Biology subjects

Heinken, A.

Publications and source records attributed to Heinken, A..

15 recordsLinked to original sources

bioRxiv2025

Genome-scale modeling reveals regulation of human metabolism by the histone deacetylase SIRT1

Genome-scale metabolic models are powerful tools for predicting metabolic fluxes, yet regulatory mechanisms are typically outside their scope. Here, we present a genome-scale modeling framework that integrates transcriptional regulation by the histone deacetylase SIRT1 into human metabolism. By combining a curated regulatory network with the Recon3D metabolic reconstruction, we developed a continuous modeling framework that simulates graded regulatory influences on metabolic fluxes. The model captures known metabolic effects of SIRT1, including enhanced fatty acid oxidation and gluconeogenesis and suppressed glycolysis, across various tissues and dietary conditions. Through cell culture experiments, we quantified the dose-dependent inhibition of SIRT1 by butyrate, a microbiome-derived metabolite. After, incorporating this relationship into the model and found good agreement between experimental metabolomics measurements and in silico predictions. This is the first model to integrate a histone deacetylase and its inhibitor into a genome-scale metabolic framework, enabling simulation of host-microbiome regulatory crosstalk. Our approach provides a dynamic, systems-level tool to explore the regulation of human metabolism and offers insights into how diet and microbial activity influence host metabolic states.

systems biology↗

bioRxiv2025

The MicroMap is a network visualisation resource for microbiome metabolism

The human microbiome plays a crucial role in metabolism and thereby influences health and disease. Constraint-based reconstruction and analysis (COBRA) has proven an attractive framework to generate mechanism-derived hypotheses along the nutrition-host-microbiome-disease axis within the computational systems biology community. Unlike for human, no large-scale visualisation resource for microbiome metabolism has been available to date. To address this gap, we created the MicroMap, a manually curated microbiome metabolic network visualisation, which captures the metabolic content of over a quarter million microbial genome-scale metabolic reconstructions. The MicroMap contains 5,064 unique reactions and 3,499 unique metabolites, including for 98 drugs. The MicroMap allows users to intuitively explore microbiome metabolism, inspect microbial metabolic capabilities, and visualise computational modelling results. Further, the MicroMap shall serve as an educational tool to make microbiome metabolism accessible to broader audiences beyond computational modellers. For example, we utilised the MicroMap to generate a comprehensive collection of 257,429 visualisations, corresponding to the entire scope of our current microbiome reconstruction resources, to enable users to visually compare and contrast the metabolic capabilities for diaerent microbes. The MicroMap seamlessly integrates with the Virtual Metabolic Human (VMH, www.vmh.life) and the COBRA Toolbox (opencobra.github.io), and is freely accessible at the MicroMap dataverse (https://dataverse.harvard.edu/dataverse/micromap), in addition to all the generated reconstruction visualisations.

systems biology↗

bioRxiv2023

APOLLO: A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites

Computational modelling of microbiome metabolism has proved instrumental to catalyse our understanding of diet-host-microbiome-disease interactions through the interrogation of mechanistic, strain- and molecule-resolved metabolic models. We present APOLLO, a resource of 247,092 human microbial genome-scale metabolic reconstructions spanning 19 phyla and accounting for microbial genomes from 34 countries, all age groups, and five body sites. We explored the metabolic potential of the reconstructed strains and developed a machine learning classifier able to predict with high accuracy the taxonomic strain assignments. We also built 14,451 sample-specific microbial community models, which could be stratified by body site, age, and disease states. Finally, we predicted faecal metabolites enriched or depleted in gut microbiomes of people with Crohns disease, Parkinson disease, and undernourished children. APOLLO is compatible with the human whole-body models, and thus, provide unprecedented opportunities for systems-level modelling of personalised host-microbiome co-metabolism. APOLLO will be freely available under https://www.vmh.life/.

systems biology↗

bioRxiv2023

A Structured Evaluation of Genome-Scale Constraint-Based Modeling Tools for Microbial Consortia

