence and exhibited no relation to Ap-ha entomological risk. These results provide evidence that white-tailed deer act as dilution hosts with respect to anaplasmosis risk in New York and support a broader Ap-ha amplification effect where increases in mammalian host biodiversity yield increased anaplasmosis risk and disease spread.

Tick-borne diseases constitute the predominant vector-borne health threat in North America. Recent observations have noted a significant expansion in the range of the black-legged tick (Ixodes scapularis Say, Acari: Ixodidae), alongside a rise in the incidence of diseases caused by its transmitted pathogens: Borrelia burgdorferi Johnson (Spirochaetales: Spirochaetaceae), Babesia microti Starcovici (Piroplasmida: Babesiidae), and Anaplasma phagocytophilium Zhu (Rickettsiales: Anaplasmataceae), the causative agents of Lyme disease, babesiosis, and anaplasmosis, respectively. Prior research identified environmental features that influence the ecological dynamics of I. scapularis and B. burgdorferi that can be used to predict the distribution and abundance of these organisms, and thus Lyme disease risk. In contrast, there is a paucity of research into the environmental determinants of B. microti and A. phagocytophilium. Here, we use over a decade of surveillance data to model the impact of environmental features on the infection prevalence of these increasingly common human pathogens in ticks across New York State (NYS). Our findings reveal a consistent northward and westward expansion of B. microti in NYS from 2009 to 2019, while the range of A. phagocytophilum varied at fine spatial scales. We constructed biogeographic models using data from over 650 site-year visits and encompassing more than 250 environmental variables to accurately forecast infection prevalence for each pathogen to a future year that was not included in model training. Several environmental features were identified to have divergent effects on the pathogens, revealing potential ecological differences governing their distribution and abundance. These validated biogeographic models have applicability for disease prevention efforts.

Traditional mechanistic trade-offs between transmission and parasite latency period length are foundational for nearly all theories on the evolution of parasite life-history strategies. Prior theoretical studies demonstrate that seasonal host activity can generate a trade-off for obligate-host killer parasites that selects for intermediate latency periods in the absence of a mechanistic trade-off between transmission and latency period lengths. Extensions of these studies predict that host seasonal patterns can lead to evolutionary bistability for obligate-host killer parasites in which two evolutionarily stable strategies, a shorter and longer latency period, are possible. Here we demonstrate that these conclusions from previously published studies hold for non-obligate host killer parasites. That is, seasonal host activity can select for intermediate parasite latency periods for non-obligate killer parasites in the absence of a trade-off between transmission and latency period length and can maintain multiple evolutionarily stable parasite life-history strategies. These results reinforce the hypothesis that host seasonal activity can act as a major selective force on parasite life-history evolution by extending the narrower prior theory to encompass a greater range of disease systems.

The modifiable areal unit problem (MAUP) is a cause of statistical and visual bias when aggregating data according to spatial units, particularly when spatial units may be changed arbitrarily. The MAUP is a concern in vector-borne disease research when entomological metrics gathered from point-level sampling data are related to epidemiological data aggregated to administrative units like counties or ZIP Codes. Here, we assess the statistical impact of the MAUP when calculating correlations between randomly aggregated cases of anaplasmosis in New York State during 2017 and a geostatistical layer of an entomological risk index for Anaplasma phagocytophilum in blacklegged ticks (Ixodes scapularis Say, Acari: Ixodidae) collected during the fall of 2017. Correlations were also calculated using various administrative boundaries for comparison. We also demonstrate the impact of the MAUP on data visualization using choropleth maps and offer pycnophylactic interpolation as an alternative. Polygon simulations indicate that increasing the number of polygons decreases correlation coefficients and their variability. Correlation coefficients calculated using ZIP Code tabulation area and Census tract polygons were beyond 4 standard deviations from the mean of the simulated correlation coefficients. These results indicate that using smaller polygons may not best incorporate the geographical context of the tick-borne disease system, despite the tendency of researchers to strive for more granular spatial data and associations.

