Systems biology of host–microbe metabolomics

Heinken, Almut; Thiele, Ines

doi:10.1002/wsbm.1301

Cited by 81 publications

(72 citation statements)

References 202 publications

(445 reference statements)

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“…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.…”

Section: R E S O U R C Ementioning

confidence: 99%

“…AGORA enables superior simulations to address mechanistic questions about host-microbe co-metabolism. We envisage the combination of AGORA models into strain-resolved microbial community models and predictions of how those community models interact with human metabolic models 22 could be used to systematically investigate host-microbiome interactions 11 . Table 9) are slower in the paired simulation compared with each microbe's in silico mono-culture growth rate on the same diet (Supplementary Table 12).…”

Section: Pairwise Interactions Of Modelsmentioning

confidence: 99%

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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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Section: R E S O U R C Ementioning

confidence: 99%

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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“…Recent reviews highlight various types of mathematical models applied to cancer metabolism [45] and host-microbial metabolomics [46]. Markert and Vazquez highlight several different types of models, including flux balance models [47], which have been successfully applied to study cellular metabolism under steady state conditions and are being expanded to include kinetic data.…”

Section: From Data To Knowledgementioning

confidence: 99%

Metabolite and Microbiome Interplay in Cancer Immunotherapy

et al. 2016

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The role of the host microbiome has come to the forefront as a potential modulator of cancer metabolism, and could be a future target for precision medicine. A recent study revealed that in colon cancer, bacteria form polysaccharide matrices called biofilms at a high frequency in the proximal colon. Comprehensive untargeted and stable isotope assisted metabolomic analysis revealed that the bacteria utilize polyamine metabolites produced from colon adenomas/carcinomas to build these protective biofilms, and may contribute to the inflammation and proliferation of colon cancer. This study highlighted the importance of finding the biological origin of a metabolite and assessing its metabolism and mechanism of action. This led to a better understanding of host and microbial interactions, thereby aiding therapeutic design for cancer. In this review we will discuss methodologies for identifying the biological origin and roles of metabolites in cancer progression, and discuss the interactions of the microbiome and metabolites in immunity and cancer treatment, focusing on the flourishing field of cancer immunotherapy.

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“…Metabolic products are often codependent on dietary nutrients, microbial composition, and host enzymatic activities (145). In particular, the functional output of the gut microbiota, including short-chain fatty acids (SCFAs), amino acids, and secondary biliary acids, is thought to modulate the pathogenesis of obesity (146).…”

Section: R E V I E W S E R I E S : T R a N S P L A N Tat I O Nmentioning

confidence: 99%