Dokument: Plant-associated microbial community diversity and network feature analysis
| Titel: | Plant-associated microbial community diversity and network feature analysis | |||||||
| URL für Lesezeichen: | https://docserv.uni-duesseldorf.de/servlets/DocumentServlet?id=63115 | |||||||
| URN (NBN): | urn:nbn:de:hbz:061-20230811-083205-8 | |||||||
| Kollektion: | Dissertationen | |||||||
| Sprache: | Englisch | |||||||
| Dokumententyp: | Wissenschaftliche Abschlussarbeiten » Dissertation | |||||||
| Medientyp: | Text | |||||||
| Autor: | Guan, Rui [Autor] | |||||||
| Dateien: |
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| Beitragende: | Prof. Dr. Klau, Gunnar [Gutachter] Prof. Dr. Rose, Laura [Gutachter] | |||||||
| Dewey Dezimal-Klassifikation: | 000 Informatik, Informationswissenschaft, allgemeine Werke » 004 Datenverarbeitung; Informatik | |||||||
| Beschreibung: | Microbes are present in most of the environments on earth. By interacting with each other, macro-organisms, and the surrounding habitat, they form diverse microbial communities denoted as microbiota. Soil-derived microbial communities associate to plant hosts and form the plant microbiota, which promotes nutrient uptake and protects the roots from pathogens. To understand microbiota composition and diversity, a variety of approaches have been developed. By analysing high throughput sequencing data derived from these experimental approaches, we can survey the microbial composition and determine the factors that affect the community assembly. In this thesis, I show how various bioinformatic tools can be applied to the analysis of the community profiling of natural and synthetic microbial communities, particularly of those associated with a plant host. Specifically, using the model plant Arabidopsis thaliana, we explored the mechanism of how root microbiota promotes nutrient uptake as well as the interplay between host innate immunity and microbiota regarding growth and defence. By comparing with other hosts, including the photosynthetic plant Lotus japonicus and model alga Chlamydomonas reinhardtii, we demonstrated host preference and shared features of their microbiota.
As one of the most widely cultivated crops, maize has been an important model organism for microbiota research to understand the effects of plant breeding on the establishment of root microbiota. Furthermore, the relationship between multiple kingdom root microbiota, abiotic factors such as soil management, and plant growth is still unclear. In order to explore these interactions, we characterized the root microbial communities of maize grown in two long-term experimental fields under four soil managements. The sampling spanned from the vegetative to reproductive growth stage. We identified stable root microbial taxa that persisted through the host growth and these taxa were accompanied by dynamic members that covaried with root metabolites. By comparing wild-type and mutant plants, we discovered a potential plant growth phase-specific interaction between arbuscular mycorrhizal fungal symbiosis, root lipid status, and soil phosphate availability. Together, our work sheds light on the spatio-temporal dynamics of maize root-associated microbiota and its impact on plant physiology and fitness. To better investigate the biological meaning behind the increasing amount of plant microbiota data, we developed a novel diversity and network analysis workflow into an open-access R package named ‘mina’. We integrated a large-scale plant- and alga-associated microbiota dataset, to which we applied the developed workflow. Higher-order features, namely clusters of connected microbes in the network, were introduced to diversity analysis and decreased the unexplained variance compared to traditional diversity measurements. To assist the comparative analysis of microbial networks, we established an approach that relies on the calculation of network spectral distances and Monte Carlo permutation significance tests. We differentiated networks constructed from samples originating from various conditions and identified the features with the highest contribution to the network differentiation. In summary, I show that insights into the assembly and function of microbiota are gained by analysing microbial community profiling data. This is not limited to the natural conditions in ecological surveys but also applies to the reconstituted synthetic microbiota systems. With the novel diversity and network analysis tools that I developed, I can better describe microbiota diversity and determine distinctive features that drive the dynamics of microbial communities. | |||||||
| Lizenz: |  Dieses Werk ist lizenziert unter einer Creative Commons Namensnennung 4.0 International Lizenz | |||||||
| Fachbereich / Einrichtung: | Mathematisch- Naturwissenschaftliche Fakultät » WE Informatik » Bioinformatik | |||||||
| Dokument erstellt am: | 11.08.2023 | |||||||
| Dateien geändert am: | 11.08.2023 | |||||||
| Promotionsantrag am: | 24.11.2022 | |||||||
| Datum der Promotion: | 04.05.2023 |