Harnessing the power of microbial consortia is integral to a diverse range of sectors, from healthcare to biotechnology to environmental remediation. To fully realize this potential, it is critical to understand the mechanisms behind the interactions that structure microbial consortia and determine their functions. Constraint-based reconstruction and analysis (COBRA) approaches, employing genome-scale metabolic models (GEMs), have emerged as the state-of-the-art tool to simulate the behavior of microbial communities from their constituent genomes. In the last decade, many tools have been developed that use COBRA approaches to simulate multi-species consortia, under either steady-state, dynamic, or spatiotemporally varying scenarios. Yet, these tools have not been systematically evaluated regarding their software quality, most suitable application, and predictive power. Hence, it is uncertain which tools users should apply to their system and what are the most urgent directions that developers should take in the future to improve existing capacities. This study conducted a systematic evaluation of COBRA-based tools for microbial communities using datasets from two-member communities as test cases. First, we performed a qualitative assessment in which we evaluated 24 published tools based on a list of FAIR (Findability, Accessibility, Interoperability, and Reusability) features essential for software quality. Next, we quantitatively tested the predictions in a subset of 14 of these tools against experimental data from three different case studies: a) syngas fermentation by C. autoethanogenum and C. kluyveri for the static tools, b) glucose/xylose fermentation with engineered E. coli and S. cerevisiae for the dynamic tools, and c) a Petri dish of E. coli and S. enterica for tools incorporating spatiotemporal variation. Our results show varying performance levels of the best qualitatively assessed tools when examining the different categories of tools. The differences in the mathematical formulation of the approaches and their relation to the results were also discussed. Ultimately, we provide recommendations for refining future GEM microbial modeling tools. Author summaryConstraint-based modeling employing genome-scale reconstructions of microbial species has become one of the most successful approaches for studying, analyzing, and engineering microbial consortia. Over the past decade, many constraint-based modeling tools have been published to examine an immense variety of microbial consortia spanning from the application areas of bioremediation to food and health biotechnology. However, new potential users lack an overview of the quality and performance of existing metabolic modeling tools that would guide their choice. To tackle this issue, we examined 24 tools for genome-scale metabolic modeling of microbial consortia. After an initial qualitative screening, we quantitatively evaluated 14 adequate tools against published experimental data that included different organisms and conditions. We conducted simulations and evaluated model features such as predictive accuracy, computational time, and tractability in capturing critical physiological properties. We found that, generally, more up-to-date, accessible, and documented tools were superior in many important aspects of model quality and performance. Although, in some cases, we observed tradeoffs in older, less elaborate tools that can be more accurate or flexible. This work has broad implications to help researchers navigate the most suitable tools, and suggests to developers opportunities for improvement of the currently existing capabilities for metabolic modeling of multi-species microbial consortia.

systems biology↗

bioRxiv2022

Longitudinal flux balance analyses of a patient with Crohn's disease highlight microbiome metabolic alterations

Inflammatory bowel diseases (IBD) are characterised by episodic inflammation of the gastrointestinal tract. Gut microbial dysbiosis characterises the pathoetiology, but its role remains understudied. We report the first use of constraint-based microbial community modelling on a single individual with IBD, covering seven dates over 16 months, enabling us to identify a number of time-correlated microbial species and metabolites. We find that the individuals dynamical microbial ecology in the disease state drives time-varying in silico overproduction, compared to healthy controls, of more than 24 biologically important metabolites, including oxygen, methane, thiamine, formaldehyde, trimethylamine N-oxide, folic acid, serotonin, histamine, and tryptamine. A number of these metabolites may yield new biomarkers of disease progression. The microbe-metabolite contribution analysis revealed that some genus Dialister species changed metabolic pathways according to the disease phases. At the first time point, characterised by the highest levels of blood and faecal inflammation biomarkers, they produced L-serine or formate. The production of the compounds, through a cascade effect, was mediated by the interaction with pathogenic Escherichia coli strains and Desulfovibrio piger. We integrated the microbial community metabolic models of each time point with a male whole-body, organ-resolved model of human metabolism to track the metabolic consequences of dysbiosis at different body sites. The presence of D. piger in the gut microbiome influenced the sulphur metabolism with a domino effect affecting the liver. These results underline the importance of tracking an individuals gut microbiome along a time course, creating a new analysis framework for self-quantified medicine. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC="FIGDIR/small/520975v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@136ef4forg.highwire.dtl.DTLVardef@1993042org.highwire.dtl.DTLVardef@2f795forg.highwire.dtl.DTLVardef@c95a6d_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2022

