Towards a predictive systems-level model of the human microbiome: progress, challenges, and opportunities

Greenblum, Sharon; Chiu, Hsuan-Chao; Levy, Roie; Carr, Robert D.; Borenstein, Elhanan

doi:10.1016/j.copbio.2013.04.001

Cited by 58 publications

(60 citation statements)

References 125 publications

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“…As molecular methods improve, multiple "meta-omic" data types (such as metaproteomic and metametabolomic data) are becoming available, providing insights into such complex interspecies processes. Developing advanced analytic and modeling frameworks that integrate these data types is one of the major challenges microbial ecology currently faces (40,41). Specifically, modeling and predicting the full range of species interactions and validating predicted interactions via model systems (42) can dramatically improve our understanding of the microbiome in health and in disease.…”

Section: Discussionmentioning

confidence: 99%

Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules

Levy

Borenstein

2013

Proc. Natl. Acad. Sci. U.S.A.

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The human microbiome plays a key role in human health and is associated with numerous diseases. Metagenomic-based studies are now generating valuable information about the composition of the microbiome in health and in disease, demonstrating nonneutral assembly processes and complex co-occurrence patterns. However, the underlying ecological forces that structure the microbiome are still unclear. Specifically, compositional studies alone with no information about mechanisms of interaction, potential competition, or syntrophy, cannot clearly distinguish habitat-filtering and species assortment assembly processes. To address this challenge, we introduce a computational framework, integrating metagenomic-based compositional data with genome-scale metabolic modeling of species interaction. We use in silico metabolic network models to predict levels of competition and complementarity among 154 microbiome species and compare predicted interaction measures to species co-occurrence. Applying this approach to two large-scale datasets describing the composition of the gut microbiome, we find that species tend to co-occur across individuals more frequently with species with which they strongly compete, suggesting that microbiome assembly is dominated by habitat filtering. Moreover, species' partners and excluders exhibit distinct metabolic interaction levels. Importantly, we show that these trends cannot be explained by phylogeny alone and hold across multiple taxonomic levels. Interestingly, controlling for host health does not change the observed patterns, indicating that the axes along which species are filtered are not fully defined by macroecological host states. The approach presented here lays the foundation for a reverse-ecology framework for addressing key questions concerning the assembly of host-associated communities and for informing clinical efforts to manipulate the microbiome.T he human body is home to numerous microbial species and several complex microbial ecosystems. Advances in sequencing technologies and metagenomics now allow researchers to characterize the composition of species that inhabit the human body and the variation these communities exhibit in health and in disease (1-3). Specifically, recent studies of the microbiome have found tremendous variation among healthy individuals (1) and demonstrated clear associations between species composition and several host phenotypes including obesity (4, 5), inflammatory bowel disease (IBD) (2), and diabetes (6), as well as with external factors such as diet (7). These studies further demonstrated that, as in many other ecosystems, the composition of species in the microbiome exhibits distinct patterns that clearly deviate from a random distribution. For example, species composition in the human microbiome exhibits a significant checkerboard pattern, indicating pairs of taxa that exclude one another from shared environments (8, 9). These patterns are similar to those seen in macroecological communities, suggesting that similar pressures may act upon such m...

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Section: Discussionmentioning

confidence: 99%

Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules

Levy

Borenstein

2013

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

361

377

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“…3). This resource of reconstructions helps to address the need for literature-curated GENREs to help to analyze gut metagenomic data 38 . Actinobacteria (118) Bacteroidetes (112) Cyanobacteria (1) Firmicutes (356) Fusobacteria (18) Proteobacteria (152) Spirochaetes (1) Synergistetes (3) Tenericutes (4) Verrucomicrobia (1) Crenarchaeota (1) Euryarchaeota (5) Thaumarchaeota (1 Number of strains that take up carbon source or secrete fermentation product Figure 3 Carbon source uptake and fermentation product secretion capabilities in AGORA.…”

