Browsing by Author "David, Lawrence A"

Advances in high-throughput DNA sequencing allow for rapid and affordable surveys of thousands of bacterial taxa across thousands of samples. The exploding availability of sequencing data has poised microbiota research to advance our understanding of fields as diverse as ecology, evolution, medicine, and agriculture. Yet, while microbiota data is now ubiquitous, methods for the analysis of such data remain underdeveloped. This gap reflects the challenge of analyzing sparse high-dimensional count data that contains compositional (relative abundance) information. To address these challenges this dissertation introduces a number of tools for Bayesian inference applied to microbiome data. A central theme throughout this work is the use of multinomial logistic-normal models which are found to concisely address these challenges. In particular, the connection between the logistic-normal distribution and the Aitchison geometry of the simplex is commonly used to develop interpretable tools for the analysis of microbiome data.

The structure of this dissertation is as follows. Chapter 1 introduces key challenges in the analysis of microbiome data. Chapter 2 introduces a novel log-ratio transform between the simplex and Real space to enable the development of statistical tools for compositional data with phylogenetic structure. Chapter 3 introduces a multinomial logistic-normal generalized dynamic linear modelling framework for analysis of microbiome time-series data. Chapter 4 explores the analysis of zero values in sequence count data from a stochastic process perspective and demonstrates that zero-inflated models often produce counter-intuitive results in this this regime. Finally, Chapter 5 introduces the theory of Marginally Latent Matrix-T Processes as a means of developing efficient accurate inference for a large class of both multinomial logistic-normal models including linear regression, non-linear regression, and dynamic linear models. Notably, the inference schemes developed in Chapter 5 are found to often be orders of magnitude faster than Hamiltonian Monte Carlo without sacrificing accuracy in point estimation or uncertainty quantification.

The human gut microbiome is increasingly recognized as having a causal or contributing role in a wide variety of diseases. While mechanisms by which the microbiome contributes to or triggers disease processes are myriad, short-chain fatty acid (SCFA) production has been found to be a powerful regulator of inflammation and gastrointestinal (GI) function, and may be central to the link between host and microbiota. Supplementing the diet with microbially accessible carbohydrates, termed prebiotics, is one mechanism by which SCFA production can be augmented or altered. While prebiotic therapies to increase SCFA in the gut have shown some promise in treating or preventing disease, treatment potential is limited by substantial inter-individual variation in responses to prebiotics. Determining the cause for this variation is necessary to develop treatment approaches that maximize patient responsiveness. Ultimately, tools to predict an individual’s response to a prebiotic and to guide treatment options must be developed. Here, I seek to understand the drivers of inter-individual and intra-individual variation in prebiotic response and to develop strategies to predict this response. In Chapter One, I introduce the human gut microbiome and its roles in maintaining host health and contributing to disease processes. I also present the existing evidence for substantial variation in SCFA productive response to prebiotic supplementation and highlight the need for a more nuanced understanding of the drivers of such variation. In Chapter Two, I explore the contributions of host factors and prebiotic choice to variation in SCFA production. This chapter introduces a novel in vitro fiber fermentation system, which is used throughout this thesis, and shows our methods validation of such. We find not only that host identity and prebiotic choice both impact SCFA production, but that the interaction of these terms is a significant contributor, introducing the possibility of the need for personalization. We then identify multiple host factors, including microbiota community composition and baseline SCFA metabolic state of stool, that explain some portion of inter-individual variation in prebiotic response. In Chapter Three, this relationship is further explored during the first in vivo triple-crossover prebiotic supplementation study. We supply 28 healthy adults with three different prebiotic supplements in a balanced and uniform crossover design, measuring SCFA as the primary outcome. This study makes the major contribution of quantifying the relative contribution of individual identity and prebiotic choice to butyrogenic response, and identifying individual as the vastly stronger predictor. We also identify habitual diet and baseline fecal SCFA concentrations as potential predictors of prebiotic efficacy. As a secondary analysis, we apply co-inertia analysis to draw associations between dietary choices and fecal SCFA metabolism. Together, these works highlight the need for personalization of prebiotic therapy and introduce potential biomarkers of responsiveness. In Chapter Four, we apply the concept of prebiotic therapy to graft-versus-host disease (GVHD) and show efficacy in a murine model. Importantly, we show that efficacy of prebiotics in this model of GVHD is dependent on the starting state of the microbiota, as observed through community composition analysis and functional in vitro fiber fermentation.

