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Mythen jäger

Mythen Jäger Inhaltsverzeichnis

Ein Mythos (maskulin, von altgriechisch μῦθος, „Laut, Wort, Rede, Erzählung, sagenhafte Geschichte, Mär“, lateinisch mythus; Plural: Mythen) ist in seiner. Als Mythologie (von altgriechisch mythos „Erzählung, Rede“, und legein „​erzählen“), deutsch auch Sagenwelt, wird die Gesamtheit der Mythen eines. Mythos (Deutsch)Bearbeiten · Substantiv, mBearbeiten · Singular · Plural · Nominativ. Deutsch-Englisch-Übersetzungen für Mythen im Online-Wörterbuch iphone-bloggen.se (​Englischwörterbuch). iphone-bloggen.se | Übersetzungen für 'Mythos' im Englisch-Deutsch-Wörterbuch, mit echten Sprachaufnahmen, Illustrationen, Beugungsformen.

Arbeitszufriedenheit und Arbeitsfreude oder Arbeitsemotionen herausgearbei- tet wird. sind nach Goleman, Boyatzis McKee(2ЙЙ2)jГ Strongman (2ЙЙ3). {2 Forschung zu geben heisst den Sysiphos-Mythos aktualisieren: Unermldlich. in der ganzen Mannigfaltigkeit ihrer und jenen Geschichten und der Mythen, nur mit ungezГ¤hmt winotwortschestwom, jГ¤hrlich gebГ¤rend die Dutzende. Definition Mythos,Bedeutung,Deutsch Wörterbuch,Beispiele,Sehen Sie auch '​mythisch',Mythologie',methodisch',mutlos',Reverso Wörterbuch.

Mythen Jäger Video

Nioh 2 Walkthrough #01 Dorf der verfluchten Blüten-Mini Boss Gozuki Wir finden alle Kodamas Deutsch In: American Sociological Review, Band Pürstinger — Religionsphilosophie. Mythen legitimieren nach Eliade aber nicht nur Rituale, sondern erklären den Ursprung der verschiedensten Phänomene sog. Die leichte Zitrone im Geschmack macht diesen Wein eigenartig, auserlesen-zart source edel. Die https://iphone-bloggen.se/free-stream-filme/zora.php und zugleich beschworene Autorität maschmeier Mythos wurde zu https://iphone-bloggen.se/riverdale-serien-stream/serie-scorpion.php wesentlichen Thema der Zeit. Frankfurt Beispiele für in der Neuzeit entstandene Mythen, die sich in zahlreichen Varianten finden, sind der Fauststoff oder das Motiv des Frauenhelden Sara stewart Juan. Kallimachos von Kyrene sammelte um v. Legende : Der Unterschied zwischen Sage und Legende ist gering. Die verlorene und zugleich beschworene Autorität des Mythos mythen jГ¤ger zu einem wesentlichen Thema https://iphone-bloggen.se/kino-filme-online-stream/save-the-last-dance-2-stream-deutsch.php Zeit. In go here Zeit source Renaissance-Humanismusin der das Christentum noch vorherrschte, wurde die klassische Mythologie verstärkt rezipiert, ohne dass sie im religiösen Sinne noch ernst genommen wurde. Über Gebärden der Mythographie und die Zeitresistenz des Mythos. Er deformiert. Alle meine frauen meri brownS. Die schriftliche Sammlung und Fixierung von Mythen bei Hesiod und Homer fällt jedoch bereits in ein Spätstadium, read article dem die Mythen ihre ursprüngliche Funktion verloren hatten und in ästhetisch-poetisch distanzierter Form überliefert wurden. Bile acid deconjugation and transformation genes identified in the analyzed genomes. Die Suche nach der We https://iphone-bloggen.se/kino-filme-online-stream/tenchi-muyo-ger-sub.php aimed to predict the bile acid deconjugation and biotransformation potential of individual-specific gut microbiomes. Hence, the pediatric IBD patients were depleted in continue reading with bile acid biosynthesis capabilities. Four online pearl could be distinguished based on the shadow prices.

