Functional Metabolic Map of Faecalibacterium prausnitzii, a Beneficial Human Gut Microbe

Heinken, Almut; Khan, M. Tanweer; Paglia, Giuseppe; Rodionov, Dmitry A.; Harmsen, Hermie J. M.; Thiele, Ines

doi:10.1128/jb.01780-14

Cited by 208 publications

(219 citation statements)

References 61 publications

Supporting

Mentioning

199

Contrasting

Unclassified

Order By: Relevance

“…The models predicted widely varying individual species growth rates as a function of position in the biofilm and the secretion of eight primary byproducts that served as putative cross-fed metabolites in multispecies simulations: acetate, butyrate, CO 2 , ethanol, formate, lactate, propionate and succinate. The predicted byproducts secreted by each species were in agreement with experimental observations [60][61][62].…”

Section: Discussionsupporting

confidence: 76%

“…To our knowledge, none of these relationships have been directly demonstrated through co-culture experiments with the participating species. However, the two acetate cross-feeding relationships were expected due to the demonstrated abilities of both B. thetaiotaomicron [60] and E. coli [62] to secrete acetate and of F. prausnitzii to consume acetate [61].…”

Section: Discussionmentioning

confidence: 99%

“…For the sake of brevity, the formulation is only briefly reviewed here with an emphasis on the novel features introduced for the gut microbiome model. We utilized genome-scale metabolic reconstructions for the commensal gut bacteria B. thetaiotaomicron [60], F. prausnitzii [61] and E. coli [62] to model individual species metabolism. A synthetic media was created by combining all the in silico nutrient requirements of the three bacteria assuming that growth was limited only by the primary carbon source glucose and three amino acids (methionine, serine, tryptophan) essential for F. prausnitzii.…”

Section: Multispecies Biofilm Model Formulationmentioning

confidence: 99%

See 2 more Smart Citations

Byproduct Cross Feeding and Community Stability in an In Silico Biofilm Model of the Gut Microbiome

Henson

Phalak

2017

Processes

View full text Add to dashboard Cite

Abstract:The gut microbiome is a highly complex microbial community that strongly impacts human health and disease. The two dominant phyla in healthy humans are Bacteroidetes and Firmicutes, with minor phyla such as Proteobacteria having elevated abundances in various disease states. While the gut microbiome has been widely studied, relatively little is known about the role of interspecies interactions in promoting microbiome stability and function. We developed a biofilm metabolic model of a very simple gut microbiome community consisting of a representative bacteroidete (Bacteroides thetaiotaomicron), firmicute (Faecalibacterium prausnitzii) and proteobacterium (Escherichia coli) to investigate the putative role of metabolic byproduct cross feeding between species on community stability, robustness and flexibility. The model predicted coexistence of the three species only if four essential cross-feeding relationships were present. We found that cross feeding allowed coexistence to be robustly maintained for large variations in biofilm thickness and nutrient levels. However, the model predicted that community composition and short chain fatty acid levels could be strongly affected only over small ranges of byproduct uptake rates, indicating a possible lack of flexibility in our cross-feeding mechanism. Our model predictions provide new insights into the impact of byproduct cross feeding and yield experimentally testable hypotheses about gut microbiome community stability.

show abstract

Section: Discussionsupporting

confidence: 76%

Section: Discussionmentioning

confidence: 99%

Section: Multispecies Biofilm Model Formulationmentioning

confidence: 99%

See 1 more Smart Citation

Byproduct Cross Feeding and Community Stability in an In Silico Biofilm Model of the Gut Microbiome

Henson

Phalak

2017

Processes

View full text Add to dashboard Cite

show abstract

“…GENREs have previously been used to design chemically defined growth media using an iterative in silico, in vitro, and metabolomics approach 39 . The predicted growth of B. caccae in extended DMEM medium supplemented with arabinogalactan under anaerobic conditions was validated.…”

Section: Pairwise Interactions Of Modelsmentioning

confidence: 99%

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

et al. 2016

Self Cite

View full text Add to dashboard Cite

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:/...

show abstract

“…Construction of host-microbe models We retrieved 11 manually constructed and validated reconstructions of human gut microbes 13,[43][44][45][46][47][48][49][50] and an extensive, highquality reconstruction of human metabolism.…”

Section: Methodsmentioning

confidence: 99%

Systematic prediction of health-relevant human-microbial co-metabolism through a computational framework

2015

Self Cite

View full text Add to dashboard Cite

Keywords: human gut microbiome, in silico, metabolome, metabolic modelingThe gut microbiota is well known to affect host metabolic phenotypes. The systemic effects of the gut microbiota on host metabolism are generally evaluated via the comparison of germfree and conventional mice, which is impossible to perform for humans. Hence, it remains difficult to determine the impact of the gut microbiota on human metabolic phenotypes. We demonstrate that a constraint-based modeling framework that simulates "germfree" and "exgermfree" human individuals can partially fill this gap and allow for in silico predictions of systemic human-microbial cometabolism. To this end, we constructed the first constraint-based host-microbial community model, comprising the most comprehensive model of human metabolism and 11 manually curated, validated metabolic models of commensals, probiotics, pathogens, and opportunistic pathogens. We used this host-microbiota model to predict potential metabolic host-microbe interactions under 4 in silico dietary regimes. Our model predicts that gut microbes secrete numerous health-relevant metabolites into the lumen, thereby modulating the molecular composition of the body fluid metabolome. Our key results include the following: 1. Replacing a commensal community with pathogens caused a loss of important host metabolic functions. 2. The gut microbiota can produce important precursors of host hormone synthesis and thus serves as an endocrine organ. 3. The synthesis of important neurotransmitters is elevated in the presence of the gut microbiota. 4. Gut microbes contribute essential precursors for glutathione, taurine, and leukotrienes. This computational modeling framework provides novel insight into complex metabolic host-microbiota interactions and can serve as a powerful tool with which to generate novel, non-obvious hypotheses regarding host-microbe co-metabolism.

show abstract

Functional Metabolic Map of Faecalibacterium prausnitzii, a Beneficial Human Gut Microbe

Cited by 208 publications

References 61 publications

Byproduct Cross Feeding and Community Stability in an In Silico Biofilm Model of the Gut Microbiome

Byproduct Cross Feeding and Community Stability in an In Silico Biofilm Model of the Gut Microbiome

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

Systematic prediction of health-relevant human-microbial co-metabolism through a computational framework

Contact Info

Product

Resources

About