References of "Heinken, Almut Katrin 50001958"
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See detailSystematic assessment of secondary bile acid metabolism in gut microbes reveals distinct metabolic capabilities in inflammatory bowel disease
Heinken, Almut Katrin UL; Ravcheev, Dmitry UL; Baldini, Federico UL et al

in Microbiome (2019)

Background The human gut microbiome performs important functions in human health and disease. A classic example for host-gut microbial co-metabolism is host biosynthesis of primary bile acids and their ... [more ▼]

Background The human gut microbiome performs important functions in human health and disease. A classic example for host-gut microbial co-metabolism is host biosynthesis of primary bile acids and their subsequent deconjugation and transformation by the gut microbiome. To understand these system-level host-microbe interactions, a mechanistic, multi-scale computational systems biology approach that integrates the different types of omic data is needed. Here, we use a systematic workflow to computationally model bile acid metabolism in gut microbes and microbial communities. Results Therefore, we first performed a comparative genomic analysis of bile acid deconjugation and biotransformation pathways in 693 human gut microbial genomes and expanded 232 curated genome-scale microbial metabolic reconstructions with the corresponding reactions (available at https://vmh.life). We then predicted the bile acid biotransformation potential of each microbe and in combination with other microbes. We found that each microbe could produce maximally six of the 13 secondary bile acids in silico, while microbial pairs could produce up to 12 bile acids, suggesting bile acid biotransformation being a microbial community task. To investigate the metabolic potential of a given microbiome, publicly available metagenomics data from healthy Western individuals, as well as inflammatory bowel disease patients and healthy controls, were mapped onto the genomes of the reconstructed strains. We constructed for each individual a large-scale personalized microbial community model that takes into account strain-level abundances. Using flux balance analysis, we found considerable variation in the potential to deconjugate and transform primary bile acids between the gut microbiomes of healthy individuals. Moreover, the microbiomes of pediatric inflammatory bowel disease patients were significantly depleted in their bile acid production potential compared with that of controls. The contributions of each strain to overall bile acid production potential across individuals were found to be distinct between inflammatory bowel disease patients and controls. Finally, bottlenecks limiting secondary bile acid production potential were identified in each microbiome model. Conclusions This large-scale modeling approach provides a novel way of analyzing metagenomics data to accelerate our understanding of the metabolic interactions between the host and gut microbiomes in health and diseases states. Our models and tools are freely available to the scientific community. [less ▲]

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See detailCreation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0.
Heirendt, Laurent UL; Arreckx, Sylvain; Pfau, Thomas UL et al

in Nature protocols (2019), 14(3), 639-702

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of ... [more ▼]

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods. [less ▲]

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See detailThe Microbiome Modeling Toolbox: from microbial interactions to personalized microbial communities
Baldini, Federico UL; Heinken, Almut Katrin UL; Heirendt, Laurent UL et al

in Bioinformatics (2018)

The application of constraint-based modeling to functionally analyze metagenomic data has been limited so far, partially due to the absence of suitable toolboxes. To address this gap, we created a ... [more ▼]

The application of constraint-based modeling to functionally analyze metagenomic data has been limited so far, partially due to the absence of suitable toolboxes. To address this gap, we created a comprehensive toolbox to model i) microbe-microbe and host-microbe metabolic interactions, and ii) microbial communities using microbial genome-scale metabolic reconstructions and metagenomic data. The Microbiome Modeling Toolbox extends the functionality of the COBRA Toolbox. The Microbiome Modeling Toolbox and the tutorials at https://git.io/microbiomeModelingToolbox. [less ▲]

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See detailThe Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease
Noronha, Alberto UL; Modamio Chamarro, Jennifer UL; Jarosz, Yohan UL et al

in Nucleic Acids Research (2018)

A multitude of factors contribute to complex diseases and can be measured with ‘omics’ methods. Databases facilitate data interpretation for underlying mechanisms. Here, we describe the Virtual Metabolic ... [more ▼]

