Simultaneous reconstruction of evolutionary history and epidemiological dynamics from viral sequences with the birth-death SIR model

The evolution of RNA viruses such as HIV, Hepatitis C and Influenza virus occurs so rapidly that the viruses' genomes contain information on past ecological dynamics. Hence, we develop a phylodynamic method that enables the joint estimation of epidemiological parameters and phylogenetic history. Based on a compartmental susceptible-infected-removed (SIR) model, this method provides separate information on incidence and prevalence of infections. Detailed information on the interaction of host population dynamics and evolutionary history can inform decisions on how to contain or entirely avoid disease outbreaks. We apply our Birth-Death SIR method (BDSIR) to two viral data sets. First, five human immunodeficiency virus type 1 clusters sampled in the United Kingdom between 1999 and 2003 are analyzed. The estimated basic reproduction ratios range from 1.9 to 3.2 among the clusters. All clusters show a decline in the growth rate of the local epidemic in the middle or end of the 90's. The analysis of a hepatitis C virus (HCV) genotype 2c data set shows that the local epidemic in the C\'ordoban city Cruz del Eje originated around 1906 (median), coinciding with an immigration wave from Europe to central Argentina that dates from 1880--1920. The estimated time of epidemic peak is around 1970.Comment: Journal link: http://rsif.royalsocietypublishing.org/content/11/94/20131106.ful

Efficient Bayesian inference under the structured coalescent

Motivation: Population structure significantly affects evolutionary dynamics. Such structure may be due to spatial segregation, but may also reflect any other gene-flow-limiting aspect of a model. In combination with the structured coalescent, this fact can be used to inform phylogenetic tree reconstruction, as well as to infer parameters such as migration rates and subpopulation sizes from annotated sequence data. However, conducting Bayesian inference under the structured coalescent is impeded by the difficulty of constructing Markov Chain Monte Carlo (MCMC) sampling algorithms (samplers) capable of efficiently exploring the state space. Results: In this article, we present a new MCMC sampler capable of sampling from posterior distributions over structured trees: timed phylogenetic trees in which lineages are associated with the distinct subpopulation in which they lie. The sampler includes a set of MCMC proposal functions that offer significant mixing improvements over a previously published method. Furthermore, its implementation as a BEAST 2 package ensures maximum flexibility with respect to model and prior specification. We demonstrate the usefulness of this new sampler by using it to infer migration rates and effective population sizes of H3N2 influenza between New Zealand, New York and Hong Kong from publicly available hemagglutinin (HA) gene sequences under the structured coalescent. Availability and implementation: The sampler has been implemented as a publicly available BEAST 2 package that is distributed under version 3 of the GNU General Public License at http://compevol.github.io/MultiTypeTree. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

In-host evolution of Staphylococcus epidermidis in a pacemaker-associated endocarditis resulting in increased antibiotic tolerance

Treatment failure in biofilm-associated bacterial infections is an important healthcare issue. In vitro studies and mouse models suggest that bacteria enter a slow-growing/non-growing state that results in transient tolerance to antibiotics in the absence of a specific resistance mechanism. However, little clinical confirmation of antibiotic tolerant bacteria in patients exists. In this study we investigate a Staphylococcus epidermidis pacemaker-associated endocarditis, in a patient who developed a break-through bacteremia despite taking antibiotics to which the S. epidermidis isolate is fully susceptible in vitro. Characterization of the clinical S. epidermidis isolates reveals in-host evolution over the 16-week infection period, resulting in increased antibiotic tolerance of the entire population due to a prolonged lag time until growth resumption and a reduced growth rate. Furthermore, we observe adaptation towards an increased biofilm formation capacity and genetic diversification of the S. epidermidis isolates within the patient

Genomic Surveillance of Vancomycin-Resistant Enterococcus faecium Reveals Spread of a Linear Plasmid Conferring a Nutrient Utilization Advantage

