Microbiome 101: Studying, Analyzing, and Interpreting Gut Microbiome Data for Clinicians.

Advances in technical capabilities for reading complex human microbiomes are leading to an explosion of microbiome research, leading in turn to intense interest among clinicians in applying these techniques to their patients. In this review, we discuss the content of the human microbiome, including intersubject and intrasubject variability, considerations of study design including important confounding factors, and different methods in the laboratory and on the computer to read the microbiome and its resulting gene products and metabolites. We highlight several common pitfalls for clinicians, including the expectation that an individual's microbiome will be stable, that diet can induce rapid changes that are large compared with the differences among subjects, that everyone has essentially the same core stool microbiome, and that different laboratory and computational methods will yield essentially the same results. We also highlight the current limitations and future promise of these techniques, with the expectation that an understanding of these considerations will help accelerate the path toward routine clinical application of these techniques developed in research settings.

Longitudinal flux balance analyses of a patient with episodic colonic inflammation reveals microbiome metabolic dynamics.

We report the first use of constraint-based microbial community modeling on a single individual with episodic inflammation of the gastrointestinal tract, who has a well documented set of colonic inflammatory biomarkers, as well as metagenomically-sequenced fecal time series covering seven dates over 16 months. Between the first two time steps the individual was treated with both steroids and antibiotics. Our methodology enabled us to identify numerous time-correlated microbial species and metabolites. We found that the individual's dynamical microbial ecology in the disease state led to time-varying in silico overproduction, compared to healthy controls, of more than 24 biologically important metabolites, including methane, thiamine, formaldehyde, trimethylamine N-oxide, folic acid, serotonin, histamine, and tryptamine. The microbe-metabolite contribution analysis revealed that some Dialister species changed metabolic pathways according to the inflammation phases. At the first time point, characterized by the highest levels of serum (complex reactive protein) and fecal (calprotectin) inflammation biomarkers, they produced L-serine or formate. The production of the compounds, through a cascade effect, was mediated by the interaction with pathogenic Escherichia coli strains and Desulfovibrio piger. We integrated the microbial community metabolic models of each time point with a male whole-body, organ-resolved model of human metabolism to track the metabolic consequences of dysbiosis at different body sites. The presence of D. piger in the gut microbiome influenced the sulfur metabolism with a domino effect affecting the liver. These results revealed large longitudinal variations in an individual's gut microbiome ecology and metabolite production, potentially impacting other organs in the body. Future simulations with more time points from an individual could permit us to assess how external drivers, such as diet change or medical interventions, drive microbial community dynamics.

Gastrointestinal Surgery for Inflammatory Bowel Disease Persistently Lowers Microbiome and Metabolome Diversity.

BACKGROUND: Many studies have investigated the role of the microbiome in inflammatory bowel disease (IBD), but few have focused on surgery specifically or its consequences on the metabolome that may differ by surgery type and require longitudinal sampling. Our objective was to characterize and contrast microbiome and metabolome changes after different surgeries for IBD, including ileocolonic resection and colectomy. METHODS: The UC San Diego IBD Biobank was used to prospectively collect 332 stool samples from 129 subjects (50 ulcerative colitis; 79 Crohn's disease). Of these, 21 with Crohn's disease had ileocolonic resections, and 17 had colectomies. We used shotgun metagenomics and untargeted liquid chromatography followed by tandem mass spectrometry metabolomics to characterize the microbiomes and metabolomes of these patients up to 24 months after the initial sampling. RESULTS: The species diversity and metabolite diversity both differed significantly among groups (species diversity: Mann-Whitney U test P value = 7.8e-17; metabolomics, P-value = 0.0043). Escherichia coli in particular expanded dramatically in relative abundance in subjects undergoing surgery. The species profile was better able to classify subjects according to surgery status than the metabolite profile (average precision 0.80 vs 0.68). CONCLUSIONS: Intestinal surgeries seem to reduce the diversity of the gut microbiome and metabolome in IBD patients, and these changes may persist. Surgery also further destabilizes the microbiome (but not the metabolome) over time, even relative to the previously established instability in the microbiome of IBD patients. These long-term effects and their consequences for health outcomes need to be studied in prospective longitudinal trials linked to microbiome-involved phenotypes.

De Novo Peptide Sequencing Reveals Many Cyclopeptides in the Human Gut and Other Environments.

Cyclic and branch cyclic peptides (cyclopeptides) represent a class of bioactive natural products that include many antibiotics and anti-tumor compounds. Despite the recent advances in metabolomics analysis, still little is known about the cyclopeptides in the human gut and their possible interactions due to a lack of computational analysis pipelines that are applicable to such compounds. Here, we introduce CycloNovo, an algorithm for automated de novo cyclopeptide analysis and sequencing that employs de Bruijn graphs, the workhorse of DNA sequencing algorithms, to identify cyclopeptides in spectral datasets. CycloNovo reconstructed 32 previously unreported cyclopeptides (to the best of our knowledge) in the human gut and reported over a hundred cyclopeptides in other environments represented by various spectra on Global Natural Products Social Molecular Network (GNPS). https://github.com/bbehsaz/cyclonovo.