A multitude of factors contribute to complex diseases and can be measured with ‘omics’ methods. Databases facilitate data interpretation for underlying mechanisms. Here, we describe the Virtual Metabolic Human (VMH, www.vmh.life) database encapsulating current knowledge of human metabolism within five interlinked resources ‘Human metabolism’, ‘Gut microbiome’, ‘Disease’, ‘Nutrition’, and ‘ReconMaps’. The VMH captures 5180 unique metabolites, 17 730 unique reactions, 3695 human genes, 255 Mendelian diseases, 818 microbes, 632 685 microbial genes and 8790 food items. The VMH’s unique features are (i) the hosting of the metabolic reconstructions of human and gut microbes amenable for metabolic modeling; (ii) seven human metabolic maps for data visualization; (iii) a nutrition designer; (iv) a user-friendly webpage and application-programming interface to access its content; (v) user feedback option for community engagement and (vi) the connection of its entities to 57 other web resources. The VMH represents a novel, interdisciplinary database for data interpretation and hypothesis generation to the biomedical community. [less ▲]

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See detailRecon3D enables a three-dimensional view of gene variation in human metabolism.
Brunk, Elizabeth; Sahoo, Swagatika; Zielinski, Daniel C. et al

in Nature biotechnology (2018), 36(3), 272-281

Genome-scale network reconstructions have helped uncover the molecular basis of metabolism. Here we present Recon3D, a computational resource that includes three-dimensional (3D) metabolite and protein ... [more ▼]

Genome-scale network reconstructions have helped uncover the molecular basis of metabolism. Here we present Recon3D, a computational resource that includes three-dimensional (3D) metabolite and protein structure data and enables integrated analyses of metabolic functions in humans. We use Recon3D to functionally characterize mutations associated with disease, and identify metabolic response signatures that are caused by exposure to certain drugs. Recon3D represents the most comprehensive human metabolic network model to date, accounting for 3,288 open reading frames (representing 17% of functionally annotated human genes), 13,543 metabolic reactions involving 4,140 unique metabolites, and 12,890 protein structures. These data provide a unique resource for investigating molecular mechanisms of human metabolism. Recon3D is available at http://vmh.life. [less ▲]

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See detailQuantitative systems pharmacology and the personalized drug–microbiota–diet axis
Thiele, Ines UL; Clancy, Catherine UL; Heinken, Almut Katrin UL et al

in Current Opinion in Systems Biology (2017), 4

Precision medicine is an emerging paradigm that aims at maximizing the benefits and minimizing the adverse effects of drugs. Realistic mechanistic models are needed to understand and limit heterogeneity ... [more ▼]

Precision medicine is an emerging paradigm that aims at maximizing the benefits and minimizing the adverse effects of drugs. Realistic mechanistic models are needed to understand and limit heterogeneity in drug responses. While pharmacokinetic models describe in detail a drug's absorption and metabolism, they generally do not account for individual variations in response to environmental influences, in addition to genetic variation. For instance, the human gut microbiota metabolizes drugs and is modulated by diet, and it exhibits significant variation among individuals. However, the influence of the gut microbiota on drug failure or drug side effects is under-researched. Here, we review recent advances in computational modeling approaches that could contribute to a better, mechanism-based understanding of drug–microbiota–diet interactions and their contribution to individual drug responses. By integrating systems biology and quantitative systems pharmacology with microbiology and nutrition, the conceptually and technologically demand for novel approaches could be met to enable the study of individual variability, thereby providing breakthrough support for progress in precision medicine. [less ▲]

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See detailGut microbiota functions: metabolism of nutrients and other food components
Rowland, Ian; Gibson, Glenn; Heinken, Almut Katrin UL et al

in European Journal of Nutrition (2017)

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See detailGeneration of genome-scale metabolic reconstructions for 773 members of the human gut microbiota
Magnusdottir, Stefania UL; Heinken, Almut Katrin UL; Kutt, Laura et al

in Nature Biotechnology (2016)

Genome-scale metabolic models derived from human gut metagenomic data can be used as a framework to elucidate how microbial communities modulate human metabolism and health. We present AGORA (assembly of ... [more ▼]

