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Scholarly Works (54 results)

  • Thesis
  • Peer Reviewed
UC San Diego Electronic Theses and Dissertations (2017)

Technological developments in the past thirty years have transformed sequencing-based microbiology into a data-intensive field. Here, computing and efficient representations are catalyzers of insight into omnipresent and complex microbial interactions. Notably, classical ecologists have set the foundations for the way we analyze these systems, with some techniques dating back to the beginning of the twentieth century. In this thesis, we expand and where possible reuse these techniques to unravel the hidden patterns comprising the human gut microbiome.

To set an appropriate motivation and context for the rest of this work, Chapter 1 reviews recent discoveries on the human microbiome and how the communities within can influence the effectiveness of therapeutic agents. Next, in Chapter 2, we introduce EMPeror, an interactive analysis and visualization tool that is crucial to the findings presented in later chapters.

The following three chapters study concrete examples where the microbiome has been implicated as a driver or marker for dysbiosis. Chapter 3 describes how the microbial signature associated with Crohn's disease (CD) in humans, described in our previous work, is overlapping but distinct to that of dogs affected with inflammatory bowel disease (IBD). Surprisingly, unlike with humans, dog fecal samples alone are strong indicators of the disease. In Chapter 4, we study IBD from a longitudinal perspective, revealing increased volatility in the gut microbiomes of subjects with IBD, a property that does not appear to be present in unaffected controls. Furthermore, we use this as a predicting feature of the disease, and improve on the classification accuracy possible through a single fecal sample. In Chapter 5, we study the effect of fecal microbiota transplants (FMTs) to treat Clostridium difficile infection (CDI) and, using the techniques described in Chapter 2, we show the first animated visualization of this process, a dramatic microbial transformation as the subjects recover from all CDI symptoms. In addition, for CDI patients who also suffer from a subtype of IBD, a treatment with a FMT results in an increased number of relapses and decreased microbial diversity.

The closing chapter discusses these results and their possible applications, as well as future directions for computationally-centric microbiome research.

    Cover page: Statistical representations of microbial systems
    • Article
    • Peer Reviewed
    UC San Diego Previously Published Works (2013)

    Background

    As microbial ecologists take advantage of high-throughput sequencing technologies to describe microbial communities across ever-increasing numbers of samples, new analysis tools are required to relate the distribution of microbes among larger numbers of communities, and to use increasingly rich and standards-compliant metadata to understand the biological factors driving these relationships. In particular, the Earth Microbiome Project drives these needs by profiling the genomic content of tens of thousands of samples across multiple environment types.

    Findings

    Features of EMPeror include: ability to visualize gradients and categorical data, visualize different principal coordinates axes, present the data in the form of parallel coordinates, show taxa as well as environmental samples, dynamically adjust the size and transparency of the spheres representing the communities on a per-category basis, dynamically scale the axes according to the fraction of variance each explains, show, hide or recolor points according to arbitrary metadata including that compliant with the MIxS family of standards developed by the Genomic Standards Consortium, display jackknifed-resampled data to assess statistical confidence in clustering, perform coordinate comparisons (useful for procrustes analysis plots), and greatly reduce loading times and overall memory footprint compared with existing approaches. Additionally, ease of sharing, given EMPeror's small output file size, enables agile collaboration by allowing users to embed these visualizations via emails or web pages without the need for extra plugins.