Evaluating Metagenomic Prediction of the Metaproteome in a 4.5-Year Study of a Patient with Crohn's Disease.

Although genetic approaches are the standard in microbiome analysis, proteome-level information is largely absent. This discrepancy warrants a better understanding of the relationship between gene copy number and protein abundance, as this is crucial information for inferring protein-level changes from metagenomic data. As it remains unknown how metaproteomic systems evolve during dynamic disease states, we leveraged a 4.5-year fecal time series using samples from a single patient with colonic Crohn's disease. Utilizing multiplexed quantitative proteomics and shotgun metagenomic sequencing of eight time points in technical triplicate, we quantified over 29,000 protein groups and 110,000 genes and compared them to five protein biomarkers of disease activity. Broad-scale observations were consistent between data types, including overall clustering by principal-coordinate analysis and fluctuations in Gene Ontology terms related to Crohn's disease. Through linear regression, we determined genes and proteins fluctuating in conjunction with inflammatory metrics. We discovered conserved taxonomic differences relevant to Crohn's disease, including a negative association of Faecalibacterium and a positive association of Escherichia with calprotectin. Despite concordant associations of genera, the specific genes correlated with these metrics were drastically different between metagenomic and metaproteomic data sets. This resulted in the generation of unique functional interpretations dependent on the data type, with metaproteome evidence for previously investigated mechanisms of dysbiosis. An example of one such mechanism was a connection between urease enzymes, amino acid metabolism, and the local inflammation state within the patient. This proof-of-concept approach prompts further investigation of the metaproteome and its relationship with the metagenome in biologically complex systems such as the microbiome. IMPORTANCE A majority of current microbiome research relies heavily on DNA analysis. However, as the field moves toward understanding the microbial functions related to healthy and disease states, it is critical to evaluate how changes in DNA relate to changes in proteins, which are functional units of the genome. This study tracked the abundance of genes and proteins as they fluctuated during various inflammatory states in a 4.5-year study of a patient with colonic Crohn's disease. Our results indicate that despite a low level of correlation, taxonomic associations were consistent in the two data types. While there was overlap of the data types, several associations were uniquely discovered by analyzing the metaproteome component. This case study provides unique and important insights into the fundamental relationship between the genes and proteins of a single individual's fecal microbiome associated with clinical consequences.

Optimizing sequencing protocols for leaderboard metagenomics by combining long and short reads.

As metagenomic studies move to increasing numbers of samples, communities like the human gut may benefit more from the assembly of abundant microbes in many samples, rather than the exhaustive assembly of fewer samples. We term this approach leaderboard metagenome sequencing. To explore protocol optimization for leaderboard metagenomics in real samples, we introduce a benchmark of library prep and sequencing using internal references generated by synthetic long-read technology, allowing us to evaluate high-throughput library preparation methods against gold-standard reference genomes derived from the samples themselves. We introduce a low-cost protocol for high-throughput library preparation and sequencing.

Phylogenomics of 10,575 genomes reveals evolutionary proximity between domains Bacteria and Archaea.

Rapid growth of genome data provides opportunities for updating microbial evolutionary relationships, but this is challenged by the discordant evolution of individual genes. Here we build a reference phylogeny of 10,575 evenly-sampled bacterial and archaeal genomes, based on a comprehensive set of 381 markers, using multiple strategies. Our trees indicate remarkably closer evolutionary proximity between Archaea and Bacteria than previous estimates that were limited to fewer "core" genes, such as the ribosomal proteins. The robustness of the results was tested with respect to several variables, including taxon and site sampling, amino acid substitution heterogeneity and saturation, non-vertical evolution, and the impact of exclusion of candidate phyla radiation (CPR) taxa. Our results provide an updated view of domain-level relationships.

Are microbiome studies ready for hypothesis-driven research?

Hypothesis-driven research has led to many scientific advances, but hypotheses cannot be tested in isolation: rather, they require a framework of aggregated scientific knowledge to allow questions to be posed meaningfully. This framework is largely still lacking in microbiome studies, and the only way to create it is by discovery-driven, tool-driven, and standards-driven research projects. Here we illustrate these issues using several such non-hypothesis-driven projects from our own laboratories, including spatial mapping, the American Gut Project, the Earth Microbiome Project (which is an umbrella project integrating many smaller hypothesis-driven projects), and the knowledgebase-driven tools GNPS and Qiita. We argue that an investment of community resources in infrastructure tasks, and in the controls and standards that underpin them, will greatly enhance the investment in hypothesis-driven research programs.

