Human-microbiota interactions in health and disease :bioinformatics analyses of gut microbiome datasets

EngD ThesisThe human gut harbours a vast diversity of microbial cells, collectively known as the gut microbiota, that are crucial for human health and dysfunctional in many of the most prevalent chronic diseases. Until recently culture dependent methods limited our ability to study the microbiota in depth including the collective genomes of the microbiota, the microbiome. Advances in culture independent metagenomic sequencing technologies have since provided new insights into the microbiome and lead to a rapid expansion of data rich resources for microbiome research. These high throughput sequencing methods and large datasets provide new opportunities for research with an emphasis on bioinformatics analyses and a novel field for drug discovery through data mining. In this thesis I explore a range of metagenomics analyses to extract insights from metagenomics data and inform drug discovery in the microbiota. Firstly I survey the existing technologies and data sources available for data mining therapeutic targets. Then I analyse 16S metagenomics data combined with metabolite data from mice to investigate the treatment model of a proposed antibiotic treatment targetting the microbiota. Then I investigate the occurence frequency and diversity of proteases in metagenomics data in order to inform understanding of host-microbiota-diet interactions through protein and peptide associated glycan degradation by the gut microbiota. Finally I develop a system to facilitate the process of integrating metagenomics data for gene annotations. One of the main challenges in leveraging the scale of data availability in microbiome research is managing the data resources from microbiome studies. Through a series of analytical studies I used metagenomics data to identify community trends, to demonstrate therapeutic interventions and to do a wide scale screen for proteases that are central to human-microbiota interactions. These studies articulated the requirement for a computational framework to integrate and access metagenomics data in a reproducible way using a scalable data store. The thesis concludes explaining how data integration in microbiome research is needed to provide the insights into metagenomics data that are required for drug discovery

From metagenomics to the metagenome: Conceptual change and the rhetoric of translational genomic research

As the international genomic research community moves from the tool-making efforts of the Human Genome Project into biomedical applications of those tools, new metaphors are being suggested as useful to understanding how our genes work – and for understanding who we are as biological organisms. In this essay we focus on the Human Microbiome Project as one such translational initiative. The HMP is a new ‘metagenomic’ research effort to sequence the genomes of human microbiological flora, in order to pursue the interesting hypothesis that our ‘microbiome’ plays a vital and interactive role with our human genome in normal human physiology. Rather than describing the human genome as the ‘blueprint’ for human nature, the promoters of the HMP stress the ways in which our primate lineage DNA is interdependent with the genomes of our microbiological flora. They argue that the human body should be understood as an ecosystem with multiple ecological niches and habitats in which a variety of cellular species collaborate and compete, and that human beings should be understood as ‘superorganisms’ that incorporate multiple symbiotic cell species into a single individual with very blurry boundaries. These metaphors carry interesting philosophical messages, but their inspiration is not entirely ideological. Instead, part of their cachet within genome science stems from the ways in which they are rooted in genomic research techniques, in what philosophers of science have called a ‘tools-to-theory’ heuristic. Their emergence within genome science illustrates the complexity of conceptual change in translational research, by showing how it reflects both aspirational and methodological influences

TGFβ/BMP immune signaling affects abundance and function of C. elegans gut commensals.

The gut microbiota contributes to host health and fitness, and imbalances in its composition are associated with pathology. However, what shapes microbiota composition is not clear, in particular the role of genetic factors. Previous work in Caenorhabditis elegans defined a characteristic worm gut microbiota significantly influenced by host genetics. The current work explores the role of central regulators of host immunity and stress resistance, employing qPCR and CFU counts to measure abundance of core microbiota taxa in mutants raised on synthetic communities of previously-isolated worm gut commensals. This revealed a bloom, specifically of Enterobacter species, in immune-compromised TGFβ/BMP mutants. Imaging of fluorescently labeled Enterobacter showed that TGFβ/BMP-exerted control operated primarily in the anterior gut and depended on multi-tissue contributions. Enterobacter commensals are common in the worm gut, contributing to infection resistance. However, disruption of TGFβ/BMP signaling turned a normally beneficial Enterobacter commensal to pathogenic. These results demonstrate specificity in gene-microbe interactions underlying gut microbial homeostasis and highlight the pathogenic potential of their disruption

