Manuel Spannagl
email: manuel.spannagl@helmholtz-muenchen.de

Michael Alaux

Matthias Lange

Daniel M Bolser

Kai C Bader

Thomas Letellier

Erik Kimmel

Raphael Flores

Cyril Pommier

Arnaud Kerhornou

Brandon Walts

Thomas Nussbaumer

Christoph Grabmuller

Jinbo Chen

Christian Colmsee

Sebastian Beier

Martin Mascher

Thomas Schmutzer

Daniel Arend

Anil Thanki

Ricardo Ramirez-Gonzalez

Martin Ayling

Sarah Ayling

Mario Caccamo

Klaus F X Mayer

Uwe Scholz

Delphine Steinbach

Hadi Quesneville

Paul J Kersey

R Ramirez- Gonzalez

transPLANT Resources for Triticeae Genomic Data

Keywords: BAC, bacterial artificial chromosome, CSS, chromosome survey sequence, EBI, European Bioinformatics Institute, EST, expressed sequence tag, GO, gene ontology, IBSC, International quantitative trait loci, RNA-seq, RNA sequencing, SNP, single nucleotide polymorphism

The genome sequences of many important Triticeae species, including bread wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.), remained uncharacterized for a long time because their high repeat content, large sizes, and polyploidy. As a result of improvements in sequencing technologies and novel analyses strategies, several of these have recently been deciphered. These efforts have generated new insights into Triticeae biology and genome organization and have important implications for downstream usage by breeders, experimental biologists, and comparative genomicists. transPLANT (http://www.transplantdb.eu) is an EU-funded project aimed at constructing hardware, software, and data infrastructure for genome-scale research in the life sciences. Since the Triticeae data are intrinsically complex, heterogenous, and distributed, the transPLANT consortium has undertaken efforts to develop common data formats and tools that enable the exchange and integration of data from distributed resources. Here we present an overview of the individual Triticeae genome resources hosted by transPLANT partners, introduce the objectives of transPLANT, and outline common developments and interfaces supporting integrated data access.

C rops from the tribe of the Triticeae, including wheat, barley, and rye (Secale cereale L.), account for some of the most important nutritional resources in the human diet. Until recently, genomics-informed breeding approaches were limited in Triticeae species, as few genomic data were available. This lack can mainly be attributed to the inherent complexity of their genomes and genetics, especially in species of high economic interest such as barley and bread wheat. With estimated haploid and triploid sizes of 5.3 and 17.1 Gb, respectively, the genomes of these (and other Triticeae) species significantly exceed the size of the haploid human genome (~3 Gb). The high overall repeat content and, in bread wheat,

the pl ant genome  m arch 2016  vol. 9, no. 1 the allohexaploid genome structure, further complicate every aspect of the collation and analysis of genomic data, including sequencing, assembly, gene calling, and functional analysis.

Nevertheless, great progress has recently been made in deciphering the genome sequences and gene content of these species through coordinated international collaboration. The International Barley Genome Sequencing Consortium (IBSC) reported a draft genome sequence for the barley cultivar Morex obtained using mainly next-generation sequencing technologies, chromosome sorting, an integrated physical and genetic map, gene predictions, and analysis of the transcriptional landscape (IBSC, 2012). For bread wheat, 5 454 whole-genome sequencing was used to generate assemblies of homeologous genes and to analyze the gene content of hexaploid wheat in a reference-directed approach [START_REF] Brenchley | Analysis of the bread wheat genome using wholegenome shotgun sequencing[END_REF]. In 2014, the International Wheat Genome Sequencing Consortium (IWGSC) released draft genome sequence assemblies for all wheat chromosome arms (again, following the use of a physical sorting strategy before sequencing), with a total of 124,201 annotated gene models and supporting transcriptomics data (IWGSC, 2014). Meanwhile, a French-led consortium has generated a reference sequence and gene annotation for wheat chromosome 3B [START_REF] Choulet | Structural and functional partitioning of bread wheat chromosome 3[END_REF]. Genomic data for a number of additional Triticeae species has also been generated recently, including the bread wheat subgenome progenitors Aegilops tauschii Coss. [START_REF] Jia | Aegilops tauschii draft genome sequence reveals a gene repertoire for wheat adaptation[END_REF], A. sharonensis Eig and A. speltoides Tausch (IWGSC, 2014), and T. urartu Tumanian ex Gandilyan [START_REF] Ling | Draft genome of the wheat A-genome progenitor Triticum urartu[END_REF], and the tetraploid T. turgidum L. subsp. durum (Desf.) Husn. (pasta wheat) (IWGSC, 2014). GenomeZippers, a synteny-enabled anchoring approach, have been constructed for the Triticeae species barley [START_REF] Mayer | Unlocking the barley genome by chromosomal and comparative genomics[END_REF]), wheat (IWGSC, 2014), A. tauschii [START_REF] Luo | A 4-gigabase physical map unlocks the structure and evolution of the complex genome of Aegilops tauschii, the wheat D-genome progenitor[END_REF], and rye [START_REF] Martis | Reticulate evolution of the rye genome[END_REF], facilitating the positioning of 10,000s of genes in the absence of finished genome sequences. While the sequence of all these species is incompletely assembled, significant progress has been made toward assembling the gene space, assigning contigs to chromosomes, and ordering them. The data is already sufficient to support analyses of gene families, variation within populations, large scale synteny, and association of genotype with phenotype.

The availability of genomic data from multiple Triticeae species is expected to facilitate powerful comparative genomics approaches and help to enhance understanding of Triticeae biology and evolution. However, the use of multiple approaches to genome sequencing and assembly (e.g., chromosome sorting, GenomeZippers, and genome survey sequencing), the variety of associated data types (e.g., gene predictions, expression data, and molecular markers), and the existence of alternative coordinate systems (e.g., genetic map, physical map, and numerical position in molecular sequence) can make it difficult for users to combine different data sets easily and correctly. Storage, integration, and visualization of these heterogenous and complex data are essential to enable efficient research.

Here we describe Triticeae genome data resources maintained by partners in the EU-funded transPLANT project. The transPLANT project aims at producing an integrated, coherent data infrastructure shared among dispersed expert resources with strong interconnections between them (including cross-linking and the use of common formats, tools, and datasets) designed to make it easy for users to switch between different resources according to which one best addresses their current point of interest.

Results

transPLANT

The transPLANT project (Table 1, reference no. 1 [Table 1.1]; hereafter, this format is used to reference Table 1) is an integrated infrastructure funded by the Framework 7 program of the European Union. It brings together 11 partners from seven countries with the aim of developing common standards, data, and technologies in the plant genomics area. A major focus of the work is variation data and the development of tools to organize, archive, and analyze this. Another major focus is the definition and use of common reference sequences so that annotation from different resources can be shared and compared. A third focus is the development of a distributed query infrastructure to provide a common point of entry to data held in multiple, dispersed resources.

In the context of Triticeae data, five transPLANT partners (The Plant Genome and Systems Biology unit at the Helmholtz Center Munich [PGSB], the European Bioinformatics Institute [EBI], the Unité de Recherche Génomique Info at the Institut National de la Recherche Agronomique [URGI], the Leibniz Institute of Plant Genetics and Crop Plant Research [IPK] and The Genome Analysis Centre [TGAC]) are involved and interacting with other partners engaged in the international consortia (IWGSC and IBSC) coordinating the sequencing and assembly of these genomes. Figure 1 provides an overview over the respective Triticeae data resources hosted by transPLANT partners together with available species and shared search interfaces and services. Data from barley, wheat, and other species have been exported to common data formats in accordance with established standards for data representation, exchanged between partners, and synchronized across the distributed transPLANT resources. Moreover, partners have collaborated in comparative analysis of Triticeae genomes to study ancient duplications, polyploidy, and syntenic relationships.

As a result, end users benefit from a number of specialized tools and interfaces operating on synchronized Triticeae genome data hosted and exchanged by the transPLANT partners. This includes an interface embedded in the transPLANT web portal enabling keyword searches over the data inventories of many transPLANT partners as well as extensive cross-linking between partner resources and the provision of web services and data warehouses. To assist users and communities in identifying data critical for their research, transPLANT provides a registry for genome resources (not restricted to Triticeae) and all data is discoverable within a single, integrated search. The registry can be accessed at the websites indicated in Table 1.2 and 1.3 (see also Fig. 2B).

The integrated search is available from the website indicated in Table 1.1.

Plant Genome and Systems Biology PlantsDB: A Platform for the Comparative Analysis of Triticeae Genome Data

Plant Genome and Systems Biology (PGSB, formerly MIPS) PlantsDB provides a platform for the integration, visualization, and comparative analysis of plant genome data. Currently, genome data from 12 different plant species are available in the public domain of PGSB PlantsDB (Fig. 2A). Access points include search and browse interfaces, BioMoby webservices [START_REF] Wilkinson | BioMOBY successfully integrates distributed heterogeneous bioinformatics Web Services. The PlaNet exemplar case[END_REF], FTP download server, as well as visualization tools. With ongoing involvement in Triticeae genome sequencing and annotation initiatives such as IBSC (IBSC, 2012) and IWGSC (IWGSC, 2014), a major focus of PlantsDB was put on the representation and analysis of complex Triticeae genome data.

To compensate for the lack of reference chromosome sequences, GenomeZippers were constructed for many Triticeae species including barley, wheat, and rye. The GenomeZipper concept integrates data from chromosome sorting, second generation sequencing, array hybridization, and systematic exploitation of conserved (Table 1.22). (B) transPLANT genome resources registry giving access to more than 300 different plant genome resources and databases (Table 1.3). (C) GenomeZipper overview representation for the barley genome (Table 1.23). (D) Visualization of syntenic relationships between the genome sequences of Aegilops tauschii and barley within the CrowsNest tool (Table 1.5).

synteny with model grasses to construct virtual ordered gene maps [START_REF] Mayer | Gene content and virtual gene order of barley chromosome 1H[END_REF][START_REF] Mayer | Unlocking the barley genome by chromosomal and comparative genomics[END_REF]. Besides batch download via FTP, all GenomeZipper data has been integrated into the PGSB PlantsDB database scheme. To assist the interactive exploration of GenomeZippers and the search for anchored elements such as expressed sequence tags (ESTs), genetic markers, and full-length complementary DNA, interfaces for querying and browsing the Geno-meZippers for barley, wheat, and rye were constructed (accessible from Table 1.4; Fig. 2C).