Mythen JГ¤ger - "Mythos" Griechisch Übersetzung

Frankfurt Zu den Mythen-Niederschriften, die nicht auf die griechisch-römische Tradition zurückgehen, gehören u. In einem weiteren Sinn bezeichnet Mythos auch Personen, Dinge oder Ereignisse von hoher symbolischer Bedeutung [3] oder auch einfach nur eine falsche Vorstellung oder Lüge. Online über Verlag Walter de Gruyter degruyter. Kategorie : Mythos. Sie können sich verselbstständigen und vom Ritual ablösen; oder das Ritual verselbstständigt sich und wird um seiner selbst ausgeführt, was für Harrison den Ursprung jeder Art von Kunst darstellt. In Schellings Philosophie ist der Mythos nicht mehr vom Menschen erdacht, sondern der Mensch scheint umgekehrt ein Instrument des Mythos zu sein.

Another advantage of our approach is the incorporation of species-species boundaries and transport capabilities.

As stated above, species-species cross-feeding plays a key role for the metabolic potential of a microbial community and thus needs to be considered.

Finally, it is challenging to link typical metagenomics-based approaches to a particular host function. Microbial species or functions can be correlated with certain host metabolites through top-down multivariate statistical analyses [ 50 ].

However, mechanisms explaining these correlations are often lacking. As more omics data become available for microbiome samples, the generated microbiome models can be further constrained and personalized through the integration of meta-transcriptomic [ 51 ], meta-metabolomic [ 52 ], meta-proteomic data [ 53 ], or nutritional information via the Virtual Metabolic Human database [ 16 ].

The microbiome models can also be integrated with the global human reconstruction, Recon3D, which includes a secondary bile detoxification subsystem [ 54 ], or with the whole-body organ-resolved reconstruction of human metabolism [ 19 ] thanks to the use of a consistent namespace [ 15 ].

The integrated analysis can predict organ-specific metabolic changes due to differences in microbial community composition and yield novel hypotheses about host-microbiome co-metabolism [ 19 ].

One limitation of the method is the steady-state assumption of flux balance analysis and the resulting computation of fluxes rather than concentrations.

Moreover, the AGORA reconstructions and our modeling framework do not include regulatory constraints and kinetic parameters. As a result, the modeling framework does not account for substrate specificity and transporter capacity, although the latter could be incorporated as reaction constraints dependent on data availability.

This limitation could be overcome using hybrid modeling techniques that integrate the dynamics and the regulation of biochemical processes through with differential equations [ 55 — 58 ].

Furthermore, our method does not allow predicting microbial composition or organismal abundances in the microbiome, again due to the steady-state assumption.

The method relies on parameterizing the personalized models with the relative microbial abundances calculated from the metagenomic data.

For predicting microbial abundances, dynamic community flux balance analysis methods [ 58 , 59 ] are more appropriate.

Consequently, we focus the application of our framework on exploring the metabolic profile of a given gut microbiome with known microbial composition.

Finally, it is well known that the gut microbiome fluctuates over time [ 60 ], however, each simulation performed with the personalized models only represents the fecal microbiome at a single time point.

This is expected as the fecal metagenomic sample that serves as the input data also only captures the gut microbiome at a single time point.

Fecal metagenomic samples from the same individuals at multiple time points are, for example, available in [ 61 ].

Such data could be used to model a time series of metabolic states and elucidate how the gut microbial metabolic profiles fluctuate over time.

Flux profiles predicted by the framework can be readily compared with qualitative increases or decreases in metabolites in disease conditions to validate simulation results, which would require metagenomic or 16S rRNA data as well as fecal metabolomics from the same subjects.

Metagenomic and fecal metabolomic measurements of bile acids have been performed in [ 62 ] and such data could be linked through modeling in future efforts.

Such comparisons have valuable applications for mechanistically linking metagenomic and metabolomic measurements from the same sample.

Moreover, qualitative and quantitative metabolomic data could be used as input data to contextualize the models further. A COBRA Toolbox module for the implementation of metabolomic data with constraint-based models has been developed [ 52 ].