Systems biology illuminates alternative metabolic niches in the human gut microbiome

Human gut bacteria perform diverse metabolic functions with consequences for host health. The prevalent and disease-linked Actinobacterium Eggerthella lenta performs several unusual chemical transformations, but it does not metabolize sugars and its core growth strategy remains unclear. To obtain a comprehensive view of the metabolic network of E. lenta, we generated several complementary resources: defined culture media, metabolomics profiles of strain isolates, and a curated genome-scale metabolic reconstruction. Stable isotope-resolved metabolomics revealed that E. lenta uses acetate as a key carbon source while catabolizing arginine to generate ATP, traits which could be recapitulated in silico by our updated metabolic model. We compared these in vitro findings with metabolite shifts observed in E. lenta-colonized gnotobiotic mice, identifying shared signatures across environments and highlighting catabolism of the host signaling metabolite agmatine as an alternative energy pathway. Together, our results elucidate a distinctive metabolic niche filled by E. lenta in the gut ecosystem.

microbiology↗

bioRxiv2021

Causal inference on microbiome-metabolome relations via in silico in vivo association pattern analyses

The effects of the microbiome on the hosts metabolism are core to understanding the role of the microbiome in health and disease. Herein, we develop the paradigm of in silico in vivo association pattern analyses, entailing a methodology to combine microbiome metabolome association studies with in silico constraint-based microbial community modelling. By dissecting confounding and causal paths, we show that in silico in vivo association pattern analyses allows for causal inference on microbiome-metabolome relations in observational data. Then, we demonstrate the feasibility and validity of our approach on a published multi-omics dataset (n=346), demonstrating causal microbiome-metabolite relations for 43 out of 53 metabolites from faeces. Finally, we utilise the identified in silico in vivo association pattern to estimate the microbial component of the faecal metabolome, revealing that the retrieved metabolite prediction scores correlate with the measured metabolite concentrations, and they also reflect the multivariate structure of the faecal metabolome. Concluding, we integrate with hypothesis free screening association studies and knowledge-based in silico modelling two major paradigms of systems biology, generating a promising new paradigm for causal inference in metabolic host-microbe interactions.

systems biology↗

bioRxiv2020

AGORA2: Large scale reconstruction of the microbiome highlights wide-spread drug-metabolising capacities.

The human microbiome influences the efficacy and safety of a wide variety of commonly prescribed drugs, yet comprehensive systems-level approaches to interrogate drug-microbiome interactions are lacking. Here, we present a computational resource of human microbial genome-scale reconstructions, deemed AGORA2, which accounts for 7,206 strains, includes microbial drug degradation and biotransformation, and was extensively curated based on comparative genomics and literature searches. AGORA2 serves as a knowledge base for the human microbiome and as a metabolic modelling resource. We demonstrate the latter by mechanistically modelling microbial drug metabolism capabilities in single strains and pairwise models. Moreover, we predict the individual-specific drug conversion potential in a cohort of 616 colorectal cancer patients and controls. This analysis reveals that some drug activation capabilities are present in only a subset of individuals, moreover, drug conversion potential correlate with clinical parameters. Thus, AGORA2 paves the way towards personalised, predictive analysis of host-drug-microbiome interactions.

