The Microbiome Modeling Toolbox: from microbial interactions to personalized microbial communities

Baldini, Federico; Heinken, Almut; Heirendt, Laurent; Magnúsdóttir, Stefanía; Fleming, Ronan M. T.; Thiele, Ines

doi:10.1093/bioinformatics/bty941

Cited by 119 publications

(179 citation statements)

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“…Briefly, we mapped each of the 309 microbiome samples on the generic microbial community model consisting of 819 gut microbial reconstructions (Magnusdottir et al 2017; Heinken et al 2019) (cf. Supplementary Material) to derived personalised microbiome models (Baldini et al 2018). We then computed a net maximal production capability (NMPC) for 129 different metabolites that could be secreted by each microbial community model (cf.…”

Section: Resultsmentioning

confidence: 99%

“…AGORA consists of a set of 819 strains of microbes commonly found in the human gut (Magnusdottir et al 2017; Noronha et al 2019). To match species taxonomic resolution, we combined strain models of the same species in one species model (‘panSpeciesModel.m’) using the function ‘createPanModels.m’ of the microbiome modelling toolbox (Baldini et al 2018). Briefly, reactions of multiple strains are combined into one pan-reconstruction.…”

Section: Methodsmentioning

confidence: 99%

“…A total of 259 species overlapped between our set of species models and SPINGO species assignment when considering species identified at least in 10 % of samples (Supplementary Material). The retrieved microbial abundance information for each sample was integrated into a community modelling setup obtaining personalised microbiome models using the automated module of the microbiome modelling toolbox (Baldini et al 2018) called mgPipe within the COBRA toolbox (Heirendt et al 2019) (commit: b097185b641fc783fa6fea4900bdd303643a6a7e). Briefly, the metabolic models of the community members are connected by a common compartment, where each model can secrete/uptake metabolites.…”

Section: Methodsmentioning

confidence: 99%

“…A complementary approach is computational modelling, or constraint-based reconstruction and analyses (COBRA) (Orth et al 2010), of microbiome-level metabolism. In this approach, metabolic reconstructions for hundreds of gut microbes (Magnusdottir et al 2017) are combined based on microbiome data (Baldini et al 2018; Heirendt et al 2019)). Flux balance analysis (FBA) (Orth et al 2010) is then used to compute, e.g., possible metabolite uptake or secretion flux rates of each microbiome model (microbiome metabolic profile) (Heinken et al 2019) or to study of microbial metabolic interactions (cross-feedings) (Klitgord and Segre 2010; Heinken and Thiele 2015).…”

Section: Introductionmentioning

confidence: 99%

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Parkinson’s disease-associated alterations of the gut microbiome can invoke disease-relevant metabolic changes

Baldini

Hertel

Sandt

et al. 2019

Preprint

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25 26 Parkinson's disease (PD) is a systemic disease clinically defined by the degeneration of 27 dopaminergic neurons in the brain. While alterations in the gut microbiome composition have 28 been reported in PD, their functional consequences remain unclear. Herein, we first analysed 29 the gut microbiome of patients and healthy controls by 16S rRNA gene sequencing of stool 30 samples from the Luxembourg Parkinson's study (n=147 typical PD cases, n=162 controls). 31All individuals underwent detailed clinical assessment, including neurological examinations 32 and neuropsychological tests followed by self-reporting questionnaires. Second, we predicted 33 the potential secretion for 129 microbial metabolites through personalised metabolic modelling 34 using the microbiome data and genome-scale metabolic reconstructions of human gut 35 microbes. Our key results include: 1. eight genera and nine species changed significantly in 36 their relative abundances between PD patients and healthy controls. 2. PD-associated microbial 37 patterns statistically depended on sex, age, BMI, and constipation. The relative abundances of 38Bilophila and Paraprevotella were significantly associated with the Hoehn and Yahr staging 39 after controlling for the disease duration. In contrast, dopaminergic medication had no 40 detectable effect on the PD microbiome composition. 3. Personalised metabolic modelling of 41 the gut microbiomes revealed PD-associated metabolic patterns in secretion potential of nine 42 microbial metabolites in PD, including increased methionine and cysteinylglycine. The 43 microbial pantothenic acid production potential was linked to the presence of specific non-44 motor symptoms and attributed to individual bacteria, such as Akkermansia muciniphila and 45 Bilophila wardswarthia. Our results suggest that PD-associated alterations of gut microbiome 46 could translate into functional differences affecting host metabolism and disease phenotype. 47