Genome-scale metabolic models derived from human gut metagenomic data can be used as a framework to elucidate how microbial communities modulate human metabolism and health. We present AGORA (assembly of gut organisms through reconstruction and analysis), a resource of genome-scale metabolic reconstructions semi-automatically generated for 773 human gut bacteria. Using this resource, we identified a defined growth medium for Bacteroides caccae ATCC 34185. We also showed that interactions among modeled species depend on both the metabolic potential of each species and the nutrients available. AGORA reconstructions can integrate either metagenomic or 16S rRNA sequencing data sets to infer the metabolic diversity of microbial communities. AGORA reconstructions could provide a starting point for the generation of high-quality, manually curated metabolic reconstructions. AGORA is fully compatible with Recon 2, a comprehensive metabolic reconstruction of human metabolism, which will facilitate studies of host–microbiome interactions. [less ▲]

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See detailSystems biology of bacteria-host interactions
Heinken, Almut Katrin UL; Ravcheev, Dmitry UL; Thiele, Ines UL

in Nibali, Luigi; Henderson, Brian (Eds.) The Human Microbiota and Chronic Disease: Dysbiosis as a Cause of Human Pathology (2016)

The aim of systems biology is to use computational methods to gain a complete, systems-level understanding of a cell, organism, or ecosystem. This chapter describes computational systems biology ... [more ▼]

The aim of systems biology is to use computational methods to gain a complete, systems-level understanding of a cell, organism, or ecosystem. This chapter describes computational systems biology approaches and their applications to human gut microbiome research, with particular focus on constraint-based modeling. At heart of the Constraint-Based Modeling and Analysis (COBRA) approach are accurate, well-structured metabolic reconstructions based on the target organisms’ genome sequences. Such genome-scale reconstructions (GENREs) are constructed in a bottom-up manner and describe the target organism's metabolism. The availability of high-quality reconstructions of human metabolism and of other host organisms, enables the computational modeling of host-microbe interactions. Simulating host-microbe interactions is particularly valuable since it could be used to minimize the number of animal experiments. The discussed computational modeling approaches will be valuable tools for studying microbial dysbiosis and its impact on host metabolism. Common approaches for computational modeling include ordinary differential equation (ODE) and kinetic modeling [less ▲]

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See detailAnoxic conditions promote species-specific mutualism between gut microbes in silico
Heinken, Almut Katrin UL; Thiele, Ines UL

in Applied and Environmental Microbiology (2015), 81(12), 4049-4061

The human gut is inhabited by thousands of microbial species, most of which are still uncharacterized. Gut microbes have adapted to each other’s presence as well as to the host and engage in complex cross ... [more ▼]

The human gut is inhabited by thousands of microbial species, most of which are still uncharacterized. Gut microbes have adapted to each other’s presence as well as to the host and engage in complex cross-feeding. Constraint-based modeling has been successfully applied to predicting microbe-microbe interactions, such as commensalism, mutualism, and competition. Here, we apply a constraint-based approach to model pairwise interactions between 11 representative gut microbes. Microbe-microbe interactions were computationally modeled in conjunction with human small intestinal enterocytes and subjected to three diets with varying levels of carbohydrate, fat, and protein in normoxic or anoxic environments. Each microbe engaged in species-specific commensal, parasitic, mutualistic, or competitive interactions. For instance, Streptococcus thermophilus efficiently outcompeted paired microbes in agreement with the domination of streptococci in the small intestinal microbiota. Under anoxic conditions, the probiotic Lactobacillus plantarum displayed mutualistic behavior towards six other species, which, surprisingly, were almost entirely abolished under normoxic conditions. This finding suggests that the anoxic conditions in the large intestine drive mutualistic cross-feeding, leading to the evolvement of a more complex ecosystem than the small intestinal microbiota. Moreover, we predict that the presence of the small intestinal enterocyte induces competition over host-derived nutrients. The presented framework can readily be expanded to a larger gut microbial community. This modeling approach will be of great value for subsequent studies aiming to predict conditions favoring desirable microbes or suppressing pathogens. [less ▲]