systems biology↗

bioRxiv2020

Integration of constraint-based modeling with fecal metabolomics reveals large deleterious effects of Fusobacteria species on community butyrate production

Integrating constraint-based community modelling with population statistics, we introduce new theoretical concepts for interrogating the metabolic functions of the microbiome, applying them to a public metagenomic dataset consisting of 365 colorectal cancer cases (CRC) and 251 healthy controls. We found that 1) glutarate production capability was significantly enriched in CRC microbiomes and mechanistically linked to lysine fermentation in Fusobacteria species, 2) acetate and butyrate production potentials were lowered in CRC, 3) Fusobacteria presence had large negative ecological effects on community butyrate production in CRC and healthy controls. Validating the model predictions against faecal metabolomics, our in silico frameworks correctly predicted in vivo species metabolite correlations with high accuracy. In conclusion, highlighting the value of combining statistical association studies with in silico modelling, this study delivers insights on the metabolic role of Fusobacteria in the gut, while providing a proof of concept for the validity of constraint-based community modelling.

systems biology↗

bioRxiv2019

Identifying differences in bile acid pathways for cholesterol clearance in Alzheimer’s disease using metabolic networks of human brain regions

Alzheimers disease (AD) is the leading cause of dementia, with metabolic dysfunction seen years before the emergence of clinical symptoms. Increasing evidence suggests a role for primary and secondary bile acids, the end-product of cholesterol metabolism, influencing pathophysiology in AD. In this study, we analyzed transcriptomes from 2114 post-mortem brain samples from three independent cohorts and identified that the genes involved in alternative bile acid synthesis pathway was expressed in brain compared to the classical pathway. These results were supported by targeted metabolomic analysis of primary and secondary bile acids measured from post-mortem brain samples of 111 individuals. We reconstructed brain region-specific metabolic networks using data from three independent cohorts to assess the role of bile acid metabolism in AD pathophysiology. Our metabolic network analysis suggested that taurine transport, bile acid synthesis and cholesterol metabolism differed in AD and cognitively normal individuals. Using the brain transcriptional regulatory network, we identified putative transcription factors regulating these metabolic genes and influencing altered metabolism in AD. Intriguingly, we find bile acids from the brain metabolomics whose synthesis cannot be explained by enzymes we find in the brain, suggesting they may originate from an external source such as the gut microbiome. These findings motivate further research into bile acid metabolism and transport in AD to elucidate their possible connection to cognitive decline.

systems biology↗

bioRxiv2019

Systematic interrogation of the distinct metabolic potential in gut microbiomes of inflammatory bowel disease patients with dysbiosis

The human gut microbiome plays an important role in human health. In order to investigate changes in metabolic activity associated with dysbiotic microbiomes, we retrieved strain-level relative abundances from metagenomics data from a cohort of pediatric Crohns Disease patients with and without dysbiosis, and healthy control children to construct a personalized microbiome model for each sample using the AGORA resource of genome-scale gut microbial reconstructions. Subsequently, we systematically profiled each individual microbiome by predicting the quantitative biosynthesis potential for all secreted metabolites as well as the strain-level contributions to each metabolite in each individual microbiome. The predicted fecal metabolomes and strain-metabolite contributions of microbiomes from patients with dysbiosis were distinct from healthy controls and patients without dysbiosis. Finally, we validated the predicted amino acid production profiles against fecal metabolomic measurements. Taken together, we presented an efficient, scalable, tractable computational approach to systematically interrogate the metabolic potential of individual microbiomes.

systems biology↗

bioRxiv2018

The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease

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, http://vmh.life) database encapsulating current knowledge of human metabolism within five interlinked resources \"Human metabolism\", \"Gut microbiome\", \"Disease\", \"Nutrition\", and \"ReconMaps\". The VMH captures 5,180 unique metabolites, 17,730 unique reactions, 3,288 human genes, 255 Mendelian diseases, 818 microbes, 632,685 microbial genes, and 8,790 food items. The VMHs unique features are i) the hosting 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; and v) user feedback option for community engagement. We demonstrate with four examples the VMHs utility. The VMH represents a novel, interdisciplinary database for data interpretation and hypothesis generation to the biomedical community.