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Parkinson’s disease-associated alterations of the gut microbiome can invoke disease-relevant metabolic changes

Baldini

Hertel

Sandt

et al. 2019

Preprint

Self Cite

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show abstract

“…Genome scale metabolic modeling is a modeling approach that formulates a model based upon experimentally-established information such as gene content (genomic data) and biochemistry, and a number of publications and reviews exist describing this method in detail [25]. By starting with gene content and connecting associated enzymes with biochemical function, a stoichiometric matrix is generated that allows simulations to be run that can be used to study a variety of biological systems including human-microbe and microbe-microbe interactions [26,27]. A number of software packages have been developed to support development and analysis of genome scale metabolic models including the constraint based reconstruction and analysis (COBRA) Toolbox [28].…”

Section: Introductionmentioning

confidence: 99%

Computational Modeling of the Human Microbiome

Chowdhury

Fong

2020

Microorganisms

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The impact of microorganisms on human health has long been acknowledged and studied, but recent advances in research methodologies have enabled a new systems-level perspective on the collections of microorganisms associated with humans, the human microbiome. Large-scale collaborative efforts such as the NIH Human Microbiome Project have sought to kick-start research on the human microbiome by providing foundational information on microbial composition based upon specific sites across the human body. Here, we focus on the four main anatomical sites of the human microbiome: gut, oral, skin, and vaginal, and provide information on site-specific background, experimental data, and computational modeling. Each of the site-specific microbiomes has unique organisms and phenomena associated with them; there are also high-level commonalities. By providing an overview of different human microbiome sites, we hope to provide a perspective where detailed, site-specific research is needed to understand causal phenomena that impact human health, but there is equally a need for more generalized methodology improvements that would benefit all human microbiome research.

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Interrogation of the perturbed gut microbiota in gouty arthritis patients through in silico metabolic modeling

Henson

2021

Engineering in Life Sciences

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Recent studies have shown perturbed gut microbiota associated with gouty arthritis, a metabolic disease characterized by an imbalance between uric acid production and excretion. To mechanistically investigate altered microbiota metabolism associated with gout disease, 16S rRNA gene amplicon sequence data from stool samples of gout patients and healthy controls were computationally analyzed through bacterial community metabolic models. Patient‐specific community models constructed with the metagenomics modeling pipeline, mgPipe, were used to perform k‐means clustering of samples according to their metabolic capabilities. The clustering analysis generated statistically significant partitioning of samples into a Bacteroides‐dominated, high gout cluster and a Faecalibacterium‐elevated, low gout cluster. The high gout cluster was predicted to allow elevated synthesis of the amino acids D‐alanine and L‐alanine and byproducts of branched‐chain amino acid catabolism, while the low gout cluster allowed higher production of butyrate, the sulfur‐containing amino acids L‐cysteine and L‐methionine, and the L‐cysteine catabolic product H2S. By expanding the capabilities of mgPipe to provide taxa‐level resolution of metabolite exchange rates, acetate, D‐lactate and succinate exchanged from Bacteroides to Faecalibacterium were predicted to enhance butyrate production in the low gout cluster. Model predictions suggested that sulfur‐containing amino acid metabolism generally and H2S more specifically could be novel gout disease markers.

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The Microbiome Modeling Toolbox: from microbial interactions to personalized microbial communities

Cited by 119 publications

References 10 publications

Parkinson’s disease-associated alterations of the gut microbiome can invoke disease-relevant metabolic changes

Parkinson’s disease-associated alterations of the gut microbiome can invoke disease-relevant metabolic changes

Computational Modeling of the Human Microbiome

Interrogation of the perturbed gut microbiota in gouty arthritis patients through in silico metabolic modeling

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