Healthcare-associated outbreaks of vancomycin-resistant Enterococcus faecium (VREfm) are a worldwide problem with increasing prevalence. The genomic plasticity of this hospital-adapted pathogen contributes to its efficient spread despite infection control measures. Here, we aimed to identify the genomic and phenotypic determinants of health care-associated transmission of VREfm. We assessed the VREfm transmission networks at the tertiary-care University Hospital of Zurich (USZ) between October 2014 and February 2018 and investigated microevolutionary dynamics of this pathogen. We performed whole-genome sequencing for the 69 VREfm isolates collected during this time frame and assessed the population structure and variability of the vancomycin resistance transposon. Phylogenomic analysis allowed us to reconstruct transmission networks and to unveil external or wider transmission networks undetectable by routine surveillance. Notably, it unveiled a persistent clone, sampled 31 times over a 29-month period. Exploring the evolutionary dynamics of this clone and characterizing the phenotypic consequences revealed the spread of a variant with decreased daptomycin susceptibility and the acquired ability to utilize N-acetyl-galactosamine (GalNAc), one of the primary constituents of the human gut mucins. This nutrient utilization advantage was conferred by a novel plasmid, termed pELF_USZ, which exhibited a linear topology. This plasmid, which was harbored by two distinct clones, was transferable by conjugation. Overall, this work highlights the potential of combining epidemiological, functional genomic, and evolutionary perspectives to unveil adaptation strategies of VREfm. IMPORTANCE Sequencing microbial pathogens causing outbreaks has become a common practice to characterize transmission networks. In addition to the signal provided by vertical evolution, bacterial genomes harbor mobile genetic elements shared horizontally between clones. While macroevolutionary studies have revealed an important role of plasmids and genes encoding carbohydrate utilization systems in the adaptation of Enterococcus faecium to the hospital environment, mechanisms of dissemination and the specific function of many of these genetic determinants remain to be elucidated. Here, we characterize a plasmid providing a nutrient utilization advantage and show evidence for its clonal and horizontal spread at a local scale. Further studies integrating epidemiological, functional genomics, and evolutionary perspectives will be critical to identify changes shaping the success of this pathogen. Keywords: Enterococcus faecium; N-acetyl-galactosamine; horizontal gene transfer; linear plasmid; transmission network

Analysis of 3800-year-old Yersinia pestis genomes suggests Bronze Age origin for bubonic plague

该论文通过对青铜器时代的两个鼠疫杆菌分离株进行测序，深入剖析了鼠疫杆菌的历史。德国、俄罗斯、中国和瑞士等多国研究员共同参与了研究。这篇论文的第一作者是德国马克斯-普朗克研究所的考古遗传学专家Maria Spyrou。她和同事从俄罗斯墓穴中埋葬的九名古代人的牙齿样本入手，发现有两人感染鼠疫杆菌。之后，他们从这些个体中分离出距今约3800年的病原菌。在这项新研究中，研究人员利用液相捕获和Illumina鸟枪法测序技术，对青铜器时代的一名男子（RT5）的鼠疫杆菌和人类宿主序列进行测序，其中鼠疫杆菌基因组的平均覆盖度达到32倍。同时，他们还对另一名感染个体（RT6）的分离株进行测序，平均覆盖度为1.9倍。系统发育分析表明，RT5和RT6分离株是共同谱系的一部分，这个谱系的祖先是史上三次瘟疫大流行的罪魁祸首。除了众所周知的中世纪欧洲瘟疫大流行，鼠疫杆菌还曾造成公元6世纪的查士丁尼瘟疫和19世纪的中国大规模鼠疫。 马克斯-普朗克人类历史科学研究所的古病理学专家Kirsten Bos表示，这些结果表明“具有传播潜力的瘟疫存在的时间比我们想象得更久。”Bos是这篇论文的通讯作者之一。【Abstract】The origin of Yersinia pestis and the early stages of its evolution are fundamental subjects of investigation given its high virulence and mortality that resulted from past pandemics. Although the earliest evidence of Y. pestis infections in humans has been identified in Late Neolithic/Bronze Age Eurasia (LNBA 5000–3500y BP), these strains lack key genetic components required for flea adaptation, thus making their mode of transmission and disease presentation in humans unclear. Here, we reconstruct ancient Y. pestis genomes from individuals associated with the Late Bronze Age period (~3800 BP) in the Samara region of modern-day Russia. We show clear distinctions between our new strains and the LNBA lineage, and suggest that the full ability for flea-mediated transmission causing bubonic plague evolved more than 1000 years earlier than previously suggested. Finally, we propose that several Y. pestis lineages were established during the Bronze Age, some of which persist to the present day.We thank Cosimo Posth, Marcel Keller, Michal Feldman and Wolfgang Haak for useful insights to the manuscript, as well as Alexander Immel and Stephen Clayton for computational support. In addition, we are thankful to Guido Brandt, Antje Wissgott and Cäcilia Freund for laboratory support. M.A.S., A.H., K.I.B. and J.K. were supported by the ERC starting grant APGREID, and by the Max Planck Society. C.C.W. was supported by the Max Planck Society and the Nanqiang Outstanding Young Talents Program of Xiamen University. D.K. was supported by a Marie Heim-Vögtlin grant from the Swiss National Science Foundation

Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causesthe infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformaticstools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection,understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to getinsight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for theroutine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemicand evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets anddevelopment of therapeutic strategies. For each tool, we briefly describe its use case and how it advances researchspecifically for SARS-CoV-2.Fil: Hufsky, Franziska. Friedrich Schiller University Jena; AlemaniaFil: Lamkiewicz, Kevin. Friedrich Schiller University Jena; AlemaniaFil: Almeida, Alexandre. the Wellcome Sanger Institute; Reino UnidoFil: Aouacheria, Abdel. Centre National de la Recherche Scientifique; FranciaFil: Arighi, Cecilia. Biocuration and Literature Access at PIR; Estados UnidosFil: Bateman, Alex. European Bioinformatics Institute. Head of Protein Sequence Resources; Reino UnidoFil: Baumbach, Jan. Universitat Technical Zu Munich; AlemaniaFil: Beerenwinkel, Niko. Universitat Technical Zu Munich; AlemaniaFil: Brandt, Christian. Jena University Hospital; AlemaniaFil: Cacciabue, Marco Polo Domingo. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación En Ciencias Veterinarias y Agronómicas. Instituto de Agrobiotecnología y Biología Molecular. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Agrobiotecnología y Biología Molecular; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Chuguransky, Sara Rocío. European Bioinformatics Institute; Reino Unido. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Drechsel, Oliver. Robert Koch-Institute; AlemaniaFil: Finn, Robert D.. Biocurator for Pfam and InterPro databases; Reino UnidoFil: Fritz, Adrian. Helmholtz Centre for Infection Research; AlemaniaFil: Fuchs, Stephan. Robert Koch-Institute; AlemaniaFil: Hattab, Georges. University Marburg; AlemaniaFil: Hauschild, Anne Christin. University Marburg; AlemaniaFil: Heider, Dominik. University Marburg; AlemaniaFil: Hoffmann, Marie. Freie Universität Berlin; AlemaniaFil: Hölzer, Martin. Friedrich Schiller University Jena; AlemaniaFil: Hoops, Stefan. University of Virginia; Estados UnidosFil: Kaderali, Lars. University Medicine Greifswald; AlemaniaFil: Kalvari, Ioanna. European Bioinformatics Institute; Reino UnidoFil: von Kleist, Max. Robert Koch-Institute; AlemaniaFil: Kmiecinski, Renó. Robert Koch-Institute; AlemaniaFil: Kühnert, Denise. Max Planck Institute for the Science of Human History; AlemaniaFil: Lasso, Gorka. Albert Einstein College of Medicine; Estados UnidosFil: Libin, Pieter. Hasselt University; BélgicaFil: List, Markus. Universitat Technical Zu Munich; AlemaniaFil: Löchel, Hannah F.. University Marburg; Alemani

Ancient Bacterial Genomes Reveal a High Diversity of Treponema pallidum Strains in Early Modern Europe

Syphilis is a globally re-emerging disease, which has marked European history with a devastating epidemic at the end of the 15th century. Together with non-venereal treponemal diseases, like bejel and yaws, which are found today in subtropical and tropical regions, it currently poses a substantial health threat worldwide. The origins and spread of treponemal diseases remain unresolved, including syphilis' potential introduction into Europe from the Americas. Here, we present the first genetic data from archaeological human remains reflecting a high diversity of Treponema pallidumin early modern Europe. Our study demonstrates that a variety of strains related to both venereal syphilis and yaws-causing T. pallidum subspecies were already present in Northern Europe in the early modern period. We also discovered a previously unknown T. pallidum lineage recovered as a sister group to yaws- and bejel-causing lineages. These findings imply a more complex pattern of geographical distribution and etiology of early treponemal epidemics than previously understood

BEAST 2:A Software Platform for Bayesian Evolutionary Analysis

We present a new open source, extensible and flexible software platform for Bayesian evolutionary analysis called BEAST 2. This software platform is a re-design of the popular BEAST 1 platform to correct structural deficiencies that became evident as the BEAST 1 software evolved. Key among those deficiencies was the lack of post-deployment extensibility. BEAST 2 now has a fully developed package management system that allows third party developers to write additional functionality that can be directly installed to the BEAST 2 analysis platform via a package manager without requiring a new software release of the platform. This package architecture is showcased with a number of recently published new models encompassing birth-death-sampling tree priors, phylodynamics and model averaging for substitution models and site partitioning. A second major improvement is the ability to read/write the entire state of the MCMC chain to/from disk allowing it to be easily shared between multiple instances of the BEAST software. This facilitates checkpointing and better support for multi-processor and high-end computing extensions. Finally, the functionality in new packages can be easily added to the user interface (BEAUti 2) by a simple XML template-based mechanism because BEAST 2 has been re-designed to provide greater integration between the analysis engine and the user interface so that, for example BEAST and BEAUti use exactly the same XML file format

Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causes the infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformatics tools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection, understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to get insight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for the routine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemic and evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets and development of therapeutic strategies. For each tool, we briefly describe its use case and how it advances research specifically for SARS-CoV-2. All tools are free to use and available online, either through web applications or public code repositories.Peer Reviewe