Size Doesn\u27t Matter: Microbial Selection Experiments Address Ecological Phenomena

Experimental evolution is relevant to ecology because it can connect physiology, and in particular metabolism, to questions in ecology. The investigation of the linkage between the environment and the evolution of metabolism is tractable because these experiments manipulate a very simple environment to produce predictable evolutionary outcomes. In doing so, microbial selection experiments can examine the causal elements of natural selection: how specific traits in varying environments will yield different fitnesses. Here, we review the methodology of microbial evolution experiments and address three issues that are relevant to ecologists: genotype-by-environment interactions, ecological diversification due to specialization, and negative frequency-dependent selection. First, we expect that genotype-by-environment interactions will be ubiquitous in biological systems. Second, while antagonistic pleiotropy is implicated in some cases of ecological specialization, other mechanisms also seem to be at work. Third, while negative frequency-dependent selection can maintain ecological diversity in laboratory systems, a mechanistic (biochemical) analysis of these systems suggests that negative frequency dependence may only apply within a narrow range of environments if resources are substitutable. Finally, we conclude that microbial experimental evolution needs to avail itself of molecular techniques that could enable a mechanistic understanding of ecological diversification in these simple systems

It Takes a Community to Raise the Prevalence of a Zoonotic Pathogen

By definition, zoonotic pathogens are not strict host-species specialists in that they infect humans and at least one nonhuman reservoir species. The majority of zoonotic pathogens infect and are amplified by multiple vertebrate species in nature, each of which has a quantitatively different impact on the distribution and abundance of the pathogen and thus on disease risk. Unfortunately, when new zoonotic pathogens emerge, the dominant response by public health scientists is to search for a few, or even the single, most important reservoirs and to ignore other species that might strongly influence transmission. This focus on the single “primary” reservoir host species can delay biological understanding, and potentially public health interventions as species important in either amplifying or regulating the pathogen are overlooked. Investigating the evolutionary and ecological strategy of newly discovered or emerging pathogens within the community of potential and actual host species will be fruitful to both biological understanding and public health

Modeling Tick Populations: An Ecological Test Case for Gradient Boosted Trees

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

Tick-, mosquito-, and rodent-borne parasite sampling designs for the National Ecological Observatory Network

Parasites and pathogens are increasingly recognized as significant drivers of ecological and evolutionary change in natural ecosystems. Concurrently, transmission of infectious agents among human, livestock, and wildlife populations represents a growing threat to veterinary and human health. In light of these trends and the scarcity of long-term time series data on infection rates among vectors and reservoirs, the National Ecological Observatory Network (NEON) will collect measurements and samples of a suite of tick-, mosquito-, and rodent-borne parasites through a continental-scale surveillance program. Here, we describe the sampling designs for these efforts, highlighting sampling priorities, field and analytical methods, and the data as well as archived samples to be made available to the research community. Insights generated by this sampling will advance current understanding of and ability to predict changes in infection and disease dynamics in novel, interdisciplinary, and collaborative ways. (Résumé d'auteur

Biodiversity of Borrelia burgdorferi Strains in Tissues of Lyme Disease Patients

Plant and animal biodiversity are essential to ecosystem health and can provide benefits to humans ranging from aesthetics to maintaining air quality. Although the importance of biodiversity to ecology and conservation biology is obvious, such measures have not been applied to strains of an invasive bacterium found in human tissues during infection. In this study, we compared the strain biodiversity of Borrelia burgdorferi found in tick populations with that found in skin, blood, synovial fluid or cerebrospinal fluid of Lyme disease patients. The biodiversity of B. burgdorferi strains is significantly greater in tick populations than in the skin of patients with erythema migrans. In turn, strains from skin are significantly more diverse than strains at any of the disseminated sites. The cerebrospinal fluid of patients with neurologic Lyme disease harbored the least pathogen biodiversity. These results suggest that human tissues act as niches that can allow entry to or maintain only a subset of the total pathogen population. These data help to explain prior clinical observations on the natural history of B. burgdorferi infection and raise several questions that may help to direct future research to better understand the pathogenesis of this infection