systems biology↗

bioRxiv2018

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

MotivationThe application of constraint-based modeling to functionally analyze metagenomic data has been limited so far, partially due to the absence of suitable toolboxes.\n\nResultsTo address this shortage, 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.\n\nAvailabilityThe Microbiome Modeling Toolbox and the tutorials at https://git.io/microbiomeModelingToolbox.

systems biology↗

bioRxiv2018

When metabolism meets physiology: Harvey and Harvetta

Precision medicine is an emerging paradigm that requires realistic, mechanistic models capturing the complexity of the human body. We present two comprehensive molecular to physiological-level, gender-specific whole-body metabolism (WBM) reconstructions, named Harvey, in recognition of William Harvey, and Harvetta. These validated, knowledge-based WBM reconstructions capture the metabolism of 20 organs, six sex organs, six blood cells, the gastrointestinal lumen, systemic blood circulation, and the blood-brain barrier. They represent 99% of the human body weight, when excluding the weight of the skeleton. Harvey and Harvetta can be parameterized based on physiological, dietary, and omics data. They correctly predict inter-organ metabolic cycles, basal metabolic rates, and energy use. We demonstrate the integration of microbiome data thereby allowing the assessment of individual-specific, organ-level modulation of host metabolism by the gut microbiota. The WBM reconstructions and the individual organ reconstructions are available under http://vmh.life. Harvey and Harvetta represent a pivotal step towards virtual physiological humans.

systems biology↗

bioRxiv2017

Personalized modeling of the human gut microbiome reveals distinct bile acid deconjugation and biotransformation potential in healthy and IBD individuals

The human gut microbiome performs important functions human health and disease. Intestinal microbes are capable of deconjugation and biotransformation of human primary bile acids to secondary bile acids. Alterations of the bile acid pool as a result of microbial dysbiosis have been linked to multifactorial diseases, such as inflammatory bowel disease (IBD).\n\nConstraint-based modeling is a powerful approach for the mechanistic, systems-level analysis of metabolic interactions in microbial communities. Recently, we constructed a resource of 773 curated genome-scale reconstructions of human gut microbes, AGORA. Here, we performed a comparative genomic analysis of bile acid deconjugation and biotransformation pathways in 693 human gut microbial genomes to expand these AGORA reconstructions accordingly (available at http://vmh.life).\n\nTo elucidate the metabolic potential of individual microbiomes, publicly available metagenomic data from a cohort of healthy Western individuals, as well as two cohorts of IBD patients and healthy controls, were mapped onto the reference set of AGORA genomes. We constructed for each individual a large-scale personalized microbial community model that take strain-level abundances into account. Using flux balance analysis, we found that distinct potential to deconjugate and tranform primary bile acids between the gut microbiomes of healthy individuals. Moreover, the microbiomes of pediatric IBD patients were significantly depleted in their bile acid production potential compared with controls. The contributions of each strain to overall bile acid production potential across individuals were found to be distinct between IBD patients and controls. IBD microbiomes were depleted in contributions of Bacteroidetes strains but enriched in contributions of Proteobacteria. Finally, bottlenecks limiting secondary bile acid production potential were identified in each microbiome model. For ursodeoxycholate, the abundance of strains producing the precursor rather than of strains directly producing this secondary bile acid was synthesis-limiting in certain microbiomes.\n\nIn summary, we integrated for the first-time metagenomics data with large-scale personalized metabolic modeling of microbial communities. We provided mechanistic insight into the link between dysbiosis and metabolic potential in IBD microbiomes. This large-scale modeling approach provides a novel way of analyzing metagenomics data to accelerate our understanding of the metabolic interactions between human host and gut microbiomes in health and diseases states.

systems biology↗