Manipulating the soil microbiome to increase plant health and productivity - Chaparro, Jacqueline Michelle, author; Vivanco, Jorge M., advisor; Leach, Jan E., committee member; Manter, Daniel K., committee member; Wallner, Stephen J., committee member (Colorado State University. Libraries, 2015)
Rhizosphere microbial communities offer immense benefits to plants. The rhizomicrobiome has the ability to help combat numerous biotic and abiotic stresses as well as increase plant health and productivity. In a world where the population keeps increasing at an alarming rate while food is scarce, new alternatives to feed the growing population need to be identified. The answer lies in harnessing and exploiting the beneficial interactions between plants and their rhizosphere microbiome to increase plant health and productivity. An understanding of the mechanisms that govern such interactions is essential to increase plant health and productivity. Based on this need, an analysis of the interactions between Arabidopsis thaliana and its rhizosphere microbial community was undertaken. Initial studies revealed that root exudates serve as a means of initiating, attracting, maintaining, and enhancing rhizosphere microbial community interactions. Furthermore, root exudation changes with development and leads to changes in the functional capacity and the members that make up the rhizosphere microbial community. These changes appear to occur so the plant can recruit specific functions necessary for survival. Once a framework outlining the importance of root exudation on plant-microbiome interactions was established, compounds from root exudates were added to soil, without the plant, and tested its impact on the soil microbiome. Studies revealed that these compounds when acting alone do in fact influence the soil microbiome and that distinct chemical classes have a direct influence on the soil microbial community. Most importantly, correlation analysis of microbes and the phytochemicals added to the soil revealed that phenolic compounds appear to predominantly modulate the soil microbial community. Finally, the knowledge acquired from these studies allowed development of statistical models that could predict the specific influence of root exudate compounds on the soil microbiome. Five statistical models were implemented, tested, and validated. These results identified models based on machine learning to be of great value in their ability to accurately predict the behavior of soil microbial community abundance after exposure to specific compounds. Overall, the results of this dissertation enable the ability to begin to modulate and manipulate the soil microbial community for increased plant health and productivity.
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