Section: Pairwise Interactions Of Modelsmentioning

confidence: 99%

Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota

et al. 2016

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1 r e s o u r c eChanges in the composition of the human gut microbiota have been associated with the development of chronic diseases including type 2 diabetes, obesity, and colorectal cancer 1 . Gut bacterial functions, such as synthesis of amino acids and vitamins 2 , breakdown of indigestible plant polysaccharides 3 , and production of metabolites involved in energy metabolism 4 , have been linked to human health. The use of 'omics approaches to study human microbiome communities has led to the generation of enormous data sets whose interpretations require systems biology tools to shed light on the functional capacity of gut microbiomes and their interactions with the human host 5 .In order to infer the metabolic repertoire of a gut metagenome data set, researchers usually map sequenced genes or organisms onto metabolic networks derived from the Kyoto Encyclopedia of Genes and Genomes (KEGG) 6 , and functional annotations from KEGG ontologies 7 . However, this approach cannot identify the contribution of each bacterial species to the metabolic repertoire of the whole gut microbiome, nor can it infer the effects of different gut microbial communities on host metabolism.A technique that can bridge this gap is constraint-based reconstruction and analysis (COBRA) 8 using genome-scale metabolic reconstructions (GENREs) of individual human gut microbes. GENREs are assembled using the genome sequence and experimental information 9 . These reconstructions form the basis for the development of condition-specific metabolic models whose functions are simulated and validated by comparison with experimental results. The models can be used to investigate genotype-phenotype relationships 10 , microbe-microbe interactions 11 , and host-microbe interactions 11 . Numerous tools can be used to automatically generate draft GENREs but such models contain errors 12 and are incomplete.Manual curation of draft reconstructions is time consuming because it involves an extensive literature review and experimental validation of metabolic functions 9 .To provide an extensive resource of GENREs for human gut microbes, we developed a comparative metabolic reconstruction method that enables any refinement to one metabolic reconstruction to be propagated to others. This accelerates reconstruction and improves model quality. We generated AGORA, which includes 773 gut microbes, comprising 205 genera and 605 species. All reconstructions were based on literature-derived experimental data and comparative genomics. The metabolic predictions of two AGORA reconstructions and their derived metabolic models were validated against experimental data. RESULTS Metabolic reconstruction pipelineWe devised a comparative metabolic reconstruction method (Fig. 1a,c), which is analogous to the comparative microbial genome annotation approach 13 that has enabled accelerated annotation by propagation of refinements to one genome to others. First, we downloaded draft GENREs using Model SEED 14 and KBase (US Department of Energy Systems Biology Knowledgebase, http:/...

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“…This information would allow identification of diagnostic biomarkers and may provide insight into the role of the gut microbiota in disease progression Qin et al, 2014;Zeller et al, 2014). A predictive systems-level model of the human gut microbiome is required to elucidate causalities and quantify the interactions between microbes, host, and diet (Greenblum et al, 2013;Manor et al, 2014;Shoaie and Nielsen, 2014).…”

Section: Introductionmentioning

confidence: 99%

Quantifying Diet-Induced Metabolic Changes of the Human Gut Microbiome

Shoaie¹,

Ghaffari²,

et al. 2015

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The human gut microbiome is known to be associated with various human disorders, but a major challenge is to go beyond association studies and elucidate causalities. Mathematical modeling of the human gut microbiome at a genome scale is a useful tool to decipher microbe-microbe, diet-microbe and microbe-host interactions. Here, we describe the CASINO (Community And Systems-level INteractive Optimization) toolbox, a comprehensive computational platform for analysis of microbial communities through metabolic modeling. We first validated the toolbox by simulating and testing the performance of single bacteria and whole communities in vitro. Focusing on metabolic interactions between the diet, gut microbiota, and host metabolism, we demonstrated the predictive power of the toolbox in a diet-intervention study of 45 obese and overweight individuals and validated our predictions by fecal and blood metabolomics data. Thus, modeling could quantitatively describe altered fecal and serum amino acid levels in response to diet intervention.

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Towards a predictive systems-level model of the human microbiome: progress, challenges, and opportunities

Cited by 58 publications

References 125 publications

Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules

Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules

Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota

Quantifying Diet-Induced Metabolic Changes of the Human Gut Microbiome

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