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See detailSystematic prediction of health-relevant human-microbial co-metabolism through a computational framework
Heinken, Almut Katrin UL; Thiele, Ines UL

in Gut Microbes (2015), 6(2), 120-130

The 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 ... [more ▼]

The 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. [less ▲]

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See detailSystems biology of host-microbe metabolomics
Heinken, Almut Katrin UL; Thiele, Ines UL

in Wiley Interdisciplinary Reviews. Systems Biology and Medicine (2015)

The human gut microbiota performs essential functions for host and wellbeing, but has also been linked to a variety of disease states, e.g., obesity and type 2 diabetes. The mammalian body fluid and ... [more ▼]

The human gut microbiota performs essential functions for host and wellbeing, but has also been linked to a variety of disease states, e.g., obesity and type 2 diabetes. The mammalian body fluid and tissue metabolomes are greatly influenced by the microbiota, with many health-relevant metabolites being considered “mammalian-microbial co-metabolites”. To systematically investigate this complex host-microbial co-metabolism, a systems biology approach integrating high-throughput data and computational network models is required. Here, we review established top-down and bottom-up systems biology approaches that have successfully elucidated relationships between gut microbiota-derived metabolites and host health and disease. We particularly focus on the constraint-based modeling and analysis approach, which enables the prediction of mechanisms behind metabolic host-microbe interactions on the molecular level. We illustrate that constraint-based models are a useful tool for the contextualization of metabolomic measurements and can further our insight into host-microbe interactions, yielding, e.g., in potential novel drugs and biomarkers. [less ▲]

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See detailA functional metabolic map of Faecalibacterium prausnitzii, a beneficial human gut microbe
Heinken, Almut Katrin UL; Khan, M Tanweer; Paglia, Giuseppe et al

in Journal of Bacteriology (2014), 196(18), 3289-3302

The human gut microbiota plays a central role in human well-being and disease. In this study, we present an integrated, iterative approach of computational modeling, in vitro experiments, metabolomics ... [more ▼]

The human gut microbiota plays a central role in human well-being and disease. In this study, we present an integrated, iterative approach of computational modeling, in vitro experiments, metabolomics, and genomic analysis to accelerate the identification of metabolic capabilities for poorly characterized (anaerobic) microorganisms. We demonstrate this approach for the beneficial human gut microbe Faecalibacterium prausnitzii strain A2-165. We generated an automated draft reconstruction, which we curated against the limited biochemical data. This reconstruction modeling was used to develop in silico and in vitro a chemically defined medium (CDM), which was validated experimentally. Subsequent metabolomic analysis of the spent medium for growth on CDM was performed. We refined our metabolic reconstruction according to in vitro observed metabolite consumption and secretion and propose improvements to the current genome annotation of F. prausnitzii A2-165. We then used the reconstruction to systematically characterize its metabolic properties. Novel carbon source utilization capabilities and inabilities were predicted based on metabolic modeling and validated experimentally. This study resulted in a functional metabolic map of F. prausnitzii, which is available for further applications. The presented workflow can be readily extended to other poorly characterized and uncharacterized organisms to yield novel biochemical insights about the target organism. [less ▲]

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See detailSystems-level characterization of a host-microbe metabolic symbiosis in the mammalian gut.
Heinken, Almut Katrin UL; Sahoo, Swagatika UL; Fleming, Ronan MT UL et al

in Gut microbes (2013), 4(1), 28-40

The human gut microbiota consists of ten times more microorganisms than there are cells in our body, processes otherwise indigestible nutrients, and produces important energy precursors, essential amino ... [more ▼]