Between the genomes of many monocotyledonous plants, including major Triticeae crops and model plants, gene order appears to be conserved over long chromosomal stretches (synteny). This facilitates the potential transfer of knowledge from model plants such as Brachypodium distachyon (L.) Beauv. or rice (Oryza sativa L.) to the more complex Triticeae crops such as barley and wheat. To assist interactive exploration and visualization of syntenic regions and genes between grass models and Triticeae crop species, the CrowsNest tool (Table 1.5) was developed and populated with the genomes of rice, sorghum [Sorghum bicolor (L.) Moench], B. distachyon, barley, and A. tauschii, the diploid progenitor of the D sub-genome of hexaploid bread wheat. Figure 2D shows a screenshot from the CrowsNest tool visualizing synteny between A. tauschii and barley on a whole-genome scale.

Wheat genomic subassembly sequences generated in a reference-directed approach [START_REF] Brenchley | Analysis of the bread wheat genome using wholegenome shotgun sequencing[END_REF] were integrated with their corresponding genes from B. distachyon, sorghum, rice, and barley. Interfaces to query this data include a BLAST server to search for homologous wheat sequences as well as search for reference genes and associated wheat sequences. Genes predicted from chromosome-sorted wheat genome sequence generated within the IWGSC (IWGSC, 2014) have been integrated into PGSB PlantsDB and cross-referenced with the corresponding repositories GnpIS Wheat and Ensembl Plants.

To visualize and search the integrated barley physical and genetic maps, dedicated instances of GBrowse and CrowsNest were set up and in cooperation with IPK Gatersleben populated with markers, bacterial artificial chromosome (BAC) end sequences, BAC sequences, and physical map information. Gene expression data from barley (IBSC, 2012) has been integrated into the RNASeq-ExpressionBrowser [START_REF] Nussbaumer | RNASeqExpressionBrowser: A web interface to browse and visualize high-throughput expression data[END_REF] and can be queried by keyword, sequence similarity search (BLAST), or gene ontology (GO) and Interpro term and domain.

Triticeae Genome Data in Ensembl Plants

Ensembl Plants (Table 1.6) is an integrative web portal for plant genomic data, developed by the EBI. The portal provides interactive and programmatic access to data from 39 species through a variety of interfaces including web browser, Perl and RESTful Application Programming Interfaces, FTP, a publicly accessible relational database server, and a data-mining tool implemented using the data-warehousing framework BioMart optimized for gene and variant-centric queries. In addition to participating in transPLANT, coordination with efforts in the United States is achieved through a formal collaboration with the Gramene project (http://www.gramene.org).

Currently, four Triticeae genomes (among 20 cereal genomes) are available in Ensembl Plants: bread wheat, two of its diploid progenitors, A. tauschii and T. urartu, and barley. In the case of both wheat and barley; additional information (from genetic and physical maps) has been used to assign the genomic contigs to chromosomes and locate them within them. For barley, many of the contiguous sequences generated and assembled by the IBSC have been binned into located clusters according to evidence from the genetic and physical maps, and this information is used to construct a chromosome level view in Ensembl. The initial assembly has recently been revised using POPSEQ (Mascher et al., 2013a) data generated by the IPK. Whole-genome alignments have been performed against B. distachyon, rice, bread wheat, and the bread wheat progenitor genomes; collections of barley and wheat ESTs and RNA-sequencing (RNA-seq) reads have been aligned to the barley reference. In addition, intervarietal single nucleotide polymorphisms (SNPs) are represented for eleven varieties of barley as well as sites of variation between wild barley (H. spontaneum) and the barley reference.

The core wheat data represented is the chromosome survey sequence (CSS) generated, assembled, and annotated by the IWGSC. However, the CSS assembly of chromosome 3B has been replaced by the BAC-by-BAC assembly constructed by [START_REF] Choulet | Structural and functional partitioning of bread wheat chromosome 3[END_REF]. In addition to sequence assemblies and gene models, a number of additional data sets have been aligned to the survey sequence, including the complete genomes of B. distachyon and rice, wheat unigene clusters from NCBI, and wheat RNA-seq data deposited in the International Nucleotide Sequence Database Collaboration archives. The wheat genome assemblies previously generated by [START_REF] Brenchley | Analysis of the bread wheat genome using wholegenome shotgun sequencing[END_REF] have also been aligned to the survey sequence B. distachyon and barley. In addition, a collection of 900,000 polymorphisms from CerealsDB (http://www.cerealsdb. uk.net) have been included in the resource as well as data from the wheat HapMap project [START_REF] Jordan | A haplotype map of allohexaploid wheat reveals distinct patterns of selection on homoeologous genomes[END_REF]. The chromosome survey sequence (and its annotations), in addition to the complete EST set, are available to search via BLAST and other search alignment algorithms.

For all gene models in Ensembl Plants, functional annotation is inferred using GO, InterPro, and homology metrics. Additionally, the evolutionary history of each protein-coding gene is inferred from comparisons to other plant species, and gene trees and protein alignments are available to browse (see Fig. 3) and download. The three bread wheat genomes have additionally been aligned to each other and linked to assertions of homeology derived from the gene tree analysis to provide supporting evidence. These alignments have been used to identify sites of variation (single nucleotide variants and insertion-deletions) between the A, B, and D genomes (see Fig. 4).

In addition, transcriptome data from another bread wheat precursor species, T. monoccocum [START_REF] Fox | De novo transcriptome assembly and analyses of gene expression during photomorphogenesis in diploid wheat Triticum monococcum[END_REF], have been aligned to the hexaploid reference.

the pl ant genome  m arch 2016  vol. 9, no. 1

Triticeae Genome Data and Tools at the Unité de Recherches en Génomique Info

The Official Wheat International Wheat Genome Sequencing Consortium Portal Unité de Recherches en Génomique Info has been chosen by the IWGSC to be the repository for wheat genomic sequences and physical maps (Table 1.7).

To allow users to download, display, and query the IWGSC sequences and physical map data, a section dedicated to wheat genomics data, the sequence repository (Table 1.8; Fig. 5A), has been set up. Data stored in the sequence repository includes the wheat survey sequence, the chromosome reference sequence (chromosome 3B), the genes and annotations (gene models, GenomeZipper, and POPSEQ), the physical maps, the RNA-Seq, and the variations (HapMap) data.

Users can display the sequence annotation of the 3B reference sequence and the survey sequence in dedicated browsers. The physical maps browser (Table 1.9; Fig. 5B) is a customized instance of the GBrowse tool developed by the GMOD community [START_REF] Stein | The generic genome browser: A building block for a model organism system database[END_REF].

The T. aestivum sequence data, diploid, and tetraploid wheat species sequence data (e.g., T. durum, T. monococcum, T. urartu, A. speltoides, A. sharonensis, and A. tauschii) are searchable using a BLAST tool (Table 1.10). The BLAST server is a customized version of the ViroBLAST tool developed by the University of Washington [START_REF] Deng | ViroBLAST: A stand-alone BLAST web server for flexible queries of multiple databases and user's datasets[END_REF].

It also hosts the supplementary data attached to IWGSC publications and we are currently developing a page dedicated to assist the upcoming reference chromosome assemblies.

Data Warehouse for Wheat Chromosome 3B

To be able to connect reference sequence data from chromosome 3B [START_REF] Choulet | Structural and functional partitioning of bread wheat chromosome 3[END_REF] with genetics and phenomics data, the Wheat3BMine data warehouse (Table 1.11; Fig. 5C) was developed in the framework of transPLANT. The warehouse is implemented using InterMine technology that provides a fast, flexible, and user friendly access to integrated data by multiple ways: a browser, a query builder, and a region search tool. Wheat3BMine users can filter their favorite features, save their own queries, and export results in many different formats (GFF3, BED, or XML). An online documentation and precomputed queries are also available.

The data warehouse contains heterogeneous data and is gene centric. In fact, the typical gene card centralizes relevant information like gene function, ontology terms, and overlapping features. Wheat3BMine provides access to genomic annotation data (genes, mRNA, polypeptides, and transposable elements), polymorphisms data (markers), genetic mapping data (quantitative trait loci [QTL], meta-QTL), and phenotyping data. Moreover, useful links are available from a gene card to the wheat 3B genome browser [START_REF] Choulet | Structural and functional partitioning of bread wheat chromosome 3[END_REF] and to additional details in GnpIS.

Wheat Data in the GnpIS Information System

GnpIS [START_REF] Steinbach | GnpIS: An information system to integrate genetic and genomic data from plants and fungi[END_REF]) is an information system that integrates genomic and genetic data for plants and fungi. A "wheat" filter was implemented within GnpIS that allows interconnecting the wheat genomic data the pl ant genome  m arch 2016  vol. 9, no. 1 evidence-based annotation system enable time-efficient and comprehensive information retrieval. An artificial neural network incorporating user feedback and behavior tracking allows relevance sorting of results. Because this enhanced relevance ranking is one of the major innovations to explore millions of database records, a special focus has been set to its training and the inclusion of user feedback. The current LAILAPS release comprises about 91 million indexed database records of trait knowledge within 13 major life science data collections and more than 60 million associations to -omics data sets.