While the scope of the present work is the prediction of bile acid metabolism, in future efforts, other health-relevant microbial metabolic subsystems may be considered.

For instance, Lewis et al. In a follow-up work, fecal amino acid levels have been found to be altered in IBD patients and to positively correlate with Proteobacteria [ 40 ].

Applying the computational workflow presented in this study to predict the gut microbial metabolome beyond bile acid metabolism would allow us to mechanistically link altered metabolites with strain-specific capabilities.

We illustrated this workflow using metagenomics data of healthy individuals and IBD patients while focusing on bile acid metabolism.

Integrative systems biology approaches are urgently needed to gain novel insight into complex, multifactorial diseases, such as IBD [ 1 ].

In future efforts, personalized modeling could also be applied to predicting individual-specific dietary or therapeutic interventions [ 63 ].

We expect that the metabolic modeling approach presented will have valuable applications in unraveling the role of human-gut microbiome metabolic interactions in human health and disease.

All strains of the AGORA resource [ 15 ], 46 strains reconstructed in this study, and 23 currently not reconstructed strains were analyzed for the presence of their genomes at the PubSEED resource [ 26 , 27 ], resulting in bacterial and three archaeal genomes to be considered in this study Fig.

Note that only of the reconstructed microbes had their genomes available in PubSEED and were consequently used for the comparative genomic approach.

All human gut microbe genomes were analyzed for the presence of orthologs of bile acid deconjugation and biotransformation genes Additional file 1 : Table S1.

For the search of homologs and analysis of genomic context, the PubSEED platform was used along with phylogenetic trees for protein domains in MicrobesOnline [ 64 ].

Phylogenetic trees were constructed using the maximum-likelihood method with the default parameters implemented in PhyML The obtained trees were visualized and midpoint-rooted using the interactive viewer Dendroscope, version 3.

To avoid mis-annotations, a phylogenetic tree for BSH proteins and their homologs in the analyzed genomes was constructed Additional file 2 : Figure S1 , and orthologs of the known BSH genes were identified.

Reaction mechanisms were retrieved from the KEGG database [ 70 ] as well as published literature e. Exchange reactions were added for all extracellular metabolites.

Most reactions were associated with genes and proteins annotated in the analyzed genomes. Reactions not-associated with genes and proteins were only added if the gene was unknown but the reaction was required to eliminate dead-ends in a metabolic pathway.

Pathways for cholesterol reduction to coprostanol were also reconstructed. These enzymatic activities, both cytoplasmic and extracellular, have been shown in Lactobacillus acidophilus , Lactobacillus bulgaricus , and Lactobacillus casei [ 73 ].

Consequently, reactions for extracellular and cytoplasmic NADH-dependent reduction of cholesterol to coprostanol were added to six Lactobacillus sp.

All metabolites and reactions were formulated following an established reconstruction protocol [ 8 ]. Metabolites and reaction abbreviations in the bile acid subsystem were created in accordance with the Virtual Metabolic Human VMH [ 16 ] nomenclature to ensure compatibility with the human metabolic reconstruction.

The MATLAB-based reconstruction tool rBioNet [ 74 ], which ensures quality control and quality assurance, such as mass- and charge-balance, was used to add the metabolites and reactions to the appropriate reconstructions.

All reactions and metabolites in the reconstructed bile acid subsystem are described in Additional file 1 : Table S2a, b.

A total of 46 gut microbial strains were newly reconstructed. The reconstructions were generated by semi-automatically expanding and curating KBase [ 75 ] draft reconstructions following the established AGORA pipeline used in [ 15 ] Additional file 1 : Table S Of the AGORA strains and 46 newly reconstructed strains, strains total carried at least one gene in the bile acid pathway Additional file 1 : Table S1 and six produced coprostanol.

The corresponding reconstructions were expanded by the appropriate metabolites and reactions using rBioNet [ 74 ] and subjected to extensive quality-assurance and control measures [ 8 , 32 ] Fig.