Microbes are the foundation of all ecosystems and crucial players in major ecosystem processes. However, most of our ecological theory was developed for plants and animals and thus may not help us understand these important communities. Previous syntheses have found mixed evidence for ecological patterns in observational data of microbes. In my dissertation, I combine observation and experiments to identify forces structuring microbial communities and how these are similar or different from those at play in systems of macroorganisms. In two chapters, I test new ecological hypotheses in soil microbial communities. In later chapters, I draw on similar ecological theory to explore the relative importance of host and microbial control for the gut microbiota.

In chapter one, I analyze insect, fungal, and bacterial responses to urbanization and habitat fragmentation. This study is the first of its kind to compare scaling relationships between macro- and micro-organisms in the same habitats. I find that microbial communities were vastly more immune than even the smallest of animals to human perturbation.

In chapter two, I seek to identify the drivers responsible for microbial community assembly during secondary succession. I use a fully factorial microcosm experiment that manipulates both biotic and abiotic factors in microcosms emulating old-fields. I find that both plant community and soil conditions are important for determining microbial community composition but that unique taxa respond to each driver.

In chapter three, I aim to quantify and document the impact of gut nitrogen availability on the microbiota. I find that stoichiometric mismatch between microbes and gut resources is pervasive for mammals, indicating that nitrogen may be limiting. Furthermore, I show that nitrogen availability in the gut is under host control, with host secreted nitrogen serving as a dynamic means for the host to manipulate microbial composition.

In chapter four, I use both in and ex vivo approaches to document shifts in composition and changes in the environment in the gut following antibiotic treatment. I find that the most significant abiotic shift is an increase in redox potential, which is due primarily to changes in microbial metabolism rather than a host response. Feedbacks between the environment and the microbial community, as well as dispersal limitation, then contribute to compositional change during post-antibiotic succession.

Isolation and culture of gut bacteria enable testing for microbial roles in disease and may also lead to novel therapeutics. However, characterization of these microbes represents a complex and substantial problem in the diversity (hundreds of microbes unique to an individual) and magnitude (up to billions of microbes within one gram of stool) of microbes present in the human gut. While microbiologists have been working on this problem for decades, the diversity and number of microbes present in the human gut impedes comprehensive experimental studies of individual bacterial taxa. Here, we combine advances in droplet microfluidics and high-throughput DNA sequencing to develop a platform for isolating and assaying microbiota members in picoliter droplets (MicDrop). MicDrop can be used to create millions of distinct bacterial colonies in a single experiment while using off-the-shelf parts compact enough to fit in an anaerobic chamber.

In proof-of-concept experiments, we used the platform to characterize antibiotic sensitivity in a set of gut microbes. We also used MicDrop to test the hypothesis that growth kinetics of individual gut bacterial taxa are associated with long-term community dynamics in an artificial gut. These demonstrations suggest the MicDrop platform could support future diagnostic efforts to personalize microbiota-directed therapies, as well as to provide comprehensive new insights into the ecology of human gut microbiota.

Dietary intake is one of the most complex and sustained environmental exposures of the human lifespan. At present, field standard methods for dietary assessment all depend on asking people to self-report what they eat. Self-reported dietary data have enabled the study of diet-health relationships and informed decades of nutritional policy, but they are also limited in their ability to collect data from individuals across the spectrum of age, cognitive ability, and culture. To address the need for alternative measures of diet, we leverage DNA sequencing technology to measure diet from degraded food DNA in human stool samples. This strategy, termed “DNA metabarcoding,” works by amplifying and sequencing regions of the genome to identify foods by their DNA sequence. Although metabarcoding strategies have been applied in other diet-related fields, they have never been used in nutritional epidemiology.