To provide an up-to-date user ergonometry, the front end features an interactive query assistance that suggests spelling correction as well as semantic query expansion. This feature makes use of PubMed abstracts to learn vectors of similar words and phrases. Queries are expressed as keyword or phrases that are spell corrected. As query results, a condensed list of relevant hits is rendered that includes a short excerpt of relevant text positions and a list of annotation links to annotated genes in plant genomes. A comprehensive result filter panel and the suggestion of semantic follow-up queries rounds off the detailed above with the germplasm, markers, QTLs, SNPs, expression, and phenotypes data. Moreover, association and genomic selection data are in the process of integration into the information system. The wheat data in GnpIS (Table 1.7; Fig. 5D) can be queried using the quick search tool (Google-like search), advanced search tool (Wheat3BMine), and the dedicated web interfaces developed in Java and Google Web Toolkit.

Triticeae Genome Data and Tools at the Leibniz Institute of Plant Genetics and Crop Plant Research Gatersleben

LAILAPS Search Engine

LAILAPS [START_REF] Esch | LAILAPS: The plant science search engine[END_REF]) is an integrated information retrieval system to link plant genomic data in the context of phenotypic traits for a detailed forward genetic research (Table 1.12; Fig. 6). LAILAPS is developed in the framework of the transPLANT project and allows exploratory search for candidate genes linked to specific traits over a loosely integrated system of indexed and interlinked genome databases. Query assistance and an query refinement features. Once the user identifies interesting records, he is guided to the most relevant genomic dataset and is able to rate its quality. In turn, this enables LAILAPS relevance prediction system to improve the relevance prediction network.

Barley BLAST Server

The IPK hosts a BLAST server (Table 1.13), providing homology searches against the complete barley sequence data sets published in IBSC (2012). This comprises the whole-genome shotgun assemblies of the cultivars Morex, Barke, and Bowman as well as the high-and lowconfidence gene sets. The latest POPSEQ anchoring data (Mascher et al., 2013a) was also integrated in addition to the barley exome capture targets (Mascher et al., 2013b).

BARLEX

The recent progress in sequencing and mapping technology has facilitated the construction of advanced genomics resources in species with large and complex crop genomes like barley. During such genome sequencing projects, the integration of large volumes of diverse information and data from disparate sources is an open issue. Existing genome browsers are not well adapted to this task, as they expect all genomic features to be anchored to a single linearly ordered reference sequence. The IPK provides the barley genome explorer, BARLEX [START_REF] Colmsee | BARLEX: The Barley Draft Genome Explorer[END_REF], as a central unified repository for the genomic resources of barley. BARLEX is centered on the genomewide physical map of barley and links it to an annotated whole-genome shotgun assembly and dense genetic Figure 6. The LAILAPS web interface, illustrating the search and results page. The text box (A) enables an interactive and spelling corrected keyword query submission. After query was executed successfully, a list of semantically related phrases is provided for query refinement (B). The query results can be either downloaded as a Microsoft Excel sheet (C) or interactively explored. For this, all relevant hits are displayed as short excerpts in the result panel (D). Connected to each hit is a list of links to associated genomic data (E). Those links can either refer to genome data directly (green) or reflect an indirect, transitive relationship (red). The left hand filter panel enables to restrict the results by fact databases (F1), linked genome databases (F2), direct or indirect linked gene annotations (F3), or synonyms (F4).

the pl ant genome  m arch 2016  vol. 9, no. 1 maps. A web-based interface presents data in tabular and graphical format and associates all information and published sequence data with shotgun assemblies, repeat annotations using KMasker (see [START_REF] Schmutzer | Kmasker: A tool for in silico prediction of single-copy FISH probes for the large-genome species Hordeum vulgare[END_REF], physical contigs, and annotated genes. A novel graphbased visualization strategy was implemented to show overlaps between adjacent BACs based on fingerprint and sequence data. BARLEX is publicly accessible at the website listed in Table 1.14 and is directly connected to the IPK Barley BLAST server as well as the LAILAPS system.

e!DAL: Plant Genomics and Phenomics Research Data Repository

The IPK is hosting a plant genomics and phenomics research data repository, which is based on the e!DAL data sharing and publication infrastructure [START_REF] Arend | e!DAL: A framework to store, share and publish research data[END_REF]. It features the publication of plant research data that is out of scope of existing domain databases, too huge, or less structured. In compliance to international standards, such as DOI, DataCite, and OpenAIRE, plant genomic and phenotpyic datasets are published. In a particular focus are studies of plant genetic resources from the system plant from the root to bloom and seed, as well from sequence analysis to systems biology. Examples are genomic datasets of Triticeae (DOIs: 10.5447/IPK/2015/0, 10.5447/IPK/2015/1, and 10.5447/IPK/2015/2).

Triticeae Genome Data and Tools at The Genome Analysis Centre

The TGAC Browser is an open-source genomic browser developed to visualize genome annotations such as genes, variations, and markers for species whose reference sequence may be contiguous or highly fragmented; the IBSC's barley genome and the wheat CSS represent two such fragmented references (Fig. 7A,7B). Traditional datatypes, such as the reference assembly and gene annotations, reside in an Ensembl schema database. Larger datasets are stored in standard file formats such as SAM, BAM, bigwig, and VCF. The TGAC Browser has an integrated BLAST functionality, essential for identifying and accessing regions of interest in fragmented genomes, and an interface to enable manual annotation of genomic features. Homeologous genes can also be explored through the Aequatus browser (Fig. 7C).

Through collaboration with CerealsDB, the TGAC wheat browser displays the mapped SNP markers from the 90K iSelect and Axiom arrays against the IWGSC chromosome survey sequence contigs. The TGAC Browsers are also available for the barley genome and barley physical map, which display the minimum tile path and BAC-end sequences. All browsers can be accessed at Table 1.15.

To tackle the issue of marker design for polyploid genomes, we have developed PolyMarker, an automated bioinformatics pipeline for SNP assay development that increases the probability of generating homeologuespecific assays for polyploid wheat [START_REF] Ramirez-Gonzalez | PolyMarker: A fast polyploid primer design pipeline[END_REF]. PolyMarker (Fig. 7D) generates a multiple alignment between the target SNP sequence and the IWGSC chromosome survey sequences (IWGSC, 2014) for each of the three wheat genomes. It then generates a mask with informative positions, which are highlighted with respect to the target genome allowing homeologuespecific primer design. The PolyMarker site (Table 1.16) provides predesigned primers for the iSelect 90K chip and 820K Axiom markers.

For more information and community support for these resources TGAC hosts the Grassroots genomics website (Table 1.17).

Discussion

Outlook Great progress has been made recently in sequencing, annotating, and analyzing the complex genomes of Triticeae species including bread wheat and barley. The data generated has great potential for applications in plant breeding, experimental plant biology, and comparative genomics. However, to make the best possible use of the large and heterogeneous data sets produced, data archiving, integration, visualization, and access are essential. A variety of platforms provide different types of analysis and presentations, but it can be difficult for users to use these resources in combination. Many of the partners within the transPLANT project are actively involved in past or ongoing Triticeae genome initiatives and the development of resources. A major focus of the project is ensuring interoperability to maximize their collective value.

Critical to this is the development of common standards and formats. transPLANT partners use accepted standards to share and disseminate data wherever possible and are involved in ongoing efforts to standardize plant phenotypic data and metadata (that is, the description of material, experimental conditions, and results [START_REF] Krajewski | Towards recommendations for metadata and data handling in plant phenotyping[END_REF]). We have also been working with common data mining interfaces (BioMart [START_REF] Smedley | The BioMart community portal: An innovative alternative to large, centralized data repositories[END_REF], InterMine [START_REF] Smith | InterMine: A flexible data warehouse system for the integration and analysis of heterogeneous biological data[END_REF]), and developing RESTful web services to support integrative programmatic access to data. We have also been developing cloud computing environments to support downstream analyses. This has translated into a number of concrete benefits for end users working with complex Triticeae genome data, including the following:

• Extensive and standardized data exchange and synchronization between partners; all data is served on common reference sequence, and portable annotation tracks can be visualized at different sites The result of these efforts is a rich collection of interfaces using common reference data, searchable through a single entry point at the central transPLANT web hub. They also simplify the identification of suitable datasets and databases for research using Triticeae genome data, assist in data acquisition, and provide powerful tools to analyze data in the context of other plant species. The training videos provide example use cases illustrating how users can take advantage of different resources in combination to interrogate the data and perform complex analyses.

With the expected emergence of additional genome sequence data from the Triticeae, the framework for data integration, exchange, and aggregation established within the transPLANT project will help to address the challenges involved with even more distributed data repositories and heterogeneous data types. In this context, a project has started recently with the objective to set up an International Wheat Information System (WheatIS) to support the wheat research community. The main objective is to provide a single-entry web-based system to access the available data resources and bioinformatics tools. The WheatIS project is an international project lead by an Export Working Group of the Wheat Initiative (Table 1.20). The Wheat Initiative is supported by the G20 Agricultural Ministers to coordinate worldwide research efforts in the fields of wheat genetics, genomics, physiology, breeding and agronomy.

The WheatIS project is driven by a network of 21 experts from Australia, Canada, France, Germany, Mexico, United States, and United Kingdom that congregates a group of volunteers willing to participate in the WheatIS project. The WheatIS (Table 1.21) will operate as a hub integrating wheat data produced and submitted to the public repositories by the community, extending the model and technologies established in transPLANT for the coordination of dispersed resources.

Figure 1 .

 1 Figure 1. Overview of Triticeae genome resources and services provided by transPLANT partners. Species for which data is available in the respective database systems are given under the resource names. Details on data types and tools and modes of access are given in the individual partner sections. Upper panels illustrate both the transPLANT integrated search tool (enabling centralized keyword searches over the data inventories of many transPLANT partners) as well as the LAILAPS search engine (linking plant genomic data to phenotypic traits).

Figure 2 .

 2 Figure 2. Triticeae and transPLANT resources hosted by PGSB PlantsDB. (A) Database overview and PlantsDB entry page (Table1.22). (B) transPLANT genome resources registry giving access to more than 300 different plant genome resources and databases (Table1.3). (C) GenomeZipper overview representation for the barley genome (Table1.23). (D) Visualization of syntenic relationships between the genome sequences of Aegilops tauschii and barley within the CrowsNest tool (Table1.5).

