"The gut microbiome is a complex biological system that impacts many aspects of human health. While several studies have identified long lists of microbes implicated in disease, why they are associated with differential host phenotypes remains unclear. Metabolomics can complement sequencing-based approaches by providing a snapshot of host-microbial co-metabolism, however, its use in the field of microbiomics is still in its infancy. The objectives of my project are therefore to (i) to become proficient in metabolomics data processing and analysis and translate this knowledge into the development of bioinformatics tools for metabolomics data, (ii) to improve biological insights obtained from untargeted metabolomics data in an open-source and transparent matter, and (iii) to implement a novel bioinformatic framework to integrate untargeted metabolomics data and taxonomic microbial data to model changes in microbial metabolism. Ultimately, this framework will permit researchers to understand metabolic mechanisms of the gut microbiome and aide the design of novel therapeutics"--

Complex communities of microbes reside in and on humans, where they closely interact with their hosts by performing a massively diverse array of metabolic reactions. Genomic and metabolomic technologies can now describe both the taxonomic profile of these communities and their metabolic products in unprecedented detail. By measuring both microbial composition and metabolite phenotypes from the same samples, and using the resulting datasets to make and evaluate predictions on the links between microbes and metabolites, it may be possible to infer and characterize metabolic mechanisms occurring in complex natural communities. However, relatively few computational analysis tools exist to integrate and make sense of such “microbiome-metabolome” datasets. In this dissertation, I describe the development and application of methods that use these datasets and reference databases to identify and evaluate relationships between microbes and metabolites. After introducing the current state of knowledge and available tools in the study of how microbial metabolites impact human health and disease, I present an initial framework for integrating microbiome and metabolomics datasets using metabolic modeling. I demonstrate its ability to predict and explain metabolic shifts in bacterial vaginosis, and further illustrate its application in two case studies, deciphering diet-microbiome interactions in mice and characterizing metabolic mechanisms in the microbiota of children with autism spectrum disorder. In order to compare this approach with alternatives and gain a better understanding of the limiting factors in microbiome-metabolome data analysis, I next describe a comprehensive framework for defining gold-standard mechanistic links between microbes and metabolites and using simulations to evaluate and compare our ability to recover them across different datasets and analysis methods. Finally, informed by the previous applications and evaluations, I introduce MIMOSA2, an updated software tool for inferring mechanistic links from microbiome-metabolome datasets. Together, this work reinforces and advances the utility of metabolic modeling for the analysis and interpretation of large-scale microbiome-metabolome studies.

High-throughput sequencing technologies are widely used to study microbial ecology across species and habitats in order to understand the impacts of microbial communities on host health, metabolism, and the environment. Due to the dynamic nature of microbial communities, longitudinal microbiome analyses play an essential role in these types of investigations. Key questions in microbiome studies aim at identifying specific microbial taxa, enterotypes, genes, or metabolites associated with specific outcomes, as well as potential factors that influence microbial communities. However, the characteristics of microbiome data, such as sparsity and skewedness, combined with the nature of data collection, reflected often as uneven sampling or missing data, make commonly employed statistical approaches to handle repeated measures in longitudinal studies inadequate. Therefore, many researchers have begun to investigate methods that could improve incorporating these features when studying clinical, host, metabolic, or environmental associations with longitudinal microbiome data. In addition to the inferential aspect, it is also becoming apparent that visualization of high dimensional data in a way which is both intelligible and comprehensive is another difficult challenge that microbiome researchers face. Visualization is crucial in both the analysis and understanding of metagenomic data. Researchers must create clear graphic representations that give biological insight without being overly complicated. Thus, this Research Topic seeks to both review and provide novels approaches that are being developed to integrate microbiome data and complex metadata into meaningful mathematical, statistical and computational models. We believe this topic is fundamental to understanding the importance of microbial communities and provides a useful reference for other investigators approaching the field.

Compared with other research fields, both microbiome and metabolomics data are complicated and have some unique characteristics, respectively. Thus, choosing an appropriate statistical test or method is a very important step in the analysis of microbiome and metabolomics data. However, this is still a difficult task for those biomedical researchers without a statistical background and for those biostatisticians who do not have research experiences in these fields. Graduate students studying microbiome and metabolomics; statisticians, working on microbiome and metabolomics projects, either for their own research, or for their collaborative research for experimental design, grant application, and data analysis; and researchers who investigate biomedical and biochemical projects with the microbiome, metabolome, and multi-omics data analysis will benefit from reading this work.

A comparative, holistic synthesis of microbiome research, spanning soil, plant, animal and human hosts.

In this book, the latest tools available for functional metagenomics research are described.This research enables scientists to directly access the genomes from diverse microbial genomes at one time and study these “metagenomes”. Using the modern tools of genome sequencing and cloning, researchers have now been able to harness this astounding metagenomic diversity to understand and exploit the diverse functions of microorganisms. Leading scientists from around the world demonstrate how these approaches have been applied in many different settings, including aquatic and terrestrial habitats, microbiomes, and many more environments. This is a highly informative and carefully presented book, providing microbiologists with a summary of the latest functional metagenomics literature on all specific habitats.

Metabolome analysis is now recognized as a crucial component of functional genomic and systems biology investigations. Innovative approaches to the study of metabolic regulation in microbial, plant and animal systems are increasingly facilitating the emergence of systems approaches in biology. This book highlights analytical and bioinformatics strategies now available for investigating metabolic networks in microbial, plant and animal systems. The contributing authors are world leaders in this field and they present an unambiguous case for pursuing metabolome analysis as a means to attain a systems level understanding of complex biological systems.