Dokument: Computational Methods for the Study of Plant-associated Microbial Communities
| Titel: | Computational Methods for the Study of Plant-associated Microbial Communities | |||||||
| URL für Lesezeichen: | https://docserv.uni-duesseldorf.de/servlets/DocumentServlet?id=45660 | |||||||
| URN (NBN): | urn:nbn:de:hbz:061-20180427-090015-1 | |||||||
| Kollektion: | Dissertationen | |||||||
| Sprache: | Englisch | |||||||
| Dokumententyp: | Wissenschaftliche Abschlussarbeiten » Dissertation | |||||||
| Medientyp: | Text | |||||||
| Autor: | Garrido Oter, Ruben [Autor] | |||||||
| Dateien: |
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| Beitragende: | Prof. Dr. Paul Schulze-Lefert [Gutachter] Prof. Dr. McHardy, Alice [Gutachter] | |||||||
| Stichwörter: | Bioinformatics; Computational Biology; Microbial Ecology; Comparative Genomics; Plant-Microbe Interactions | |||||||
| Dewey Dezimal-Klassifikation: | 000 Informatik, Informationswissenschaft, allgemeine Werke » 004 Datenverarbeitung; Informatik | |||||||
| Beschreibung: | Higher organisms such as animals and land plants host diverse communities of microorganisms which are collectively designated as microbiota. There is a growing body of evidence that establishes links between these microbial assemblages and the fitness of their host, for example via indirect protection against pathogens or enhanced nutrient acquisition. Additionally, host-microbiota systems can be used as models to investigate the principles underlying microbial community structure and assembly and the dynamics of co-adaptation between multiple organisms.
The aim of this work was to develop and apply computational methods for the analysis of sequence data obtained from environmental samples of plant-associated microbes as well as genomic sequences of cultured isolates in a comparative framework. Employing culture-independent community profiling techniques (e.g. 16S rRNA gene amplicon surveys or shotgun-metagenomics) we were able to describe and characterize the taxonomic structure and functional potential of the plant microbiota across multiple hosts, including the model Arabidopsis thaliana and relatives, the crop barley (Hordeum vulgare) or the legume Lotus japonicus. In addition, we have developed a number of culture-dependent methods to study the plant microbiome, including the characterization of a large collection of cultured bacterial microbiota members using a sequence-indexed library of more than 5,000 colony-forming units. Whole-genome sequencing of a taxonomically representative subset of 400 isolates from this collection revealed a large overlap of functional capabilities between leaf- and root-derived bacteria as well as few significant differences at the level of individual functional categories. A targeted, large-scale isolation and sequencing effort focused on the Rhizobiales order, a taxonomic group of particular interest which includes members that are capable of engaging in highly adapted and beneficial symbiotic interactions with legumes resulted in the generation of a dataset of more than 900 draft genomes, which includes a large number from previously uncharted branches of the species tree of rhizobia. Phylogenomic analysis of these sequences provided evidence of an ancestral form of association between rhizobia and flowering plants that predates the capacity for nodulation and nitrogen fixation, which ancestral reconstruction of relevant genomic features suggests was acquired in multiple subsequent events, most likely via horizontal gene transfer, in an example of convergent evolution. Finally, we developed a novel phylogenetic approach for determining clusters of co-evolving genes and their network organization by modeling gene gain and loss as a continuous process along the branches of the species tree. This method accounts for uncertainty in the reconstruction of the ancestral states as well as in the inference of the species tree and robustly identifies clusters of co-evolving genes that significantly enrich for functional categories and pathways and which are relevant for adaptation to diverse environments. We demonstrate the potential of this approach to detect biologically meaningful gene family interactions and predict genotype-phenotype relationships by analyzing a total of 2,737 bacterial genomes from diverse environments, including plant commensals and symbionts as well as human pathogens. In summary, we have generated and analyzed large quantities of sequencing data that provide a taxonomic and functional characterization of the plant microbiota, constituting a large dataset and a valuable resource for future research. Additionally, novel methodology for the analysis of collections of microbial genomes provides tools for the identification of sets of genes involved in relevant biological processes as well as links to corresponding phenotypes. Extending these datasets and the available number of sequenced genomes, together with further development of phylogenenomic methods has the potential to greatly improve our understanding of the processes that drive the adaptation of microbes in the context of the complex communities which they form. | |||||||
| Lizenz: |  Urheberrechtsschutz | |||||||
| Fachbereich / Einrichtung: | Mathematisch- Naturwissenschaftliche Fakultät » WE Informatik » Bioinformatik | |||||||
| Dokument erstellt am: | 27.04.2018 | |||||||
| Dateien geändert am: | 27.04.2018 | |||||||
| Promotionsantrag am: | 11.04.2018 | |||||||
| Datum der Promotion: | 06.06.2018 |