Figure 3 .

 3 Figure3. A gene family conserved with 1:1 orthology over 21 Poaceae genomes as visualized in Ensembl Plants. The three bread wheat genes are highlighted in red and blue, and the chromosome (and subgenome) are indicated in the prefix of the gene name (e.g., Traes_4AL_X is the name of a gene from the long arm of chromosome 4 in the A genome). The most related genes are those of the bread wheat precursors, followed by barley. Genes from more distant Poaceae species are located below.

Figure 4 .

 4 Figure 4. Homeologous regions from the bread wheat A, B, and D genomes as visualized in Ensembl Plants. (A) The top panel shows the annotations made on three regions of contiguous sequence. (B) The lower panel is centered on the homeologous genes and shows gene structures, intervarietal polymorphisms and interhomeologous variants.

Figure 5 .

 5 Figure 5. Triticeae resources hosted by URGI. (A) Wheat@URGI website: IWGSC Sequence Repository page (Table 1.8). (B) Wheat physical maps browser (Table 1.9). (C) Wheat3BMine tool homepage (Table 1.11). (D) Wheat data search in GnpIS: quick search, advanced tool and dedicated web interfaces (Table 1.7).

Figure 7 .

 7 Figure 7. The Genome Analysis Centre (TGAC) wheat genome resources. (A) Wheat TGAC Browser: TGAC browser (Table 1.24) showing Triticum aestivum scaffold IWGSC_2AS_scaff_5306489, with SNPs from 820K Axiom array and gene annotations from MIPS/PGSB. (B) TGAC Browser Barley Physical Map: TGAC browser showing the barley physical map for fingerprint (FPC) contig 20 (Table 1.25). MTP BACs are highlighted in blue. (C) Aequatus Browser (Table 1.26) showing Brachypodium distachyon gene BRADI5G04777.1 and homologous genes from Triticum aestivum, Aegilops tauschii, Oryza sativa, and Triticum uratu. (D) Primers designed by PolyMarker (Table 1.16).The webpage highlights the designed primers for validation that can be downloaded in spreadsheet formats.

Table 1 .

 1 List of URLs for transPLANT Triticeae resources, tools, and websites. References to specific URLs are given in the format Table1. REFERENCE no. (e.g. Table1.1) throughout the text.

	Reference no.	Service provider	URL	Short description
	1	transPLANT	http://www.transplantdb.eu	transPLANT web hub
	2	transPLANT	http://www.transplantdb.eu/resource-registry	transPLANT resource registry
	3	PGSB	http://pgsb.helmholtz-muenchen.de/plant/transplant/genomeResources.jsp	transPLANT resource registry mirror at PGSB
	4	PGSB	http://pgsb.helmholtz-muenchen.de/plant/triticeae/index.jsp	PlantsDB Triticeae homepage
	5	PGSB	http://pgsb.helmholtz-muenchen.de/plant/crowsNest/index.jsp	PlantsDB CrowsNest tool
	6	EBI	http://plants.ensembl.org	Ensembl Plants homepage
	7	INRA	http://wheat-urgi.versailles.inra.fr/	INRA URGI wheat database
	8	INRA	http://wheat-urgi.versailles.inra.fr/Seq-Repository	IWGSC wheat sequence repository
	9	INRA	https://urgi.versailles.inra.fr/gb2/gbrowse/wheat_phys_pub	Wheat physical maps browser
	10	INRA	https://urgi.versailles.inra.fr/blast/	URGI wheat BLAST search tool
	11	INRA	http://urgi.versailles.inra.fr/Wheat3BMine/	Wheat 3B data warehouse
	12	IPK	http://lailaps.ipk-gatersleben.de	LAILAPS search engine
	13	IPK	http://webblast.ipk-gatersleben.de/barley	IPK barley BLAST server
	14	IPK	http://barlex.barleysequence.org	Barlex home page
	15	TGAC	www.tgac.ac.uk/tools-resources	TGAC tools and resources homepage
	16	TGAC	http://polymarker.tgac.ac.uk	TGAC Polymarker
	17	TGAC	http://www.tgac.ac.uk/grassroots-genomics	TGAC grassroots genomics website
	18	transPLANT	http://www.transplantdb.eu/training-resources	transPLANT user training resources
	19	transPLANT	http://www.transplantdb.eu/videos	transPLANT user training videos
	20	WheatIS	http://www.wheatinitiative.org/tools/wheat	Wheat Initiative website
	21	WheatIS	www.wheatis.org	Wheat Information System homepage
	22	PGSB		PlantsDB entry page
	23	PGSB		PlantsDB GenomeZipper view
	24	TGAC

http://pgsb.helmholtz-muenchen.de/plant/plantsdb.jsp http://pgsb.helmholtz-muenchen.de/plant/barley/gz/tablejsp/index.jsp http://browser.tgac.ac.uk/wheat/ TGAC wheat browser 25 TGAC http://browser.tgac.ac.uk/barley_phys/ TGAC physical map browser 26 TGAC http://browser.tgac.ac.uk/wheat_compara/ TGAC Aequatus browser

Table 1 .

 1 8). (B) Wheat physical maps browser (Table1.9). (C) Wheat3BMine tool homepage (Table1.11). (D) Wheat data search in GnpIS: quick search, advanced tool and dedicated web interfaces (Table1.7).

•

  Extensive cross-linking between Triticeae genome resources and tools • Provision of programmatic access to databases via APIs or web services • Comprehensive online user training material available for download, covering both resource usage and wider analytical approaches, developed during a series of hands-on workshops (Table 1.18) • Integrated training videos providing an overview of available resources (Table 1.19)

Acknowledgments

All authors and institutions would like to acknowledge funding of the transPLANT project by the European Commission within its 7th Framework Programme, under the thematic area Infrastructures, contract number 283496. EBI acknowledges funding from the United Kingdom Biotechnology and Biological Sciences Research Council grants BB/ I008071/1 and BB/J00328X/1, and grant 52930112 from the United States National Science Foundation. The IPK resources Barley Blast Server, BARLEX, and Kmasker e!DAL were supported by the Leibniz Association (WGL) in the context of the Pakt für Forschung und Innovation/ WGL and the German Federal Ministry of Education and Research (BMBF) in the frame of the projects BARLEX (FKZ 0314000A), and TRITEX (FKZ 0315954A) and DPPN (FKZ 031A053B). URGI likes to acknowledge funding from INRA, french Research National Agency (ANR-09-GENM-025-003) 3BSEQ project, Investment for the Future (ANR-10-BTBR-03, France AgriMer, FSOV) BreedWheat project, European commission within 7th Framework Program TriticeaeGenome (KBBE-212019) and WHEALBI (FP7-613556) projects. TGAC likes to acknowledge funding from the Biotechnology and Biological Sciences Research Council grants BB/L002124/1 and BB/L024144/1. RRG is supported by a Norwich Research Park PhD Studentship and The Genome Analysis Centre Funding and Maintenance Grant. PGSB acknowledges funding from the German Federal Ministry of Education and Research (BMBF) in the frame of the projects BARLEX (FKZ 0314000A), and TRI-TEX (FKZ 0315954A) as well as Deutsche Forschungsgemeinschaft (DFG) funding to project SFB924 Molecular mechanisms regulating yield and yield stability in plants.


domain: sdv sde

text:
Antonios Zampounis

Sandrine Pigné

Jean-Félix Dallery

Alexander H J Wittenberg

Shiguo Zhou

David C Schwartz

Michael R Thon

Richard J O'connell
email: richard.oconnell@versailles.inra.fr.

Genome Sequence and Annotation of Colletotrichum higginsianum, a Causal Agent of Crucifer Anthracnose Disease

Colletotrichum higginsianum is an ascomycete fungus causing anthracnose disease on numerous cultivated plants in the family Brassicaceae, as well as the model plant Arabidopsis thaliana. We report an assembly of the nuclear genome and gene annotation of this pathogen, which was obtained using a combination of PacBio long-read sequencing and optical mapping.

C olletotrichum is a large genus of plant-pathogenic fungi that cause economically important anthracnose diseases on leaves and fruits of numerous monocot and dicot crops worldwide [START_REF] Crouch | The genomics of Colletotrichum[END_REF], although some species can grow as endophytes in symptomless plants [START_REF] Hacquard | Survival tradeoffs in plant roots during colonization by closely related beneficial and pathogenic fungi[END_REF]. C. higginsianum attacks cultivated Brassicaceae and also Arabidopsis thaliana, providing a tractable model for analyzing fungal pathogenicity and plant responses [START_REF] O'connell | A novel Arabidopsis-Colletotrichum pathosystem for the molecular dissection of plant-fungal interactions[END_REF]. We previously sequenced the C. higginsianum genome using a combination of short reads from 454 FLX (350 bp) and Illumina (36 bp and 100 bp) platforms. This produced a highly fragmented assembly (GenBank accession number CACQ02000000) containing 10,269 contigs (N 50 length ϭ 6,150 bp) with a total length of 49.08 Mbp (4). In the resulting annotation of 16,172 protein-coding genes, many genes were truncated (~9%) or split between contigs (~7%), producing multiple gene calls. Here, we report a nearcomplete assembly of the nuclear genome of this fungus, which was obtained by combining long PacBio reads with the Optical Mapping System [START_REF] Dimalanta | A microfluidic system for large DNA molecule arrays[END_REF].