The human gut microbiota consists of ten times more microorganisms than there are cells in our body, processes otherwise indigestible nutrients, and produces important energy precursors, essential amino acids, and vitamins. In this study, we assembled and validated a genome-scale metabolic reconstruction of Bacteroides thetaiotaomicron (iAH991), a prominent representative of the human gut microbiota, consisting of 1488 reactions, 1152 metabolites, and 991 genes. To create a comprehensive metabolic model of host-microbe interactions, we integrated iAH991 with a previously published mouse metabolic reconstruction, which was extended for intestinal transport and absorption reactions. The two metabolic models were linked through a joint compartment, the lumen, allowing metabolite exchange and providing a route for simulating different dietary regimes. The resulting model consists of 7239 reactions, 5164 metabolites, and 2769 genes. We simultaneously modeled growth of mouse and B. thetaiotaomicron on five different diets varying in fat, carbohydrate, and protein content. The integrated model captured mutually beneficial cross-feeding as well as competitive interactions. Furthermore, we identified metabolites that were exchanged between the two organisms, which were compared with published metabolomics data. This analysis resulted for the first time in a comprehensive description of the co-metabolism between a host and its commensal microbe. We also demonstrate in silico that the presence of B. thetaiotaomicron could rescue the growth phenotype of the host with an otherwise lethal enzymopathy and vice versa. This systems approach represents a powerful tool for modeling metabolic interactions between a gut microbe and its host in health and disease. [less ▲]

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See detailA systems biology approach to studying the role of microbes in human health.
Thiele, Ines UL; Heinken, Almut Katrin UL; Fleming, Ronan MT UL

in Current Opinion in Biotechnology (2013), 24(1), 4-12

Host-microbe interactions play a crucial role in human health and disease. Of the various systems biology approaches, reconstruction of genome-scale metabolic networks combined with constraint-based ... [more ▼]

Host-microbe interactions play a crucial role in human health and disease. Of the various systems biology approaches, reconstruction of genome-scale metabolic networks combined with constraint-based modeling has been particularly successful at in silico predicting the phenotypic characteristics of single organisms. Here, we summarize recent studies, which have applied this approach to investigate microbe-microbe and host-microbe metabolic interactions. This approach can be also expanded to investigate the properties of an entire microbial community, as well as single organisms within the community. We illustrate that the constraint-based modeling approach is suitable to model host-microbe interactions at molecular resolution and will enable systematic investigation of metabolic links between the human host and its microbes. Such host-microbe models, combined with experimental data, will ultimately further our understanding of how microbes influence human health. [less ▲]

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See detailA systems biology approach to drug targets in Pseudomonas aeruginosa biofilm.
Sigurdsson, Gunnar; Fleming, Ronan MT UL; Heinken, Almut Katrin UL et al

in PLoS ONE (2012), 7(4), 34337

Antibiotic resistance is an increasing problem in the health care system and we are in a constant race with evolving bacteria. Biofilm-associated growth is thought to play a key role in bacterial ... [more ▼]

Antibiotic resistance is an increasing problem in the health care system and we are in a constant race with evolving bacteria. Biofilm-associated growth is thought to play a key role in bacterial adaptability and antibiotic resistance. We employed a systems biology approach to identify candidate drug targets for biofilm-associated bacteria by imitating specific microenvironments found in microbial communities associated with biofilm formation. A previously reconstructed metabolic model of Pseudomonas aeruginosa (PA) was used to study the effect of gene deletion on bacterial growth in planktonic and biofilm-like environmental conditions. A set of 26 genes essential in both conditions was identified. Moreover, these genes have no homology with any human gene. While none of these genes were essential in only one of the conditions, we found condition-dependent genes, which could be used to slow growth specifically in biofilm-associated PA. Furthermore, we performed a double gene deletion study and obtained 17 combinations consisting of 21 different genes, which were conditionally essential. While most of the difference in double essential gene sets could be explained by different medium composition found in biofilm-like and planktonic conditions, we observed a clear effect of changes in oxygen availability on the growth performance. Eight gene pairs were found to be synthetic lethal in oxygen-limited conditions. These gene sets may serve as novel metabolic drug targets to combat particularly biofilm-associated PA. Taken together, this study demonstrates that metabolic modeling of human pathogens can be used to identify oxygen-sensitive drug targets and thus, that this systems biology approach represents a powerful tool to identify novel candidate antibiotic targets. [less ▲]

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