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Abstract
Microbes are associated with all complex organisms, influencing host fitness, local ecologyand evolutionary trajectories. Thus, there is burgeoning interest in engineering microbiomes
for practical applications, such as sustainable agriculture and precision medicine. However,
the emergent phenotype from confounding host, microbe, and environmental interactions
within diverse microbiomes proves challenging to characterize, let alone engineer.
One method of microbiome characterization is to quantify differential abundances of distinct
bacteria taxonomic groups among hosts. The development of high throughput sequencing
facilitates this type of characterization using variable regions of marker genes to taxonomically
group the microbes. However, the canonical 16S v5-v7 gene region used to assess plant
microbiomes is relatively constrained, effectively grouping distinct bacteria into higher levels
and potentially masking host-microbe interactions.
Here, I propose decomposing taxonomic groups to lower levels, facilitating finer groupingsof bacteria to more accurately describe the respective microbe-microbe interactions and
subsequently investigate host-genotype effects on the microbiome abundance phenotype.
I first describe the collection and classification of natural bacteria isolates collected from
Arabidopsis thaliana plants from the field. In the next chapter, I describe the development
of a new marker gene database using gyrase subunit-β (gyrB). Using the isolates from the
previous chapter in combination with published data sets of A. thaliana microbiomes, I show
that gyrB provides both finer taxonomic resolution and stronger correlations between genetic
and genomic distances compared to the canonical 16S v5-v7 marker gene. Lastly, I apply
gyrB sequencing to leaf microbiome community abundances from replicated A. thaliana field
experiments. I show that using gyrB, compared to 16S v5-v7, and including microbe-microbe
interactions improves model fits for broad-sense heritability estimates. I use these data
to perform Genome Wide Association Studies (GWAS), and identify host gene-candidates
potentially shaping the microbiome.