C. higginsianum strain IMI 349063 was originally isolated from leaves of Brassica campestris (3). High-molecular-weight genomic DNA was purified from mycelium using AXG100 columns (Macherey-Nagel) according to the manufacturer's instructions. A size-selected library (~20 kb) was prepared and sequenced using the PacBio RSII sequencer and 15 single-molecule real-time (SMRT) cells with the P5-C3 polymerase-chemistry combination and 240-min movie time. The filtered sequence reads (7.06 Gbp, ~132.13ϫ average coverage) were assembled de novo using HGAP version 3.0 and SMRT analysis version 2.3.0 software with default settings. Assembled sequences were aligned to the previously reported chromosome optical maps (4) for manual ordering and orientation of the contigs. Overlapping contigs were merged using Minimus2 [START_REF] Sommer | Minimus: a fast, lightweight genome assembler[END_REF]. The assembled nuclear genome comprises 25 contigs (N 50 contig length ϭ 5.20 Mbp, maximum contig length ϭ 6.04 Mbp), with a total length of 50.72 Mbp and a 51.86% GϩC content. The 12 largest contigs (the predicted number of chromo-somes) represent 99.3% of the assembly, and 11 chromosomes are completely sequenced from telomere to telomere. An additional 13 contigs, containing the rDNA repeats, were too small for alignment to the optical map. Based on consensus-calling results, the accuracy of the assembly is high (Ն99.9%). Using the MAKER2 pipeline [START_REF] Holt | MAKER2: an annotation pipeline and genomedatabase management tool for second-generation genome projects[END_REF] to annotate the nuclear genome, a total of 14,651 protein-coding gene models were predicted.

By combining the long reads obtained from SMRT sequencing with optical mapping, we obtained a near-complete assembly of the nuclear genome of C. higginsianum, allowing a more accurate gene annotation that will facilitate future studies on the infection biology of this important model pathogen. The large contiguous genomic regions will be especially valuable for studying structural rearrangements, large secondary metabolism gene clusters, and the chromosome distribution of repetitive elements, pathogenicity-related genes, and epigenetic marks.

Accession number(s). This whole-genome shotgun project has been deposited at DDBJ/ENA/GenBank under the accession number LTAN00000000. The version described in this paper is version LTAN01000000.

Genome Announcements July/August 2016 Volume 4 Issue 4 e00821-16 genomea.asm.org 1 on August 18, 2016 by guest http://genomea.asm.org/ Downloaded from

Genome Announcements 2 genomea.asm.org July/August 2016 Volume 4 Issue 4 e00821-16 on August 18, 2016 by guest http://genomea.asm.org/ Downloaded from

ACKNOWLEDGMENTS

We are grateful to Bruno Huettel (Max Planck Genome Centre, Cologne, Germany) for expert advice on the preparation of high molecular weight genomic DNA and Julie Vallet for excellent technical assistance.

FUNDING INFORMATION

This work, including the efforts of Richard O'Connell, was funded by Agence Nationale de la Recherche (ANR) (ANR-12-CHEX-0008-01).


Maude Jacquot
email: maude.jacquot@gmail.com

Maxime Bisseux

David Abrial

Maud Marsot

Elisabeth Ferquel

Jean- Louis Chapuis

Gwenae ¨l Vourc'h

Xavier Bailly

High-Throughput Sequence Typing Reveals Genetic Differentiation and Host Specialization among Populations of the Borrelia burgdorferi Species Complex that Infect Rodents

Lyme disease is a zoonosis caused by various species belonging to the Borrelia burgdorferi bacterial species complex. These pathogens are transmitted by ticks and infect multiple, taxonomically distinct, host species. From an epidemiological perspective, it is important to determine whether genetic variants within the species complex are able to spread freely through the whole host community or, instead, if certain variants are restricted to particular hosts. To this end, we characterized the genotypes of members of the B. burgdorferi species complex; the bacteria were isolated from more than two hundred individuals captured in the wild and belonging to three different rodent host species. For each individual, we used a high-throughput approach to amplify and sequence rplB, a housekeeping gene, and ospC, which is involved in infection. This approach allowed us to evaluate the genetic diversity both within and among species in the B. burgdorferi species complex. Strong evidence of genetic differentiation among host species was revealed by both genes, even though they are, a priori, not constrained by the same selective pressures. These data are discussed in the context of the advancements made possible by multi-locus high-throughput sequencing and current knowledge of Lyme disease epidemiology.

Introduction

A large percentage of the pathogens that infect humans are zoonotic [START_REF] Woolhouse | Host range and emerging and reemerging pathogens[END_REF] and are maintained in local host reservoirs. Pathogens can be specific to a single host species, or they can possess a more generalist transmission strategy and infect multiple, unrelated host species [START_REF] Haydon | Identifying reservoirs of infection: a conceptual and practical challenge[END_REF]. Identifying these hosts remains a challenging task that can have crucial implications for infectious disease management strategies [START_REF] Keesing | Impacts of biodiversity on the emergence and transmission of infectious diseases[END_REF].

Studying the genetic diversity of zoonotic pathogens can provide valuable insights into various aspects of their epidemiology. For example, analyses of the diversity of pathogens within infected individuals can reveal information about the exposure and/or the susceptibility of hosts to different pathogen genotypes. Furthermore, cases of host co-infection, in which a host is simultaneously infected by different pathogen genotypes, are influenced by the contact dynamics between hosts and infectious agents, the establishment of host defenses, and potential interactions between and among pathogen genotypes. Finally, observations of genetic differentiation between pathogens that infect different host species provide information about genetic exchanges between pathogens found in different potential reservoirs and potential specialization events.

With these goals in mind, the Borrelia burgdorferi species complex is an ideal model system in which to study the spread of zoonotic pathogens within a community of hosts. This species complex includes the pathogens that cause Lyme borreliosis, one of the most frequent vector-borne zoonotic diseases in the Northern Hemisphere. Bacteria of the B. burgdorferi species complex are acquired and transmitted by ticks during their blood meals and infect many different host species, including small mammals, birds, and reptiles. While species of the complex are thought to have host ranges restricted to one or a few host species, the host specificity of individual bacterial genotypes has yet to be evaluated [START_REF] Margos | Population genetics, taxonomy, phylogeny and evolution of Borrelia burgdorferi sensu lato[END_REF]. Finally, this species complex also provides an opportunity to study coinfection, as individual hosts are usually infested by several ticks, each of which can transmit a different bacterial genotype [START_REF] Brisson | ospC diversity in Borrelia burgdorferi: different hosts are different niches[END_REF].

The process of infection by members of the B. burgdorferi species complex involves many different bacterial genes. Among these, the outer surface protein encoded by the ospC gene is characterized by a peculiar pattern of polymorphism, one that is shaped by diversifying selective pressures that arise as a function of host diversity at both a genetic and an immunological scale. In addition, recombination plays an important role in the evolution of this genome region [START_REF] Barbour | Evolution and distribution of the ospC gene, a transferable serotype determinant of Borrelia burgdorferi[END_REF], and the shuffling of genotypes caused by recombination could result in the disruption of any potential associations between ospC genotypes and those of housekeeping genes. However, residual association might still result from either selective or epidemiological constraints that induce linkage disequilibrium at the whole genome scale. Therefore, by comparing the patterns of diversity found in housekeeping genes with those found in infection-related genes, we can obtain information about the evolution of the B. burgdorferi species complex.

Recent developments in sequencing technology have provided the opportunity to investigate the genetic diversity of pathogens within and among hosts in greater detail. Massive parallel sequencing can be used to obtain numerous sequences from the pathogens within a single host and to characterize multiple loci within each pathogen. Moreover, pathogens infecting several hosts can be characterized in a single high-throughput sequencing run, providing an efficient way to perform studies across a community of hosts.

In this context, the aim of this study was to describe patterns of genetic differentiation within and among populations of the B. burgdorferi species complex that infect three rodent species. Hosts were sampled in a French peri-urban forest over the course of several years, and bacteria were characterized using High-Throughput Multi-Locus Sequence Typing (HiMLST) [START_REF] Boers | High-throughput multilocus sequence typing: bringing molecular typing to the next level[END_REF]. This aimed at obtaining an accurate description of genotypes that infect the studied hosts. Characterization of pathogens targeted both a housekeeping gene and an infection-related gene, differentiated by different phylogenies, to evaluate potential evolutionary scenarios. Results are discussed in the context of current knowledge of molecular epidemiology into the B. burgdorferi species complex.

Materials and Methods

Ethic statement

All conducted experiments complied with the current laws of France. Trapping and collection of rodents conducted on the study site (Fore ˆt de Se ´nart, Essonne, France) were carried out under the control of Laurent TILLON (Office National des Fore ˆts), Head of Research Group mammals. The project was approved by the Ethics Committee in Animal Experiment (CEMEA Auvergne). Rodents were euthanized by cervical dislocation. Ear punch biospsies were limited to the minimum size needed, the puncture was disinfected with hydrogen peroxide and checked before releasing the animal.

Sample selection

As part of another study, Siberian chipmunks (Tamias sibiricus barberi), bank voles (Myodes glareolus), and wood mice (Apodemus sylvaticus) were caught during field sampling performed in the forest of Se ´nart (3200 ha, 48u409N, 02u299E), located near Paris (France), over the course of six years, from 2005 to 2010. An ear biopsy was taken from each animal, from which we extracted DNA; PCR Restriction Fragment Length Polymorphism (RFLP) analysis was then used to test whether the animals were infected with bacteria belonging to the B. burgdorferi species complex [START_REF] Marsot | Introduced Siberian chipmunks (Tamias sibiricus barberi) harbor more-diverse Borrelia burgdorferi sensu lato genospecies than native bank voles (Myodes glareolus)[END_REF] [START_REF] Marsot | Introduced Siberian chipmunks (Tamias sibiricus barberi) contribute more to lyme borreliosis risk than native reservoir rodents[END_REF].

As the aim of this study was to evaluate, in depth, the genotypic diversity present within these hosts, we selected 228 infected individuals (Table S1) using a random stratified sampling strategy, targeting a maximum of 30 individuals per host species per year, as long as there were enough samples; the sample contained 125 chipmunks, 93 bank voles, and 10 wood mice.