Escherichia coli B2 strains prevalent in inflammatory bowel disease patients have distinct metabolic capabilities that enable colonization of intestinal mucosa.

BACKGROUND: Escherichia coli is considered a leading bacterial trigger of inflammatory bowel disease (IBD). E. coli isolates from IBD patients primarily belong to phylogroup B2. Previous studies have focused on broad comparative genomic analysis of E. coli B2 isolates, and identified virulence factors that allow B2 strains to reside within human intestinal mucosa. Metabolic capabilities of E. coli strains have been shown to be related to their colonization site, but remain unexplored in IBD-associated strains. RESULTS: In this study, we utilized pan-genome analysis and genome-scale models (GEMs) of metabolism to study metabolic capabilities of IBD-associated E. coli B2 strains. The study yielded three results: i) Pan-genome analysis of 110 E. coli strains (including 53 isolates from IBD studies) revealed discriminating metabolic genes between B2 strains and other strains; ii) Both comparative genomic analysis and GEMs suggested that B2 strains have an advantage in degrading and utilizing sugars derived from mucus glycan, and iii) GEMs revealed distinct metabolic features in B2 strains that potentially allow them to utilize energy more efficiently. For example, B2 strains lack the enzymes to degrade amadori products, but instead rely on neighboring bacteria to convert these substrates into a more readily usable and potentially less sought after product. CONCLUSIONS: Taken together, these results suggest that the metabolic capabilities of B2 strains vary significantly from those of other strains, enabling B2 strains to colonize intestinal mucosa.The results from this study motivate a broad experimental assessment of the nutritional effects on E. coli B2 pathophysiology in IBD patients.

Creating a 3D microbial and chemical snapshot of a human habitat.

One of the goals of forensic science is to identify individuals and their lifestyle by analyzing the trace signatures left behind in built environments. Here, microbiome and metabolomic methods were used to see how its occupants used an office and to also gain insights into the lifestyle characteristics such as diet, medications, and personal care products of the occupants. 3D molecular cartography, a molecular visualization technology, was used in combination with mass spectrometry and microbial inventories to highlight human-environmental interactions. Molecular signatures were correlated with the individuals as well as their interactions with this indoor environment. There are person-specific chemical and microbial signatures associated with this environment that directly relate who had touched objects such as computers, computer mice, cell phones, desk phone, table or desks. By combining molecular and microbial investigation forensic strategies, this study offers novel insights to investigators who value the reconstructing of human lifestyle and characterization of human environmental interaction.

Metagenomics-Based, Strain-Level Analysis of Escherichia coli From a Time-Series of Microbiome Samples From a Crohn's Disease Patient.

Dysbiosis of the gut microbiome, including elevated abundance of putative leading bacterial triggers such as E. coli in inflammatory bowel disease (IBD) patients, is of great interest. To date, most E. coli studies in IBD patients are focused on clinical isolates, overlooking their relative abundances and turnover over time. Metagenomics-based studies, on the other hand, are less focused on strain-level investigations. Here, using recently developed bioinformatic tools, we analyzed the abundance and properties of specific E. coli strains in a Crohns disease (CD) patient longitudinally, while also considering the composition of the entire community over time. In this report, we conducted a pilot study on metagenomic-based, strain-level analysis of a time-series of E. coli strains in a left-sided CD patient, who exhibited sustained levels of E. coli greater than 100X healthy controls. We: (1) mapped out the composition of the gut microbiome over time, particularly the presence of E. coli strains, and found that the abundance and dominance of specific E. coli strains in the community varied over time; (2) performed strain-level de novo assemblies of seven dominant E. coli strains, and illustrated disparity between these strains in both phylogenetic origin and genomic content; (3) observed that strain ST1 (recovered during peak inflammation) is highly similar to known pathogenic AIEC strains NC101 and LF82 in both virulence factors and metabolic functions, while other strains (ST2-ST7) that were collected during more stable states displayed diverse characteristics; (4) isolated, sequenced, experimentally characterized ST1, and confirmed the accuracy of the de novo assembly; and (5) assessed growth capability of ST1 with a newly reconstructed genome-scale metabolic model of the strain, and showed its potential to use substrates found abundantly in the human gut to outcompete other microbes. In conclusion, inflammation status (assessed by the blood C-reactive protein and stool calprotectin) is likely correlated with the abundance of a subgroup of E. coli strains with specific traits. Therefore, strain-level time-series analysis of dominant E. coli strains in a CD patient is highly informative, and motivates a study of a larger cohort of IBD patients.

American Gut: an Open Platform for Citizen Science Microbiome Research.