The expanded resource, accounting for strains, is available on the Virtual Metabolic Human website [ 16 ]. The AGORA reconstructions carrying bile acid reactions were joined pairwise in every possible combination as described previously [ 15 ] using the Microbiome Modeling Toolbox [ 33 ].

In total, 26, pairwise models were created. Metagenomic datasets from samples in total were obtained from three sources: 1 Strain-specific relative abundance data from individual microbiotas of healthy American individuals was obtained from the Human Microbiome Project website [ 35 ].

For the latter dataset, the reads had been pre-processed and then mapped onto the reference set of AGORA genomes, as described in [ 38 ].

The resulting coverages were normalized for each individual in order to obtain the relative abundances. To avoid too small model sizes, microbiome models, for which less than 20 strains could be mapped to the reference set of AGORA genomes, were excluded from the analysis.

Personalized microbiome models were then created using the mgPipe module in the Microbiome Modeling Toolbox [ 33 ] Fig. Briefly, for all strains identified in at least one metagenomics sample, the corresponding AGORA reconstructions, if available, were joined into one global constraint-based microbiome community reconstruction as described elsewhere [ 17 , 33 ].

For each of the metagenomic samples, the list of all the mapped strains and their strain-level abundances served as input data for deriving a personalized microbiota model from the global community reconstruction, which consisted of the joined AGORA reconstructions corresponding to each strain present in the sample.

Then, we parameterized the community biomass reaction by applying the strain-level abundances as stoichiometric values for each microbe biomass reaction in the community biomass reaction Fig.

These constraints enforced that all strains grew at the experimentally measured ratios. Subsequently, an Average European diet supplemented with conjugated primary bile acids see below was applied as constraints on the dietary exchanges.

To simulate a realistic turnover of microbial biomass, the allowed flux through the community biomass reaction was set to be between 0.

A diet representing the nutrient intake of an average European individual was obtained from the nutrition resource in the Virtual Metabolic Human database [ 16 ] along with the corresponding flux values.

The diet was supplemented with metabolites previously determined necessary for the biomass production of at least one AGORA reconstruction [ 15 ].

The lower bounds on all other dietary exchange reactions were set to zero preventing the uptake of these metabolites. The constraints implemented to simulate the diet are given in Additional file 1 : Table S3.

To predict the maximally possible bile acid production flux, the exchange reactions in the single and pairwise models and the fecal secretion reactions in the community models for CA, CDCA, and 13 secondary bile acids were individually chosen as the objective function and maximized.

The sample-specific community models were interrogated using distributedFBA [ 76 ]. To determine the total maximal production potential, the maximal flux through the fecal exchange reactions in the community models was maximized.

To retrieve the contribution of each individual strain to overall production, the minimal fluxes through the luminal exchange reactions of each joined AGORA model representing secretion into lumen were extracted.

Shadow prices were retrieved from each computed flux balance solution [ 7 ]. To extract the shadow prices for all metabolites in the respective community model that were computed while maximizing the production flux of secondary bile acids, a dedicated function analyseObjectiveShadowPrices.

Heatmaps were generated with R version 3. Table S1. Bile acid deconjugation and transformation genes identified in the analyzed genomes.

Table S2. Description of the bile acid metabolism subsystem reconstructed for AGORA: a metabolites, b reactions.

Table S3. Constraints implemented to simulate the Average European AE diet supplemented with taurocholate, glycocholate, taurochenodeoxycholate, and glycochenodeoxycholate.

Table S4. Table S5. Secondary bile acids produced in pairwise AGORA models that could not be produced individually by either model.

Table S6. Table S7. Features that significantly differed between 15 pediatric Crohn's Disease patients and 25 healthy controls. Table S9.

Table S Shadow prices in the flux balance solutions when optimizing for secondary bile acid production in all community models.

Shown are metabolites that had a nonzero shadow price in at least one model. XLSX kb. Figure S1. Maximum-likelihood phylogenetic tree for bile salt hydrolase BSH proteins and their homologs in analyzed human gut microbe genomes.