Sample characterization

The purpose of this study was to compare the patterns of genetic differentiation observed in a housekeeping gene with those from a gene obviously affected by host-driven selective pressures. From among the different markers included in the MLST scheme that has been developed for the B. burgdorferi species complex, we focused on partial sequences of rplB, a housekeeping gene. We also studied partial sequences of ospC, an infection-related gene with antigenic properties that is affected by balancing and/or negative frequency-dependent selective pressures that arise from hostpathogen interactions [START_REF] Haven | Pervasive Recombination and Sympatric Genome Diversification Driven by Frequency-Dependent Selection in Borrelia burgdorferi, the Lyme disease Bacterium[END_REF] [START_REF] Brisson | ospC diversity in Borrelia burgdorferi: different hosts are different niches[END_REF]. Evidence of these evolutionary patterns can be observed in ospC sequence data and seem to be widespread among species within the B. burgdorferi species complex.

Markers were amplified independently via semi-nested PCR for each DNA sample using the GoTaq kit (Promega, Fitchburg, USA). Outer amplifications were performed using the modified primers rplB_Out_F (59 AGGGTATTAAGACTTATAAGC 39) and rplB_Out_R (59 AGGCTGTCCCCAAGGAGAYAC 39) for rplB [START_REF] Margos | MLST of housekeeping genes captures geographic population structure and suggests a European origin of Borrelia burgdorferi[END_REF]. For ospC, primers ospC_F1 (59 GGGAWCCAAAATC-TAATAYAA 39) and ospC_R1 (59 ATATTGACTT-TATTTTTCCAGTTAC 39) were used [START_REF] Barbour | Evolution and distribution of the ospC gene, a transferable serotype determinant of Borrelia burgdorferi[END_REF]. Amplifications were performed using a total volume of 25 ml, which included 8 ml of DNA extract, 0.25 ml of Taq polymerase, 10 pmol of each primer, and the buffer provided by the manufacturer at a final concentration of 1X. Amplifications were performed using the following reaction cycle parameters: an initial denaturation step at 95uC for 10 min, followed by 40 cycles, each composed of a denaturation step at 94uC for 30 s, an annealing step of 30 s (at 58uC for rplB or 56uC for ospC), and an elongation step at 72uC for 40 s. These cycles were followed by a final elongation step of 5 min at 72uC.

For the inner PCR amplifications, the forward primers for each region were composed of two parts: 1) a 30-base-pair (bp) oligonucleotide required for the unidirectional sequencing process on a 454 platform (59 CCTATCCCCTGTGTGCCTTGG-CAGTCTCAG 39) and 2) a gene-specific oligonucleotide to bind the sequence region of interest. The target-specific oligonucleotides were the primers rplB_In_F (59 CGCTATAAGACGACTT-TATC 39) for rplB [START_REF] Margos | MLST of housekeeping genes captures geographic population structure and suggests a European origin of Borrelia burgdorferi[END_REF] and ospC_F2 (59 AAAAGGAGGCA-CAAATTAATG 39) for ospC [START_REF] Theisen | Evolution of the Borrelia burgdorferi outer surface protein OspC[END_REF]. Similarly, the reverse primer was composed of three parts: 1) a 30-bp oligonucleotide required for sequencing on a 454 platform (59 CCATCT-CATCCCTGCGTGTCTCCGACTCAG 39); 2) a 12-bp oligonucleotide tag corresponding to the individual host from which the DNA had been extracted; and 3) rplB_Out_R or ospC_R1, which had already been used for the outer PCR amplifications. Inner amplifications were performed using the same thermal cycling parameters as described above, except that the annealing temperature was fixed at 58uC for both sets of primers.

For each locus, 10 ml of the PCR product obtained from the inner amplification reaction was mixed in a 15-ml tube (BD Biosciences, San Jose, USA). The mixture was vortexed, and 2 ml were pipetted and purified using the NucleoSpin PCR Clean-up Kit (Macherey-Nagel, Du ¨ren, Germany). The quantity of DNA after purification was assessed by spectrophotometry at 260 and 280 nm (Nanodrop, Thermo Scientific, Wilmington, USA). Equal amounts of purified rplB and ospC PCR products were then mixed with each other and sent to GATC-Biotech (Konstanz, Germany) for sequencing on 1/16th of a GS FLX+ run.

Sequence analysis

Assignment of sequences to individuals and loci. Raw sequences were assigned to their respective individual host animals and target loci using a BLAST approach [START_REF] Altschul | Basic local alignment search tool[END_REF]. We matched the individual-host-specific 12-bp tag at the beginning of each sequence with our reference tag sequences using BLASTN. To ensure the best possible assignment for each sequence, BLAST searches were performed using the following parameters: the query strands to search against database for BLASTN was fixed at 1; the reward for nucleotide match was fixed at 4; the penalty for a nucleotide mismatch was fixed at 5; the gap opening cost was fixed at 3; the gap extending cost was fixed at 5; and the used word size was fixed at 4. The best hit was saved and used to assign each sequence to an individual host.

To assign each sequence to its respective locus (rplB or ospC), we then used BLASTN to compare each sequence to those in a database that contained different representative sequences of the target loci from the B. burgdorferi MLST database (http://borrelia. mlst.net) [START_REF] Margos | MLST of housekeeping genes captures geographic population structure and suggests a European origin of Borrelia burgdorferi[END_REF] and from GenBank [14]. BLAST searches were performed using the default parameters. Sequences were assigned to a given locus based on the best BLAST hit if the length of the hit was longer than 130 bp with a percentage of identity higher than 80%. Nucleotides corresponding to primers were removed, and assigned sequences were saved for further analysis. distribution of mismatches along the alignments of the assigned sequences, we decided to use only the first 500 bp of the sequences for subsequent analyses (data not shown). We also removed from the dataset all sequences shorter than 350 bp, of which there were relatively few. We constructed two alignments of the respective sequences of each locus of each individual using K-Align [START_REF] Lassmann | Kalign -an accurate and fast multiple sequence alignment algorithm[END_REF].

Genotype delineation. A major drawback of high-throughput sequencing methods is the relatively high base-calling error rate. This issue could present a problem for within-species studies of diversity as analyses are likely to be affected by the introduction of low-frequency variants that are, in reality, artifacts of sequencing error. In order to circumvent this problem, genotypes and genotype groups were delineated according to two successive distance-based nearest-neighbor classifications.

First, we used the sequence alignments obtained for each locus from each host individual to define a subset of nucleotide sites that contained only sites with Single-Nucleotide Polymorphisms (SNPs); in order to be included in the subset, each site had to be successfully base-called in more than 40% of sequences and had to contain at least two alleles that were present in more than 5% of the sequences in the alignment. We then computed pairwise distances between the sequences contained in each alignment based on the number of selected SNPs in which they differed. Gapped positions were ignored during pairwise comparisons. Finally, sequences were clustered so that the pairwise distance between a given sequence and another of the same group (i.e. genotype) was equal to or lower than a locus-specific threshold. These thresholds were fixed according to the diversity pattern of each marker. As rplB appeared to contain little intra-specific diversity, we set the threshold for divergence at one nucleotide site. However, we observed more genetic divergence within the ospC sequences and therefore set the ospC threshold at two sites. For all of the genotypes identified, we then obtained consensus sequences based on the majority rule for each locus in each individual host.

Next, to identify genotypes that were present in different host individuals, all consensus sequences and singletons obtained from our intra-individual analysis were grouped together according to the algorithm described above. Then, a majority-rule consensus sequence was found for each genotype that was shared across multiple hosts. To ensure robust data, genotypes that were represented by three or fewer sequences in the larger dataset were removed before subsequent analyses.

Differentiation among host species and sampling years. In order to assess the influence of various structuring factors on the observed genetic diversity, we performed an analysis of molecular variance (AMOVA) on the sequence dataset [START_REF] Excoffier | Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data[END_REF], with host species, sampling year, and host individual as structuring factors. This analysis was based on the genotypes defined above, and, within individual hosts, each genotype was weighted according to the number of sequences it contained. In order to prevent bias in the analysis resulting from the impact of interspecific divergence among Borrelia species, genotypes were considered to be genetically equidistant from each other. The analysis was performed with the amova function in the ade4 library in R [START_REF] Dray | The ade4 Package: Implementing the Dualitity Diagram for Ecologists[END_REF].

Genotype phylogeny and delineation of genotype groups. We used a phylogenetic approach to further examine the diversity of the observed genotypes. For each locus, consensus sequences and reference sequences were aligned to each other using K-Align [START_REF] Lassmann | Kalign -an accurate and fast multiple sequence alignment algorithm[END_REF] these reference sequences were the same as those that had been previously used for locus assignments. Phylogenetic searches were performed with a maximum likelihood approach using PHYML [START_REF] Guindon | A Simple, Fast, and Accurate Algorithm to Estimate Large Phylogenies by Maximum Likelihood[END_REF],and we chose the most appropriate model of evolution for each alignment according to the Akaike Information Criterion (AIC) [START_REF] Akaike | A new look at the statistical model identification[END_REF] calculated using the APE library in R [START_REF] Paradis | APE: Analyses of Phylogenetics and Evolution in R language[END_REF]. A phylogenetic network was obtained for each locus using the Neighbor-Net method [START_REF] Bryant | Neighbor-net: an agglomerative method for the construction of phylogenetic networks[END_REF] in SPLITSTREE4 [START_REF] Huson | Application of phylogenetic networks in evolutionary studies[END_REF]. The distance matrices used to create the networks were computed from alignments with PAUP* 4.0 b10 [START_REF] Swofford | PAUP*: phylogenetic analysis using parsimony[END_REF] using the best model for each locus, i.e. GTR+G for rplB and GTR+I+G for ospC [START_REF] Tavare | Some probabilistic and statistical problems in the analysis of DNA sequences[END_REF] [START_REF] Gu | Maximum likelihood estimation of the heterogeneity of substitution rate among nucleotide sites[END_REF]. The substitution rate matrices were estimated via maximum likelihood and assuming empirical nucleotide frequencies. As calculated by the PHYML analysis, the shape parameter of the gamma distribution for rplB was fixed at 0.227; for ospC, the proportion of invariable sites and the shape parameter of the gamma distribution were fixed at 1.117 and 0.169, respectively.