In Chapter 1, I describe the potential of DNA metabarcoding for human dietary assessment. In Chapter 2, we apply DNA metabarcoding with the chloroplast trnL-P6 marker region to >1,000 stool samples from >300 unique participants. Across multiple cohorts, we find that the number of plant taxa detected per sample by trnL metabarcoding correlates with dietary diversity and quality estimates from established self-report assessments. In a cohort where the administration of self-reports failed, we use trnL metabarcoding to retrospectively derive diet data, and detect relationships between trnL dietary diversity and age, socioeconomic indicators, and health that replicate prior epidemiologic findings.

In Chapter 3, we perform a pilot study of a second molecular technique— metaproteomics— to determine if it can address a key limitation of metabarcoding: the inability of DNA to distinguish food type for foods like chicken and egg, which are derived from the same source species. We identify tissue-specific protein signatures of food intake and confirm an overall correspondence between DNA- and protein- based dietary assessment in comparison to written records. We also expand our metabarcoding protocols to include the mitochondrial 12SV5 marker for combined detection of plant and animal intake. Taken together, these findings establish the value of molecular measurements from stool as a strategy to monitor and improve nutrition.

The human gut is home to trillions of microbes that interact intimately with the host and its diet. An important emergent phenotype by these microbes is colonization resistance, the process by which a microbial community resists colonization of an exogenous microbe. This resistance barrier is critical for protecting humans from infectious enteric pathogens. However, it is detrimental to the deliberate engraftment of probiotics which are live microbes beneficial to the host. The mechanisms behind these barriers have been studied extensively, but the microbiome is a network of many biological nodes and ecological edges that can interact with an invader in numerous ways. Therefore, defining the precise mechanisms for resistance of a specific pathogen or probiotic is challenging, due in large to this combinatorial challenge. In the second chapter of this dissertation, I demonstrate a novel approach that can suggest key taxa or host factors associated with clinical outcomes of interest including colonization resistance. In particular, I leveraged a rare prospective cohort study with machine learning methods for identifying gut bacterial signatures associated with susceptibility to cholera. I demonstrated that the human gut microbiota can predict the susceptibility of its host to the diarrheal disease. One of the predictive gut microbes identified by my model, Paracoccus aminovorans, facilitated the growth of Vibrio cholera, the etiologic agent of cholera, in vitro. My model also linked gut microbiota structure, clinical outcomes, and age. This integrative approach suggested that gastrointestinal immaturity of the host and its gut microbiome may be crucial for resisting colonization of enteric pathogens. The predictive model was also over-represented with members of the Bacteroidetes phylum, including several Bacteroides species. These taxa belong to a genus that is dominant in the gut of human on a western diet. Genomic, biochemical, and metabolic studies have vastly studied the traits of these Bacteroides species and how they interact with the host. As obligate anaerobes that are stably colonized in the human gut, the Bacteroides is an ideal model genus for studying ecological mechanisms of colonization resistance. In the third chapter of this dissertation, I developed a high-throughput rapid approach for inferring the relative abundance of several Bacteroides species in a mixed community grown on single carbon substrates. I validated the utility of this method by investigating whether Bacteroides species cooperate or compete when carbon resources are limited. By profiling the growth of mixed cultures on single carbohydrates, I show that Bacteroides exhibit both patterns of resource cooperation and competition. Together, these chapters show that development and application of novel computational and experimental tools can shed light on the intimate interactions between diet, microbiome, and the host in the context of colonization resistance.

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The gut microbiome is a complex ecosystem of hundreds of species that is constantly subject to perturbations as a result of day-to-day dietary variation, among other factors. In some cases, disturbances to microbial communities have been associated with lasting impacts on microbiome structure. While much research has been done to uncover sources of inter-individual variation in the gut microbiome, less focus has been given to understanding the ecological mechanisms governing intra¬-individual variation. To address this, we carried out dietary intervention studies in human cohorts and analyzed microbiome composition, metabolism, and physical particulate structure. We also employed in vitro models of the gut microbiome to manipulate variables difficult to modulate in vivo, and to collect samples with a greater temporal resolution. As discussed in more detail in Chapter 2, we found that bacterial communities retain an “ecological memory” of past prebiotic exposures, which is encoded within one day by changes to the abundance and transcriptional state of primary degraders. Chapter 3 details findings from an investigation into fecal particle size, in which we found this metric to correlate with microbiome diversity and to be stable within individuals. Together, the results presented in this dissertation present fundamental new insights into the ecology of the gut microbiome.