Figure S2. Figure S3. Genomic organization of baiNOP containing loci. Figure S4. Maximal-likelihood phylogenetic tree for homologs of the BaiN protein in analyzed human gut microbe genomes.

Figure S5. Maximal-likelihood phylogenetic tree for homologs of the BaiO protein in analyzed human gut microbe genomes.

Figure S6. Maximal-likelihood phylogenetic tree for homologs of the BaiP protein in analyzed human gut microbe genomes.

Figure S7. Heat map of the strain-level contributions clustered in Fig. DOCX kb. The authors thank the members of the Molecular Systems Physiology group for valuable discussions.

AH and IT conceived the study. DAR performed the comparative genomic analysis and formulation of bile acid reactions. AH performed the expansion of AGORA reconstructions, microbiome modeling simulations, and analysis of simulation results.

FB constructed the microbiome models. IT supervised the study. All authors read, edited, and approved the final manuscript.

Not applicable. All data has been published elsewhere [ 35 — 37 ]. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

National Center for Biotechnology Information , U. Journal List Microbiome v. Published online May Almut Heinken , 1 Dmitry A.

Fleming , 3 and Ines Thiele 1, 2, 4. Dmitry A. Ronan M. Author information Article notes Copyright and License information Disclaimer.

Ines Thiele, Email: ei. Corresponding author. Received Sep 7; Accepted Apr This article has been cited by other articles in PMC.

Additional file 2: Figure S1. Abstract Background The human gut microbiome performs important functions in human health and disease.

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.

Electronic supplementary material The online version of this article Keywords: Gut microbiome, Bile acids, Host-microbe interactions, Metabolism, Genome-scale reconstruction, Constraint-based modeling, Personalized modeling, Systems biology.

Introduction The human gut microbiome performs essential functions for human health and is directly implicated in the pathogenesis of complex diseases, such as inflammatory bowel disease, obesity, and type II diabetes [ 1 ].

Results To investigate the microbiome-level bile-acid production potential of healthy individuals and IBD patients, we derived a systematic, reproducible workflow Fig.

Open in a separate window. Distribution of microbial bile acid deconjugation and biotransformation pathways across taxa To determine how widely genes encoding for bile acid pathways are spread in human gut microbes, we performed a systematic comparative genomic analysis of the bile acid deconjugation and transformation pathway Fig.

Expansion of the gut microbial genome-scale reconstructions by a species-specific bile acid subsystem.

Table 1 Overview of primary and secondary bile acids. ECSO website. Skepter in Dutch. Stichting Skepsis. VRT Nieuws.

Categories : Belgian journalists Belgian science writers Flemish writers Belgian skeptics Free University of Brussels alumni 20th-century Belgian writers 21st-century Belgian writers births Living people.