Using the networks, we were able to empirically delineate groups of closely related genotypes. We were then able to calculate the proportion of individuals within each host species that was infected by any given group of closely related genotypes.

Genotype associations and co-occurrence. To search for multi-locus associations, i.e. associations between the different genotypes of the two markers, a graph analysis illustrating the frequency of co-occurrence of the different genotype groups within hosts was performed for both marker. First, we constructed an incidence matrix (m*n), with m as the number of individual hosts and n as the number of genotype groups. This matrix described the presence or absence of the different genotype groups among host individuals. We then created a co-occurrence matrix (n*n) that described the amount of co-occurrence of genotype groups within individuals by multiplying the incidence matrix by its transpose. One-half of the resulting matrix provided the information required to build a graph that described the co-occurrence of genotypes over all individual hosts. Second, we evaluated preferential associations among genotype groups using a ''greedy'' approach [START_REF] Clauset | Finding community structure in very large networks[END_REF] to assess modularity. Measures of modularity aim to determine the adequacy of different classification schemes in representing clusters and divisions in datasets; here, the clusters represented the co-occurrence of genotypes in individual hosts. In the first step of this analysis, each genotype group was considered to be a set. At each step, the algorithm combined two sets such that the resulting classification had the highest degree of modularity as compared to all possible combinations of sets; this was performed until a single set remained. We calculated estimates of modularity for each classification by comparing the fraction of co-occurrence of genotype groups that fall within the sets of the classification with the expected value of the same quantity if the co-occurrences happened randomly without regard for sets of classification [START_REF] Newman | Fast algorithm for detecting community structure in networks[END_REF]. In the final step of the analysis, the classification with the highest modularity from all the generated classifications was selected.

This analysis was performed using the igraph library in R [START_REF] Csardi | The igraph software package for complex network research[END_REF]. The co-occurrence matrix was obtained from the incidence matrix using the graph.incidence and the bipartite.projection functions. The classification analysis was performed using the fastgreedy.community function, and graphical output was produced using the tkplot function.

Finally, to obtain quantitative information on the frequency of co-infection by the different genotypes identified in the host community, we used the data from each marker to calculate how frequently an individual host was infected by at least two different genotypes.

Results

In total, we obtained 16,913 sequences. Of these, 16765 of them were assigned to individual hosts and deposited to the Sequence Read Archive (SRA) database under the accession SRP032755. 16,222 sequences, i.e. 95.9% of the total raw sequences, were assigned to individual hosts and one of the two loci. In the sample of all of the 228 studied mammals, we obtained, on average, 33.2 rplB sequences and 37.9 ospC sequences per host individual. A few individuals did not yield any sequences, but we were able to successfully obtain rplB sequences from 96 chipmunks, 92 bank voles, and 8 wood mice, and ospC sequences from 94 chipmunks, 90 bank voles, and 5 wood mice.

Using the nearest-neighbor classification algorithm, genotypes were delineated from the sequences for each locus; genotypes containing fewer than four sequences were excluded from subsequent analyses. Consequently, our rplB dataset included 33 unique genotypes identified from 87 chipmunks, 87 bank voles, and 7 wood mice. In the phylogenetic analysis based on the rplB data, these genotypes formed five major genotype groups (Figure 1). Most of these groups, with the exception of the group G3, were closely related to previously described, reference sequences. Genotype groups G3 and G4, whose members had been recovered from 86 bank voles and 48 chipmunks, respectively, clustered with sequences of B. afzelii. Sequences of the G1 genotype group were isolated from 46 chipmunks; the sequences seemed to correspond to those from B. burgdorferi, while G2 genotype sequences grouped with the sequence of the B. spielmanii rplB72 allele. Genotype group G5, whose sequences clustered with those of B. garinii, was recovered from only two individual hosts. Within each genotype group, most sequences were obtained from only a single host species, as most members of the G1, G2, and G4 genotypes were isolated from chipmunks and the majority of the G3 sequences came from bank voles. The relatively few sequences recovered from wood mice were found in groups G2 and G4.

In the ospC analysis, 80 genotypes were retained after grouping via the the nearest-neighbor classification algorithm. The genetic diversity displayed in the phylogenetic network of ospC sequences, when compared with that of the rplB network, was consistent with the higher number of genotypes (Figure 2). Based on this network, ospC genotypes were delineated into 14 groups, in which, as in the rplB data, there was a clear influence of host specificity. Groups G1, G2, G4, G9, G10, G11, G12, G13, and G14 included mainly sequences from chipmunks, while groups G3 and G8 consisted mainly of sequences recovered from bank voles. Sequences isolated from wood mice clustered in genotypes G2 and G10.

We studied the genetic differentiation among Borrelia genotypes detected in our sample using an AMOVA, in which the explanatory factors were host species, sampling year, and host individual. As expected from the phylogenetic networks, a substantial percentage of the molecular variance in our sample was explained by the different host species for both loci, 58% for rplB and 21% for ospC (Tables 1 and2). Sampling year failed to explain much molecular variance (,1%). Finally, 11 and 17% of the observed variance could be explained by intra-individual variation in rplB and ospC, respectively. a degrees of freedom; b sums of squares; c mean squares; d components of covariance; e percentage contribution to the total covariance; f Phi statistic.

The graph of genotype associations illustrates the frequency of co-occurrence of the different genotype groups for both markers in host individuals (Figure 3). By using measures of modularity to classify groups of co-occurring sequences, we found that the genotype groups isolated from bank voles were distinct from those isolated from chipmunks. We also found evidence that supports the delineation of various species in the Borrelia burgdorferi species complex. Among the various associations detected by this analysis, we observed that several ospC genotype groups were associated with a single rplB genotype. In addition, chipmunks were infected by several rplB genotype groups, indicating co-infection by different species of Borrelia. Of the individuals from which sequences were recovered, 26% of chipmunks and 11% of bank voles were co-infected by different rplB genotypes, while more than 30% of chipmunks, bank voles, and wood mice were co-infected by multiple ospC genotypes (Table 3).

Discussion

In previous studies, genotypes of the B. burgdorferi species complex have been characterized using multiple techniques, including MLST using Sanger sequencing [29] [30] [31] [START_REF] Margos | MLST of housekeeping genes captures geographic population structure and suggests a European origin of Borrelia burgdorferi[END_REF] [32] and PCR-RFLP [START_REF] Postic | Diversity of Borrelia burgdorferi Sensu Lato Evidenced by Restriction Fragment Length Polymorphism of rrf (5S)-rrl (23S) Intergenic Spacer Amplicons[END_REF]. These two methods are complementary as the first gives an accurate estimation of the relatedness between bacterial genotypes and the second is more useful in detecting coinfection. Here, we used HiMLST [START_REF] Boers | High-throughput multilocus sequence typing: bringing molecular typing to the next level[END_REF] to efficiently characterize the genetic diversity of the B. burgdorferi species complex within three sympatric mammal species. As expected, high-throughput sequencing is potentially quite valuable in these types of studies because it combines the advantages of both MLST and PCR-RFLP.

Benefits associated with high-throughput sequence typing

High-throughput sequencing of the rplB and the ospC markers of the B. burgdorferi species complex revealed a high degree of bacterial diversity within infected chipmunks, bank voles, and wood mice in the forest of Se ´nart (France). The observed rplB genotypes were analyzed in the context of pre-existing MLST data and were found to correspond to four species of Borrelia: i) B. afzelii, including a genotype group that was not described in the dedicated MLST database, ii) B. burgdorferi s.s., iii) B. garinii, and iv) B. spielmanii. When we used this method to assign a species label to the main genotype of each infected individual, we found that the result was very similar, but not identical, to results obtained by PCR-RFLP (Table S1, [8] [9]). An advantage of a HiMLST approach over PCR-RFLP, however, is that a more detailed description of genotypes is possible due to the increased resolution of the sequence data and the high number of sequences obtained from each sample. For example, at the rplB locus, we were able to identify subspecific groups within B. afzelii, and an even higher degree of genetic diversity was observed at the ospC locus. Additionally, the phylogenetic analysis based on this marker revealed that the ospC genotypes associated with a given host species do not form monophyletic groups, an observation that reflects the evolutionary processes, such as diversifying host-driven selective pressures and recombination, that affect this gene [START_REF] Barbour | Evolution and distribution of the ospC gene, a transferable serotype determinant of Borrelia burgdorferi[END_REF].

Patterns of association among ospC and rplB genotypes

In spite of the fact that the observed distribution of sequence polymorphism in the rplB data was quite different from that of ospC, we were able to identify common co-occurrence patterns among the genotype groups. Some of these co-occurrence patterns described multilocus associations, i.e. multilocus sequence types, which were common within the samples studied. In particular, these patterns revealed that multiple ospC alleles were associated with a single allele of rplB, probably because of recurrent recombination in the ospC region. Furthermore, co-occurrence of distinct genotypes of a single locus, as we observed in the rplB housekeeping gene, can provide information about the coinfection of host individuals by different bacterial lineages. Statistical developments that allow researchers to quantify the relative impact of both kinds of co-occurrence patterns will be invaluable in future analyses of similar metagenomic data.