The chemical interactome space between the human host and the genetically defined gut metabotypes

The bacteria that colonize the gastrointestinal tracts of mammals represent a highly selected microbiome that has a profound influence on human physiology by shaping the host's metabolic and immune system activity. Despite the recent advances on the biological principles that underlie microbial symbiosis in the gut of mammals, mechanistic understanding of the contributions of the gut microbiome and how variations in the metabotypes are linked to the host health are obscure. Here, we mapped the entire metabolic potential of the gut microbiome based solely on metagenomics sequencing data derived from fecal samples of 124 Europeans (healthy, obese and with inflammatory bowel disease). Interestingly, three distinct clusters of individuals with high, medium and low metabolic potential were observed. By illustrating these results in the context of bacterial population, we concluded that the abundance of the Prevotella genera is a key factor indicating a low metabolic potential. These metagenome-based metabolic signatures were used to study the interaction networks between bacteria-specific metabolites and human proteins. We found that thirty-three such metabolites interact with disease-relevant protein complexes several of which are highly expressed in cells and tissues involved in the signaling and shaping of the adaptive immune system and associated with squamous cell carcinoma and bladder cancer. From this set of metabolites, eighteen are present in DrugBank providing evidence that we carry a natural pharmacy in our guts. Furthermore, we established connections between the systemic effects of non-antibiotic drugs and the gut microbiome of relevance to drug side effects and health-care solutions.link_to_subscribed_fulltex

Harnessing machine learning for development of microbiome therapeutics

The last twenty years of seminal microbiome research has uncovered microbiota's intrinsic relationship with human health. Studies elucidating the relationship between an unbalanced microbiome and disease are currently published daily. As such, microbiome big data have become a reality that provide a mine of information for the development of new therapeutics. Machine learning (ML), a branch of artificial intelligence, offers powerful techniques for big data analysis and prediction-making, that are out of reach of human intellect alone. This review will explore how ML can be applied for the development of microbiome-targeted therapeutics. A background on ML will be given, followed by a guide on where to find reliable microbiome big data. Existing applications and opportunities will be discussed, including the use of ML to discover, design, and characterize microbiome therapeutics. The use of ML to optimize advanced processes, such as 3D printing and in silico prediction of drug-microbiome interactions, will also be highlighted. Finally, barriers to adoption of ML in academic and industrial settings will be examined, concluded by a future outlook for the field

Gut microbiome-host interactions in health and disease

The gut microbiome is the term given to describe the vast collection of symbiotic microorganisms in the human gastrointestinal system and their collective interacting genomes. Recent studies have suggested that the gut microbiome performs numerous important biochemical functions for the host, and disorders of the microbiome are associated with many and diverse human disease processes. Systems biology approaches based on next generation 'omics' technologies are now able to describe the gut microbiome at a detailed genetic and functional (transcriptomic, proteomic and metabolic) level, providing new insights into the importance of the gut microbiome in human health, and they are able to map microbiome variability between species, individuals and populations. This has established the importance of the gut microbiome in the disease pathogenesis for numerous systemic disease states, such as obesity and cardiovascular disease, and in intestinal conditions, such as inflammatory bowel disease. Thus, understanding microbiome activity is essential to the development of future personalized strategies of healthcare, as well as potentially providing new targets for drug development. Here, we review recent metagenomic and metabonomic approaches that have enabled advances in understanding gut microbiome activity in relation to human health, and gut microbial modulation for the treatment of disease. We also describe possible avenues of research in this rapidly growing field with respect to future personalized healthcare strategies