    Conclusions

    Here we present EMPeror, an open source and web browser enabled tool with a versatile command line interface that allows researchers to perform rapid exploratory investigations of 3D visualizations of microbial community data, such as the widely used principal coordinates plots. EMPeror includes a rich set of controllers to modify features as a function of the metadata. By being specifically tailored to the requirements of microbial ecologists, EMPeror thus increases the speed with which insight can be gained from large microbiome datasets.
      Cover page: EMPeror: a tool for visualizing high-throughput microbial community data.
      • Article
      • Peer Reviewed
      UC San Diego Previously Published Works (2013)
      Microbial ecology is flourishing, and in the process, is making contributions to how the ecology and biology of large organisms is understood. Ongoing advances in sequencing technology and computational methods have enabled the collection and analysis of vast amounts of molecular data from diverse biological communities. While early studies focused on cataloguing microbial biodiversity in environments ranging from simple marine ecosystems to complex soil ecologies, more recent research is concerned with community functions and their dynamics over time. Models and concepts from traditional ecology have been used to generate new insight into microbial communities, and novel system-level models developed to explain and predict microbial interactions. The process of moving from molecular inventories to functional understanding is complex and challenging, and never more so than when many thousands of dynamic interactions are the phenomena of interest. We outline the process of how epistemic transitions are made from producing catalogues of molecules to achieving functional and predictive insight, and show how those insights not only revolutionize what is known about biological systems but also about how to do biology itself. Examples will be drawn primarily from analyses of different human microbiota, which are the microbial consortia found in and on areas of the human body, and their associated microbiomes (the genes of those communities). Molecular knowledge of these microbiomes is transforming microbiological knowledge, as well as broader aspects of human biology, health and disease.
        Cover page: From molecules to dynamic biological communities.
        • Article
        • Peer Reviewed
        UC San Diego Previously Published Works (2016)
        Many factors affect the microbiomes of humans, mice, and other mammals, but substantial challenges remain in determining which of these factors are of practical importance. Considering the relative effect sizes of both biological and technical covariates can help improve study design and the quality of biological conclusions. Care must be taken to avoid technical bias that can lead to incorrect biological conclusions. The presentation of quantitative effect sizes in addition to P values will improve our ability to perform meta-analysis and to evaluate potentially relevant biological effects. A better consideration of effect size and statistical power will lead to more robust biological conclusions in microbiome studies.
          Cover page: Tiny microbes, enormous impacts: what matters in gut microbiome studies?
          • Article
          • Peer Reviewed
          UC San Diego Previously Published Works (2021)
            Cover page: Persons With Schizophrenia Exhibit Altered Gut Microbiome Functional Pathways Related to Immune Modulation and Cardiovascular Risk
            • Article
            • Peer Reviewed
            UC San Diego Previously Published Works (2016)
            The role of the human microbiome is the subject of continued investigation resulting in increased understanding. However, current microbiome research has only scratched the surface of the variety of healthy microbiomes. Public participation in science through crowdsourcing and crowdfunding microbiome research provides a novel opportunity for both participants and investigators. However, turning participatory science into publishable data can be challenging. Clear communication with the participant base and among researchers can ameliorate some challenges. Three major aspects need to be considered: recruitment and ongoing interaction, sample collection, and data analysis. Usable data can be maximized through diligent participant interaction, careful survey design, and maintaining an open source pipeline. While participatory science will complement rather than replace traditional avenues, it presents new opportunities for studies in the microbiome and beyond.
              Cover page: Turning Participatory Microbiome Research into Usable Data: Lessons from the American Gut Project
              • Article
              • Peer Reviewed
              UC San Diego Previously Published Works (2021)
