Ticks are increasingly important vectors of human and agricultural diseases. While many studies have focused on tick-borne bacteria, far less is known about tick-associated viruses and their roles in public health or tick physiology. To address this, I investigated patterns of bacterial and viral communities across two field populations of western black-legged ticks (Ixodes pacificus). In addition to commonly found tick-associated microbes, I discovered 11 novel RNA viruses from Rhabdoviridae, Chuviridae, Picornaviridae, Phenuiviridae, Reoviridae, Solemovidiae, Narnaviridae and 2 highly divergent RNA viruses lacking sequence similarity to any known viral families. I also unexpectedly identified numerous virus-like transcripts that are likely encoded by tick genomic DNA, and which are distinct from known endogenous viral element-mediated immunity pathways in invertebrates. Together, my work reveals that I. pacificus ticks carry a greater diversity of viruses than previously appreciated, in some cases resulting in evolutionarily acquired virus-like transcripts. These findings highlight how pervasive and intimate tick–virus interactions are, with major implications for both the fundamental biology and vectorial capacity of I. pacificus ticks.

Diseases related to the human gut microbiome, such as irritable bowel disease (IBD), irritable bowel syndrome (IBS), and colorectal cancer, have increased within developed nations. At the same time, increased access to metagenomics has allowed us to try to understand the microbiome. While many microbiome-disease association studies have been carried out, they primarily focus on genera or species that vary in abundance with disease status; this relatively coarse level of analysis limits our understanding of why microbes may act as disease markers and overlooks cases where disease risk is related to the presence or absence of specific strains with unique biological functions. My thesis shows that there are strong sub-strain phenotypic signatures across samples and then introduces microSLAM, a method that incorporates random effects to represent the population structure of the bacteria modeled in an association study. I applied microSLAM to many gut metagenomes from IBD samples, where I discovered 49 species whose population structure correlates with IBD. In addition, after controlling for population structure, I found 57 microbial genes that are significantly more common in healthy individuals and 26 that are more common in IBD patients, including a seven-gene operon in Faecalibacterium prausnitzii that is involved in the utilization of fructoselysine from the gut environment. In addition, I performed extensive simulations to understand the limitations and capabilities of microSLAM and found it was much more conservative and specific than standard a generalized linear model (GLM). These findings have highlighted the importance of considering within-species population structure in microbiome studies.

High-throughput sequencing has firmly established itself as the leading method for assaying the structure and functional capacity of microbial communities. With this deluge of data, care must be taken to account for technical and biological artifacts in order to produce robust candidate biomarkers. Of particular interest is the use of mixed effects models and nonlinear models to assess key differences between healthy and diseased individuals that arise over time. In my thesis work, I analyzed data from a longitudinal study of inflammatory bowel disease in mice with the aim of uncovering biological features predictive of abnormal microbiome development in the context of chronic inflammation. My analysis uncovered multiple taxa and gene families that have differential temporal trajectories, as well as a few gene families that stratify the diseased and wild type subjects early on. This investigation led to a follow-up study of the underrepresented microbial genomes present in lab mice, to expand our knowledge of the model animal’s microbiome. Since the majority of microbiome studies aimed at future clinical impact are carried out in mice, it is important to know what separates human microbiomes from those of mice, in order to limit hypotheses that are not transferrable. We found that even a modest single cell sequencing effort leads to an appreciable gain in phylogenetic diversity and significantly improves the recruitment of short reads from unrelated mouse metagenomes. Overall, I have shown that robust findings are possible even with a limited set of subjects if one leverages a nuanced statistical modeling approach and undertakes targeted acquisition of new data.

Deep metagenomic sequencing has the potential to illuminate the intra-species genomic variation of abundant microbial species. In this thesis, I develop a new tool MIDAS (Metagenomic Intra-species Diversity Analysis System) for rapidly and automatically quantifying species abundance, single nucleotide polymorphisms (SNPs), and gene copy number variants (CNVs) from metagenomes. To illustrate the utility of this approach, I reanalyze three public datasets with MIDAS. First, I re-analyze stool metagenomes from 98 mother-infant pairs and used rare SNPs to track strain transmission. I find that early colonizers are likely transmitted from the mother whereas late colonizers are likely transmitted from the environment. Second, I re-analyze >300 stool metagenomes from healthy adults and use SNPs to identify examples of both strain co-existence and strain coexclusion. Third, I re-analyze 198 globally distributed marine metagenomes and used gene copy number variants to show that many species have population structure that correlates with geographic location. Strain level genetic variants clearly reveal extensive structure and dynamics that are obscured when metagenomes are analyzed at coarser taxonomic resolution.