Influences of Host Community Characteristics on Borrelia burgdorferi Infection Prevalence in Blacklegged Ticks

Lyme disease is a major vector-borne bacterial disease in the USA. The disease is caused by Borrelia burgdorferi, and transmitted among hosts and humans, primarily by blacklegged ticks (Ixodes scapularis). The ~25 B. burgdorferi genotypes, based on genotypic variation of their outer surface protein C (ospC), can be phenotypically separated as strains that primarily cause human diseases—human invasive strains (HIS)—or those that rarely do. Additionally, the genotypes are non-randomly associated with host species. The goal of this study was to examine the extent to which phenotypic outcomes of B. burgdorferi could be explained by the host communities fed upon by blacklegged ticks. In 2006 and 2009, we determined the host community composition based on abundance estimates of the vertebrate hosts, and collected host-seeking nymphal ticks in 2007 and 2010 to determine the ospC genotypes within infected ticks. We regressed instances of B. burgdorferi phenotypes on site-specific characteristics of host communities by constructing Bayesian hierarchical models that properly handled missing data. The models provided quantitative support for the relevance of host composition on Lyme disease risk pertaining to B. burgdorferi prevalence (i.e. overall nymphal infection prevalence, or NIPAll) and HIS prevalence among the infected ticks (NIPHIS). In each year, NIPAll and NIPHIS was found to be associated with host relative abundances and diversity. For mice and chipmunks, the association with NIPAll was positive, but tended to be negative with NIPHIS in both years. However, the direction of association between shrew relative abundance with NIPAll or NIPHIS differed across the two years. And, diversity (H') had a negative association with NIPAll, but positive association with NIPHIS in both years. Our analyses highlight that the relationships between the relative abundances of three primary hosts and the community diversity with NIPAll, and NIPHIS, are variable in time and space, and that disease risk inference, based on the role of host community, changes when we examine risk overall or at the phenotypic level. Our discussion focuses on the observed relationships between prevalence and host community characteristics and how they substantiate the ecological understanding of phenotypic Lyme disease risk.DB: Burroughs Welcome Fund (1012376) - http://www.bwfund.org, Lyme Research Alliance - http://www. lymeresearchalliance.org, Bay Area Lyme Foundation - http://www.bayarealyme.org, National Institutes of Health (AI076342, AI097137) - https:// www.niaid.nih.gov. PES: USDA National Institute of Food and Agriculture Hatch project (accession number 1005333), “Evolutionary Diversity & Biogeographic Pattern, Reflecting Ecological & Anthropogenic Forces,” through the New Jersey Agricultural Experiment Station, Hatch project NJ17160, https://nifa.usda.gov/. RSO: Environmental Protection Agency (STAR Grant 83377601) to RSO and F. Keesing and the National Science Foundation-National Institutes of Health joint program in the Ecology of Infectious Diseases (EF0813035) to F. Keesing and RS

Allelic Variation Contributes to Bacterial Host Specificity

Understanding the molecular parameters that regulate cross-species transmission and host adaptation of potential pathogens is crucial to control emerging infectious disease. Although microbial pathotype diversity is conventionally associated with gene gain or loss, the role of pathoadaptive nonsynonymous single-nucleotide polymorphisms (nsSNPs) has not been systematically evaluated. Here, our genome-wide analysis of core genes within Salmonella enterica serovar Typhimurium genomes reveals a high degree of allelic variation in surface-exposed molecules, including adhesins that promote host colonization. Subsequent multinomial logistic regression, MultiPhen and Random Forest analyses of known/suspected adhesins from 580 independent Typhimurium isolates identifies distinct host-specific nsSNP signatures. Moreover, population and functional analyses of host-associated nsSNPs for FimH, the type 1 fimbrial adhesin, highlights the role of key allelic residues in host-specific adherence in vitro. Together, our data provide the first concrete evidence that functional differences between allelic variants of bacterial proteins likely contribute to pathoadaption to diverse hosts