Doctoral Dissertations

Orcid ID

https://orcid.org/0000-0001-5340-383X

Date of Award

5-2023

Degree Type

Dissertation

Degree Name

Doctor of Philosophy

Major

Life Sciences

Major Professor

Bode A. Olukolu

Committee Members

Albrecht G. von Arnim, Tian Hong, Jennifer Morrell-Falvey

Abstract

Functional and quantitative metagenomic profiling remains challenging and limits our understanding of host-microbe interactions. This body of work aims to mediate these challenges by using a novel quantitative reduced representation sequencing strategy (OmeSeq-qRRS), development of a fully automated software for quantitative metagenomic/microbiome profiling (Qmatey: quantitative metagenomic alignment and taxonomic identification using exact-matching) and implementing these tools for understanding plant-microbe-pathogen interactions in maize and sweetpotato. The next generation sequencing-based OmeSeq-qRRS leverages the strengths of shotgun whole genome sequencing and costs lower that the more affordable amplicon sequencing method. The novel FASTQ data compression/indexing and enhanced-multithreading of the MegaBLAST in Qmatey allows for computational speeds several thousand-folds faster than typical runs. Regardless of sample number, the analytical pipeline can be completed within days for genome-wide sequence data and provides broad-spectrum taxonomic profiling (virus to eukaryotes). As a proof of concept, these protocols and novel analytical pipelines were implemented to characterize the viruses within the leaf microbiome of a sweetpotato population that represents the global genetic diversity and the kernel microbiomes of genetically modified (GMO) and nonGMO maize hybrids. The metagenome profiles and high-density SNP data were integrated to identify host genetic factors (disease resistance and intracellular transport candidate genes) that underpin sweetpotato-virus interactions Additionally, microbial community dynamics were observed in the presence of pathogens, leading to the identification of multipartite interactions that modulate disease severity through co-infection and species competition. This study highlights a low-cost, quantitative and strain/species-level metagenomic profiling approach, new tools that complement the assay’s novel features and provide fast computation, and the potential for advancing functional metagenomic studies.

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