Although much work has linked the human microbiome to specific phenotypes and lifestyle variables, data from different projects have been challenging to integrate and the extent of microbial and molecular diversity in human stool remains unknown. Using standardized protocols from the Earth Microbiome Project and sample contributions from over 10,000 citizen-scientists, together with an open research network, we compare human microbiome specimens primarily from the United States, United Kingdom, and Australia to one another and to environmental samples. Our results show an unexpected range of beta-diversity in human stool microbiomes compared to environmental samples; demonstrate the utility of procedures for removing the effects of overgrowth during room-temperature shipping for revealing phenotype correlations; uncover new molecules and kinds of molecular communities in the human stool metabolome; and examine emergent associations among the microbiome, metabolome, and the diversity of plants that are consumed (rather than relying on reductive categorical variables such as veganism, which have little or no explanatory power). We also demonstrate the utility of the living data resource and cross-cohort comparison to confirm existing associations between the microbiome and psychiatric illness and to reveal the extent of microbiome change within one individual during surgery, providing a paradigm for open microbiome research and education. IMPORTANCE We show that a citizen science, self-selected cohort shipping samples through the mail at room temperature recaptures many known microbiome results from clinically collected cohorts and reveals new ones. Of particular interest is integrating n = 1 study data with the population data, showing that the extent of microbiome change after events such as surgery can exceed differences between distinct environmental biomes, and the effect of diverse plants in the diet, which we confirm with untargeted metabolomics on hundreds of samples.

Bringing the Dynamic Microbiome to Life with Animations.

Our bodies and natural environment contain complex microbial communities, colloquially termed microbiomes. We previously created a web-based application, EMPeror, for visualizing ordinations derived from comparisons of these microbiome communities. We have now improved EMPeror to create interactive animations that connect successive samples to highlight patterns over time.

Toward more transparent and reproducible omics studies through a common metadata checklist and data publications.

Biological processes are fundamentally driven by complex interactions between biomolecules. Integrated high-throughput omics studies enable multifaceted views of cells, organisms, or their communities. With the advent of new post-genomics technologies, omics studies are becoming increasingly prevalent; yet the full impact of these studies can only be realized through data harmonization, sharing, meta-analysis, and integrated research. These essential steps require consistent generation, capture, and distribution of metadata. To ensure transparency, facilitate data harmonization, and maximize reproducibility and usability of life sciences studies, we propose a simple common omics metadata checklist. The proposed checklist is built on the rich ontologies and standards already in use by the life sciences community. The checklist will serve as a common denominator to guide experimental design, capture important parameters, and be used as a standard format for stand-alone data publications. The omics metadata checklist and data publications will create efficient linkages between omics data and knowledge-based life sciences innovation and, importantly, allow for appropriate attribution to data generators and infrastructure science builders in the post-genomics era. We ask that the life sciences community test the proposed omics metadata checklist and data publications and provide feedback for their use and improvement.

Toward More Transparent and Reproducible Omics Studies Through a Common Metadata Checklist and Data Publications.

Biological processes are fundamentally driven by complex interactions between biomolecules. Integrated high-throughput omics studies enable multifaceted views of cells, organisms, or their communities. With the advent of new post-genomics technologies, omics studies are becoming increasingly prevalent; yet the full impact of these studies can only be realized through data harmonization, sharing, meta-analysis, and integrated research. These essential steps require consistent generation, capture, and distribution of metadata. To ensure transparency, facilitate data harmonization, and maximize reproducibility and usability of life sciences studies, we propose a simple common omics metadata checklist. The proposed checklist is built on the rich ontologies and standards already in use by the life sciences community. The checklist will serve as a common denominator to guide experimental design, capture important parameters, and be used as a standard format for stand-alone data publications. The omics metadata checklist and data publications will create efficient linkages between omics data and knowledge-based life sciences innovation and, importantly, allow for appropriate attribution to data generators and infrastructure science builders in the post-genomics era. We ask that the life sciences community test the proposed omics metadata checklist and data publications and provide feedback for their use and improvement.

Quantifying your body: a how-to guide from a systems biology perspective.

During the coming decade we will see an accelerated digital transformation of healthcare. Leading this change within the institutional medical community are both the move to digital medical records and the use of digital biomedical measurement devices. In addition to this institutional evolution, there is a non-institutional, bottom-up, unorganized, highly idiosyncratic movement by early adopters to "quantify" their own bodies. In this article, I share my decade-long personal experience of tracking many blood and stool biomarkers, which provide insight into the health or disease of major subsystems of my body. These results are interpreted in the context of the genetics of my human DNA and that of the microbes in my gut. Even though I am a computer scientist and not a medical professional, by using commercially available tests and a systems biology integrative approach, I have become an early example of Leroy Hood's vision of the emergence of predictive, preventive, personalized, and participatory (P4) medicine. It is an individual's story illustrating how each of us can contribute to realizing this paradigm shift.