              Emerging evidence has linked the gut microbiome changes to schizophrenia. However, there has been limited research into the functional pathways by which the gut microbiota contributes to the phenotype of persons with chronic schizophrenia. We characterized the composition and functional potential of the gut microbiota in 48 individuals with chronic schizophrenia and 48 matched (sequencing plate, age, sex, BMI, and antibiotic use) non-psychiatric comparison subjects (NCs) using 16S rRNA sequencing. Patients with schizophrenia demonstrated significant beta-diversity differences in microbial composition and predicted genetic functional potential compared to NCs. Alpha-diversity of taxa and functional pathways were not different between groups. Random forests analyses revealed that the microbiome predicts differentiation of patients with schizophrenia from NCs using taxa (75% accuracy) and functional profiles (67% accuracy for KEGG orthologs, 70% for MetaCyc pathways). We utilized a new compositionally-aware method incorporating reference frames to identify differentially abundant microbes and pathways, which revealed that Lachnospiraceae is associated with schizophrenia. Functional pathways related to trimethylamine-N-oxide reductase and Kdo2-lipid A biosynthesis were altered in schizophrenia. These metabolic pathways were associated with inflammatory cytokines and risk for coronary heart disease in schizophrenia. Findings suggest potential mechanisms by which the microbiota may impact the pathophysiology of the disease through modulation of functional pathways related to immune signaling/response and lipid and glucose regulation to be further investigated in future studies.
                Cover page: Gut microbiome in Schizophrenia: Altered functional pathways related to immune modulation and atherosclerotic risk
                • Article
                • Peer Reviewed
                UC San Diego Previously Published Works (2018)
                  Cover page: Guiding longitudinal sampling in IBD cohorts.
                  • Article
                  • Peer Reviewed
                  UC San Diego Previously Published Works (2021)
                  Microbiome data are sparse and high dimensional, so effective visualization of these data requires dimensionality reduction. To date, the most commonly used method for dimensionality reduction in the microbiome is calculation of between-sample microbial differences (beta diversity), followed by principal-coordinate analysis (PCoA). Uniform Manifold Approximation and Projection (UMAP) is an alternative method that can reduce the dimensionality of beta diversity distance matrices. Here, we demonstrate the benefits and limitations of using UMAP for dimensionality reduction on microbiome data. Using real data, we demonstrate that UMAP can improve the representation of clusters, especially when the clusters are composed of multiple subgroups. Additionally, we show that UMAP provides improved correlation of biological variation along a gradient with a reduced number of coordinates of the resulting embedding. Finally, we provide parameter recommendations that emphasize the preservation of global geometry. We therefore conclude that UMAP should be routinely used as a complementary visualization method for microbiome beta diversity studies. IMPORTANCE UMAP provides an additional method to visualize microbiome data. The method is extensible to any beta diversity metric used with PCoA, and our results demonstrate that UMAP can indeed improve visualization quality and correspondence with biological and technical variables of interest. The software to perform this analysis is available under an open-source license and can be obtained at https://github.com/knightlab-analyses/umap-microbiome-benchmarking; additionally, we have provided a QIIME 2 plugin for UMAP at https://github.com/biocore/q2-umap.
                    Cover page: Uniform Manifold Approximation and Projection (UMAP) Reveals Composite Patterns and Resolves Visualization Artifacts in Microbiome Data
                    • Article
                    • Peer Reviewed
                    UC San Diego Previously Published Works (2022)
                    Feature selection is indispensable in microbiome data analysis, but it can be particularly challenging as microbiome data sets are high dimensional, underdetermined, sparse and compositional. Great efforts have recently been made on developing new methods for feature selection that handle the above data characteristics, but almost all methods were evaluated based on performance of model predictions. However, little attention has been paid to address a fundamental question: how appropriate are those evaluation criteria? Most feature selection methods often control the model fit, but the ability to identify meaningful subsets of features cannot be evaluated simply based on the prediction accuracy. If tiny changes to the data would lead to large changes in the chosen feature subset, then many selected features are likely to be a data artifact rather than real biological signal. This crucial need of identifying relevant and reproducible features motivated the reproducibility evaluation criterion such as Stability, which quantifies how robust a method is to perturbations in the data. In our paper, we compare the performance of popular model prediction metrics (MSE or AUC) with proposed reproducibility criterion Stability in evaluating four widely used feature selection methods in both simulations and experimental microbiome applications with continuous or binary outcomes. We conclude that Stability is a preferred feature selection criterion over model prediction metrics because it better quantifies the reproducibility of the feature selection method.
                      Cover page: Utilizing stability criteria in choosing feature selection methods yields reproducible results in microbiome data.
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