Metabolomics and Machine Learning Techniques applied to investigate beneficial Plant-Bacteria Interactions

Author(s)
Lena Fragner, Florian Schindler, Johannes Herpell, Weimin Li, Xiaoliang Sun, Anke Bellaire, Wolfram Weckwerth
Abstract

Endophytic non-pathogenic colonization of plant tissue by bacteria is a well-known and wide spread phenomenon, even expected to be the case for all angiosperms. Symbiotic plant-bacteria interactions comprise various levels of obligations and ecological benefits to at least one of the partners. The beneficial effects and functions for plants are manifold, including enhanced stress resistance, plant growth promotion or capacity for controlling plant-pathogens. In the present work, we focus on obligatory and constant symbioses occurring in the plant families Rubiaceae, Primulaceaeand Dioscoreaceae. Highly specialized bacterial symbionts are mainly host-specific, often not cultivable and their absence can lead to dwarf phenotypes of the host-plants. Endophytic bacteria of leaves can be evenly distributed between the mesophyll cells or accumulated in specific leaf areas or in specialized structures.Most studies focused on genome and proteome analyses suggesting potential alterations of secondary metabolism caused by the presence of beneficial symbionts. However, detailed mechanisms and functions of these highly specialized mutualistic plant-bacteria symbioses are not yet fully understood.In the present study we investigate alterations in the metabolome of colonized leaf tissue. Primary and secondary metabolites were analyzed by GC-MS and LC-MS respectively, complemented with physiological and morphological data, andanalyzed with machine learning techniques. Results indicate distinctive mechanisms of the symbiosis in investigated beneficial plant-bacteria interactions and will be discussed in detail.

Organisation(s)
Functional and Evolutionary Ecology, Department of Ecogenomics and Systems Biology, Research Platform Vienna Metabolomics Center, Large-Instrument Facility for Mass Spectrometry in Life Sciences
Publication date
06-2019
Peer reviewed
Yes
Austrian Fields of Science 2012
Metabolomics , Molecular biology, Microbiology, Plant physiology
Keywords
ASJC Scopus subject areas
Portal url
https://ucris.univie.ac.at/portal/en/publications/metabolomics-and-machine-learning-techniques-applied-to-investigate-beneficial-plantbacteria-interactions(2904817c-af19-4f4c-b46b-5ff4f4271ca3).html