Reservoir host associations have been observed among and within Borrelia genospecies, and host complement-mediated killing is a major determinant in these interactions. In North America, only a subset of Borrelia burgdorferi lineages cause the majority of disseminated infections in humans. We hypothesize that differential resistance to human complement-mediated killing may be a major phenotypic determinant of whether a lineage can establish systemic infection. As a corollary, we hypothesize that borreliacidal action may differ among human subjects. To test these hypotheses, we isolated primary B. burgdorferi clones from field-collected ticks and determined whether the killing effects of human serum differed among those clones in vitro and/or whether these effects were consistent among human sera. Clones associated with human invasiveness did not show higher survival in human serum compared to noninvasive clones. These results indicate that differential complement-mediated killing of B. burgdorferi lineages is not a determinant of invasiveness in humans. Only one significant difference in the survivorship of individual clones incubated in different human sera was detected, suggesting that complement-mediated killing of B. burgdorferi is usually similar among humans. Mechanisms other than differential human complement-mediated killing of B. burgdorferi lineages likely explain why only certain lineages cause the majority of disseminated human infections.

Addressing many of the major outstanding questions in the fields of microbial evolution and pathogenesis will require analyses of populations of microbial genomes. Although population genomic studies provide the analytical resolution to investigate evolutionary and mechanistic processes at fine spatial and temporal scales—precisely the scales at which these processes occur—microbial population genomic research is currently hindered by the practicalities of obtaining sufficient quantities of the relatively pure microbial genomic DNA necessary for next-generation sequencing. Here we present swga2.0, an optimized and parallelized pipeline to design selective whole genome amplification (SWGA) primer sets. Unlike previous methods, swga2.0 incorporates active and machine learning methods to evaluate the amplification efficacy of individual primers and primer sets. Additionally, swga2.0 optimizes primer set search and evaluation strategies, including parallelization at each stage of the pipeline, to dramatically decrease program runtime. Here we describe the swga2.0 pipeline, including the empirical data used to identify primer and primer set characteristics, that improve amplification performance. Additionally, we evaluate the novel swga2.0 pipeline by designing primer sets that successfully amplify Prevotella melaninogenica, an important component of the lung microbiome in cystic fibrosis patients, from samples dominated by human DNA.

General linear models have been the foundational statistical framework used to discover the ecological processes that explain the distribution and abundance of natural populations. Analyses of the rapidly expanding cache of environmental and ecological data, however, require advanced statistical methods to contend with complexities inherent to extremely large natural data sets. Modern machine learning frameworks such as gradient boosted trees efficiently identify complex ecological relationships in massive data sets, which are expected to result in accurate predictions of the distribution and abundance of organisms in nature. However, rigorous assessments of the theoretical advantages of these methodologies on natural data sets are rare. Here we compare the abilities of gradient boosted and linear models to identify environmental features that explain observed variations in the distribution and abundance of blacklegged tick ( Ixodes scapularis ) populations in a data set collected across New York State over a ten-year period. The gradient boosted and linear models use similar environmental features to explain tick demography, although the gradient boosted models found non-linear relationships and interactions that are difficult to anticipate and often impractical to identify with a linear modeling framework. Further, the gradient boosted models predicted the distribution and abundance of ticks in years and areas beyond the training data with much greater accuracy than their linear model counterparts. The flexible gradient boosting framework also permitted additional model types that provide practical advantages for tick surveillance and public health. The results highlight the potential of gradient boosted models to discover novel ecological phenomena affecting pathogen demography and as a powerful public health tool to mitigate disease risks.

