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      Genomic insights into the ancient spread of Lyme disease across North America

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          Abstract

          Lyme disease is the most prevalent vector-borne disease in North America and continues to spread. The disease was first clinically described in the 1970s in Lyme, Connecticut, but the origins and history of spread of the Lyme disease bacteria, Borrelia burgdorferi sensu stricto, are unknown. To explore the evolutionary history of B. burgdorferi in North America, we collected ticks from across the United States and southern Canada from 1984 to 2013 and sequenced the largest ever collection of 146 B. burgdorferi s.s. genomes. Here, we show that B. burgdorferi s.s. has a complex evolutionary history with previously undocumented levels of migration. Diversity is ancient and geographically widespread, well predating the Lyme disease epidemic of the last ~40 years, as well as the Last Glacial Maximum ~20,000 years ago. This means the recent emergence of human Lyme disease likely reflects ecological change—climate change and land use changes over the last century—rather than evolutionary change of the bacterium.

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          Most cited references49

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          Improving the accuracy of demographic and molecular clock model comparison while accommodating phylogenetic uncertainty.

          Recent developments in marginal likelihood estimation for model selection in the field of Bayesian phylogenetics and molecular evolution have emphasized the poor performance of the harmonic mean estimator (HME). Although these studies have shown the merits of new approaches applied to standard normally distributed examples and small real-world data sets, not much is currently known concerning the performance and computational issues of these methods when fitting complex evolutionary and population genetic models to empirical real-world data sets. Further, these approaches have not yet seen widespread application in the field due to the lack of implementations of these computationally demanding techniques in commonly used phylogenetic packages. We here investigate the performance of some of these new marginal likelihood estimators, specifically, path sampling (PS) and stepping-stone (SS) sampling for comparing models of demographic change and relaxed molecular clocks, using synthetic data and real-world examples for which unexpected inferences were made using the HME. Given the drastically increased computational demands of PS and SS sampling, we also investigate a posterior simulation-based analogue of Akaike's information criterion (AIC) through Markov chain Monte Carlo (MCMC), a model comparison approach that shares with the HME the appealing feature of having a low computational overhead over the original MCMC analysis. We confirm that the HME systematically overestimates the marginal likelihood and fails to yield reliable model classification and show that the AICM performs better and may be a useful initial evaluation of model choice but that it is also, to a lesser degree, unreliable. We show that PS and SS sampling substantially outperform these estimators and adjust the conclusions made concerning previous analyses for the three real-world data sets that we reanalyzed. The methods used in this article are now available in BEAST, a powerful user-friendly software package to perform Bayesian evolutionary analyses.
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            Detecting Correlated Evolution on Phylogenies: A General Method for the Comparative Analysis of Discrete Characters

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              Genomic sequence of a Lyme disease spirochaete, Borrelia burgdorferi.

              The genome of the bacterium Borrelia burgdorferi B31, the aetiologic agent of Lyme disease, contains a linear chromosome of 910,725 base pairs and at least 17 linear and circular plasmids with a combined size of more than 533,000 base pairs. The chromosome contains 853 genes encoding a basic set of proteins for DNA replication, transcription, translation, solute transport and energy metabolism, but, like Mycoplasma genitalium, it contains no genes for cellular biosynthetic reactions. Because B. burgdorferi and M. genitalium are distantly related eubacteria, we suggest that their limited metabolic capacities reflect convergent evolution by gene loss from more metabolically competent progenitors. Of 430 genes on 11 plasmids, most have no known biological function; 39% of plasmid genes are paralogues that form 47 gene families. The biological significance of the multiple plasmid-encoded genes is not clear, although they may be involved in antigenic variation or immune evasion.
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                Author and article information

                Journal
                101698577
                46074
                Nat Ecol Evol
                Nat Ecol Evol
                Nature ecology & evolution
                2397-334X
                3 December 2018
                28 August 2017
                October 2017
                25 March 2019
                : 1
                : 10
                : 1569-1576
                Affiliations
                [1. ]Department of Epidemiology of Microbial Disease, Yale University, New Haven, CT, 06511, USA
                [2. ]Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
                [3. ]Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, 06511, USA
                [4. ]Department of Ecology, Evolution, and Environmental Biology, Columbia University, New York City, NY, 10027, USA
                Author notes

                Author contributions.

                Conceived of and designed the experiments: KSW, MADW, AC, GC. Performed the experiments and analyzed the data: KSW. Contributed reagents/materials/analysis tools: MADW, AC, GC, KSW. Wrote the manuscript: KSW MADW AC.

                [*]

                These authors contributed equally.

                Correspondence: Katharine S. Walter, Department of Epidemiology of Microbial Disease, Yale University, New Haven, CT, 06511, USA, katharine.walter@ 123456yale.edu
                Article
                NIHMS894064
                10.1038/s41559-017-0282-8
                6431794
                29185509
                3cb276f3-17cc-48e3-81bf-e5b5df7cccdc

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