Differences in co-infection patterns among hosts

As described above, the co-infection of individual hosts in our samples by different genotypes was not rare. For example, our graph analysis revealed the frequent presence in bank voles of two ospC variants genetically linked with a single rplB allele. In chipmunks, however, we observed more co-infection events, which can involve multiple combinations of different Borrelia species, a finding that could be explained by several hypotheses. First, at these study sites, chipmunks have a higher tick burden than bank voles do [START_REF] Pisanu | Introduced Siberian chipmunks are more heavily infested by ixodid ticks than are native bank voles in a suburban forest in France[END_REF]. This could result in a higher probability of contact with bacteria of the B. burgdorferi species complex and, consequently, a higher risk of co-infection. Second, the chipmunk is considered an invasive species in the Se ´nart forest; it was introduced there in the early 1970s [START_REF] Chapuis | Datasheet on Tamias sibiricus[END_REF], and the species had colonized the study site by the late 1990s. An increase in the allocation of an individual's resources to reproduction at the cost of allocation to immune responses has been presented as a potential strategy of successful invasive species [START_REF] Lee | A role for immunology in invasion biology[END_REF]. It might thus as well as reference sequences for species in the Borrelia burgdorferi species complex. Genotype groups are delineated by blue dotted lines, and capital letters to the side of each group represent unique ospC alleles detected in previous studies [START_REF] Rudenko | Detection of Borrelia burgdorferi sensu stricto ospC alleles associated with human lyme borreliosis worldwide in non-human-biting tick Ixodes affinis and rodent hosts in Southeastern United States[END_REF]. The pie charts connected to each main genotype group show the proportion of genotypes in the group that were isolated from each host species: Siberian chipmunks (purple), bank voles (green), or wood mice (orange). doi:10.1371/journal.pone.0088581.g002 complex. Finally, it is possible that more bacterial genotypes are able to infect chipmunks than bank voles.

Differentiation among hosts

The strong genetic differentiation observed between bacterial genotypes that infected bank voles and those that infected chipmunks is compatible with the hypothesis of host-specificity in bacterial populations. In spite of the difference in polymorphism patterns between rplB and ospC, genotypes that were isolated from chipmunks and bank voles were significantly different from each other at both markers. Indeed, this study did not identify any bacterial genotype that was present at high frequencies in both host species. Our data support previous results, obtained via PCR-RFLP, that show that in a sample of chipmunks and bank voles, genotypes of B. burgdorferi s.s. were found only in chipmunks. However, the use of PCR-RFLP did not allow that previous study to discriminate between genotypes of B. afzelii associated with either chipmunks or bank voles [START_REF] Marsot | Introduced Siberian chipmunks (Tamias sibiricus barberi) harbor more-diverse Borrelia burgdorferi sensu lato genospecies than native bank voles (Myodes glareolus)[END_REF]. Together with those of the current study, these results demonstrate that chipmunks and bank voles are hosts of independent genotype groups within the B. burgdorferi species complex. The ability of different strains of Lyme borreliosis-causing bacteria to persist in two independent host species could be explained by two non-exclusive hypotheses. First, a given host species might be able to clear infections caused by a specific subset of Borrelia genotypes or bacterial lineages could differ in their ability to infect the two host species, which would result in different infection cycles for the different genotypes. However, it cannot be excluded that chipmunks and bank voles are simply exposed to different bacterial genotypes as a result of host-vector interactions, a situation that could result in the creation of different, host-specific transmission cycles. For example, if ticks have a higher probability of parasitizing a given host species, some degree of differentiation in both housekeeping and virulence-related genes would likely result. It would therefore be interesting to compare samples from questing ticks and feeding ticks to determine if there are changes in the diversity of genotypes of the B. burgdorferi species complex.

Origin of low frequency infections

Unlike chipmunks and bank voles, wood mice are only infrequently infected by strains of the B. burgdorferi species complex [START_REF] Marsot | Introduced Siberian chipmunks (Tamias sibiricus barberi) harbor more-diverse Borrelia burgdorferi sensu lato genospecies than native bank voles (Myodes glareolus)[END_REF], and all but one of the genotypes identified from wood mice were shared with the two other host species. This finding could indicate that wood mice are able to become infected but do not play a critical role in the dynamic of the B. burgdorferi species complex in Se ´nart forest. This infection pattern in wood mice

Perspectives

To conclude, this study used high-throughput sequencing to reveal that strains of the Borrelia burgdorferi species complex that were isolated from three sympatric rodent host species presented strong evidence of genetic differentiation at two different loci subject to distinct evolutionary pressures. This result strengthens the need of experimental studies to investigate properly the adaptation of host specific lineages to assess more accurately the dynamic of Lyme disease bacteria among hosts. The efficiency of the HiMLST procedure opens new avenues of study and offers exciting opportunities for further characterization of the diversity and evolution of pathogens infecting host species and vectors. It is indeed important to take into account the variability of pathogens within samples to i) assess properly their population genetic structure and, ii) compare the diversity among infected individuals and eventually at a tissue scale within individuals.

Figure 1 .

 1 Figure 1. Phylogenetic network of rplB sequences. The network includes consensus sequences of the genotypes recovered in this study (blue)as well as reference sequences for species in the Borrelia burgdorferi complex. The genotypes identified in this study formed empirically delineated genotype groups that are indicated with blue dotted lines. The pie charts connected to each main genotype group show the proportion of genotypes in the group that were isolated from each host species: Siberian chipmunks (purple), bank voles (green), or wood mice (orange). doi:10.1371/journal.pone.0088581.g001

Figure 2 .

 2 Figure 2. Phylogenetic network of ospC sequences. The network includes consensus sequences of the genotypes recovered in this study (blue) as well as reference sequences for species in the Borrelia burgdorferi species complex. Genotype groups are delineated by blue dotted lines, and capital letters to the side of each group represent unique ospC alleles detected in previous studies[START_REF] Rudenko | Detection of Borrelia burgdorferi sensu stricto ospC alleles associated with human lyme borreliosis worldwide in non-human-biting tick Ixodes affinis and rodent hosts in Southeastern United States[END_REF]. The pie charts connected to each main genotype group show the proportion of genotypes in the group that were isolated from each host species: Siberian chipmunks (purple), bank voles (green), or wood mice (orange). doi:10.1371/journal.pone.0088581.g002

Figure 3 .

 3 Figure 3. Visual representation of patterns of genotype group associations found in this study. The graph includes genotypes of both target loci and from all three host species. Each distinct community of co-occurring genotype groups is highlighted in a different color, and the thickness of lines is proportional to the frequency of co-occurrence. Vertices were placed empirically; genotypes located towards the top of the figure correspond to those mainly found in bank voles, while those towards the bottom were associated with chipmunks. The cluster of genotypes at the bottom left corresponds to strains of B. burgdorferi s.s. and that on the top and bottom right to B. afzelii. doi:10.1371/journal.pone.0088581.g003

Table 1 .

 1 Results of the AMOVA performed on rplB sequence data.

	Levels of variability	Df a	SSq b MSq c S d	% e	Phi f
	Among host species	2	1039.14 519.57 0.27	58.19 0.58
	Among years within host	12	77.05 6.42	0.003 0.58	0.01
	species
	Among samples within	172	922.79 5.37	0.14	29.83 0.72
	years
	Within samples	7072	373.20 0.053 0.053 11.41 0.89

a degrees of freedom; b sums of squares; c mean squares; d components of covariance; e percentage contribution to the total covariance; f Phi statistic. doi:10.1371/journal.pone.0088581.t001

Table 2 .

 2 Results of the AMOVA performed on ospC sequence data.

	Levels of variability	Df a	SSq b MSq c S d	% e	Phi f
	Among host species	2	439.44 219.72 0.11	20.93 0.21
	Among years within host	11	190.96 17.36 0.001 0.23	0.003
	species
	Among samples within	163	2451.03 15.04 0.32	61.94 0.79
	years
	Within samples	8238	713.59 0.087 0.087 16.90 0.83

doi:10.1371/journal.pone.0088581.t002

Table 3 .

 3 Percentage (%) of host individuals of each host species in which co-infection by at least two genotypes of the Borrelia burgdorferi species complex was detected.Results are shown for both the rplB and ospC datasets. doi:10.1371/journal.pone.0088581.t003 might be due to either i) a relative paucity of bacterial strains that are adapted to infect wood mice, or ii) the low susceptibility to Borrelia of wood mice in Se ´nart. The infection of a few chipmunks and bank voles by atypical (e.g., B. garinii) and/or low frequency genotypes might illustrate similar infection patterns.

	Host species	rplB	ospC
	Tamias sibiricus barberi	19.54	38.64
	Myodes glareolus	4.55	31.33
	Apodemus sylvaticus	0	0

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February 2014 | Volume 9 | Issue 2 | e88581

February 2014 | Volume 9 | Issue 2 | e88581Trimming of the sequence dataset. After checking the

Acknowledgments

We thank the staff of the GATC, especially Zelie Dubreucq and Marie-Elise Galoppini, for handling the sequencing process. We also thank Jessica Pearce and Lindsay Higgins for proofreading and editing our manuscript.

This work was funded by the French National Institute for Agricultural Research (INRA) through the 'AIP Bioressources 2011' program. Maude Jacquot is supported by a PhD fellowship from the French Ministry of Higher Education and Research (MESR). Interactions between members of the INRA and the Pasteur Institute are funded by the 'Tick and Tick-Borne Diseases' (TMT) group, which is part of the Research Consortium on Biological Interactions (GdR REID). Field sampling was conducted with the financial support of the Conseil Re ´gional d'Ile-de-France and the Office National des Fore ˆts. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Supporting Information

Table S1 Information on the host individuals sampled and the identity of the Borrelia species that were detected in each sample via PCR-RFLP [START_REF] Marsot | Introduced Siberian chipmunks (Tamias sibiricus barberi) harbor more-diverse Borrelia burgdorferi sensu lato genospecies than native bank voles (Myodes glareolus)[END_REF] [9] and/or HiMLST. Species labels were assigned to genotypes in this study based on the genotype groups present in the rplB data. Genotype G1 was assigned to Borrelia burgdorferi s.s., G3 and G4 to Borrelia afzelii, and G5 to Borrelia garinii. 0 means that the species was not detected by PCR-RFLP or HiMLST; 1 that the species was detected only by PCR-RFLP; 2 that species was detected only by HiMLST; and 3 species was detected by both methods; X no sequences were retained for this individual after genotypes containing fewer than four sequences were removed. (DOC)

Author Contributions