The causative bacterium of Lyme disease, Borrelia burgdorferi, expanded from an undetected human pathogen into the etiologic agent of the most common vector‐borne disease in the United States over the last several decades. Systematic field collections of the tick vector reveal increases in the geographic range and prevalence of B. burgdorferi–infected ticks that coincided with increases in human Lyme disease incidence across New York State. We investigate the impact of environmental features on the population dynamics of B. burgdorferi. Analytical models developed using field collections of nearly 19,000 nymphal Ixodes scapularis and spatially and temporally explicit environmental features accurately explained the variation in the nymphal infection prevalence of B. burgdorferi across space and time. Importantly, the model identified environmental features reflecting landscape ecology, vertebrate hosts, climatic metrics, climate anomalies and surveillance efforts that can be used to predict the biogeographical patterns of B. burgdorferi–infected ticks into future years and in previously unsampled areas. Forecasting the distribution and prevalence of a pathogen at fine geographic scales offers a powerful strategy to mitigate a serious public health threat. Synthesis and applications. A decade of environmental and tick data was collected to create a model that accurately predicts the infection prevalence of Borrelia burgdorferi over space and time. This predictive model can be extrapolated to create a high‐resolution risk map of the Lyme disease pathogen for future years that offers an inexpensive approach to improve both ecological management and public health strategies to mitigate disease risk.

Background: The emergence of vector-borne pathogens in novel geographic areas is regulated by the migration of their arthropod vectors. Blacklegged ticks (Ixodes scapularis) and the pathogens they vector, including the causative agents of Lyme disease, babesiosis and anaplasmosis, continue to grow in their population sizes and to expand in geographic range. Migration of this vector over the previous decades has been implicated as the cause of the re-emergence of the most prevalent infectious diseases in North America.Methods: We systematically collected ticks from across New York State (hereafter referred to as New York) from 2004 to 2017 as part of routine tick-borne pathogen surveillance in the state. This time frame corresponds with an increase in range and incidence of tick-borne diseases within New York. We randomly sampled ticks from this collection to explore the evolutionary history and population dynamics ofI. scapularis. We sequenced the mitochondrial genomes of each tick to characterize their current and historical spatial genetic structure and population growth using phylogeographic methods.Results: We sequenced whole mitochondrial genomes from 277 ticks collected across New York between 2004 and 2017. We found evidence of population genetic structure at a broad geographic scale due to differences in the relative abundance, but not the composition, of haplotypes among sampled ticks. Ticks were often most closely related to ticks from the same and nearby collection sites. The data indicate that both short- and long-range migration events shape the population dynamics of blacklegged ticks in New York.Conclusions: We detailed the population dynamics of the blacklegged tick (Ixodes scapularis) in New York during a time frame in which tick-borne diseases were increasing in range and incidence. Migration of ticks occurred at both coarse and fine scales in the recent past despite evidence of limits to gene flow. Past and current tick population dynamics have implications for further range expansion as habitat suitability for ticks changes due to global climate change. Analyses of mitochondrial genome sequencing data will expound upon previously identified drivers of tick presence and abundance as well as identify additional drivers. These data provide a foundation on which to generate testable hypotheses on the drivers of tick population dynamics occurring at finer scales.Graphical

Traditional mechanistic trade‐offs between transmission and virulence are the foundation of nearly all theory on parasite virulence evolution. For obligate‐host killer parasites, evolution toward intermediate virulence depends on a trade‐off between virulence (time to death) and transmission (the number of progeny released upon death). Although several ecological factors impact optimal virulence strategies constrained by trade‐offs, these factors have been insufficient to explain the intermediate virulence levels observed in nature. The timing of seasonal activity, or phenology, is a factor that commonly influences ecological interactions but is difficult to incorporate into virulence evolution studies. We present a mathematical model of a seasonal obligate‐killer parasite to study the impact of host phenology on virulence evolution. The model demonstrates that host phenology can select for intermediate parasite virulence even when a traditional mechanistic trade‐off between transmission and virulence is omitted. The optimal virulence strategy is impacted by both the host activity period duration and the host emergence timing variation. Parasites with lower virulence strategies are favored in environments with longer host activity periods and when hosts emerge synchronously. The results demonstrate that host phenology can be sufficient to select for intermediate virulence strategies, providing an alternative driver of virulence evolution in some natural systems.