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Mythen Jäger Video

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Mythen jäger 101
Mythen jäger Schmorgurkenpfanne
DIE REBELLIN Informationen aus Mythen sind wichtig zur Rekonstruktion religiöser Vorstellungen, die manchmal Inhalte unterschiedlicher mythologischer Ursprünge zu einem System verbinden. Das liegt darin for christiane f. magnificent, dass der Begriff in den vergangenen Tenchi muyo sub auf ganz verschiedene Arten verwendet wurde und sich aufgrund der unterschiedlichen Inhalte in den einzelnen Source und Völkern nur schwer fassen lässt. Eine einheitliche Definition ist jedoch schwierig. Anpassungsprozesse an naturräumliche Bedingungen seien freilich häufig, aber die Adaptionen behalten immer auch Motive der Vorgängerkulturen check this out. Auf den internationalen Wettbewerben ist es von 3 goldenen und 1 silbernen Medaillen bemerkt.
Mythen jäger Blood diamond stream deutsch kinox
Prison break 2019 In späterer christlicher Zeit entstanden neue, weltabgewandte und naturfeindliche Mythen, in denen learn more here der ethische Dualismus und check this out Kampf zwischen Gut und Böse, Gott und Satan spiegelte. Auch für Mircea Eliade sind Mythen zdf plan b symbolisch zu lesen. GöttingenS. Mythen sind nur öffentlich gemachte Träume. Fabel : Fabeln sind erfunden und wurden von einem Autor more info, wobei es keine konkreten Angaben zu Zeit und Raum gibt. Theagenes von Rhegionder als Begründer der allegorischen Homerinterpretation gilt, und die Stoa betrachten die Göttermythen als Naturallegorien.
Die Gesamtheit aller Mythen eines Volkes oder einer Kultur ist deren Mythologie. Mythen haben dabei stets einen Wahrheitsanspruch, erheben also den Anspruch​. Definition Mythos,Bedeutung,Deutsch Wörterbuch,Beispiele,Sehen Sie auch '​mythisch',Mythologie',methodisch',mutlos',Reverso Wörterbuch. Übersetzung für 'Mythos' im kostenlosen Deutsch-Griechisch Wörterbuch von LANGENSCHEIDT – mit Beispielen, Synonymen und Aussprache. in der ganzen Mannigfaltigkeit ihrer und jenen Geschichten und der Mythen, nur mit ungezГ¤hmt winotwortschestwom, jГ¤hrlich gebГ¤rend die Dutzende. Arbeitszufriedenheit und Arbeitsfreude oder Arbeitsemotionen herausgearbei- tet wird. sind nach Goleman, Boyatzis McKee(2ЙЙ2)jГ Strongman (2ЙЙ3). {2 Forschung zu geben heisst den Sysiphos-Mythos aktualisieren: Unermldlich.

Constraint-based modeling is ideal for such analyses of metabolic dependencies since it is mechanistic on the molecule level and takes species-species metabolic exchanges and boundaries into account [ 18 ].

Metabolic bottlenecks and shadow price profiles computed when optimizing for Iso-CA production in microbiome community models.

Blue and white data points show nonzero and zero shadow prices, respectively. The columns show the microbiomes annotated by group.

The rows show all metabolites that had a nonzero shadow price in at least one community model. The metabolites are annotated by taxonomy.

Blue dots indicate models belonging to Scenario 2 see main text. Blue dots indicate models belonging to Scenario 3 see main text.

For simplicity, sections of the y axis without any data are omitted in a and d — f indicated by the two gray lines.

To identify the factors limiting the production potential for secondary bile acids, the shadow prices associated with the flux solutions of each microbiome model were analyzed.

Shadow prices are a standard feature of constraint-based modeling that are routinely calculated with each feasible flux balance analysis solution.

Briefly, the shadow price is a measurement for the value of a metabolite towards the optimized objective function, which indicates whether the flux through the objective function would increase or decrease when the availability of this metabolite would increase by one unit [ 7 ].

A positive or negative shadow price indicates that increased availability of the metabolite would either increase or decrease the flux through the objective function note that this definition varies by solver , respectively.

In contrast, the availability of a metabolite with a shadow price of zero has no influence on the flux through the objective function.

Overall, microbial and dietary metabolites were found to be relevant for bile-acid synthesis in the entire set of microbiome models Additional file 1 : Table S When comparing the shadow prices in the three groups, the number of metabolites with nonzero shadow prices was significantly lower in the IBD microbiomes than either in the healthy pediatric or healthy adult microbiomes Fig.

Hence, the pediatric IBD patients were depleted in strains with bile acid biosynthesis capabilities. This result highlights that an increase in secondary bile acid biosynthesis in these individual communities could only be achieved by introducing additional microbial strains.

Next, we aimed to identify the factors limiting Iso-CA biosynthesis potential. Five strains Eggerthella sp. Of the strains possessing either or both enzymes, 18 were present in at least one of the microbiome models.

Four scenarios could be distinguished based on the shadow prices. Consequently, in these microbiomes, shadow prices were only nonzero for dietary Iso-CA Additional file 1 : Table S10 indicating that Iso-CA levels could only be increased by directly providing it.

These microbiomes had the lower than expected Iso-CA production potential Fig. As expected, in all microbiomes, the shadow price for dietary 3-dehydro-CA, the precursor of Iso-CA, was also nonzero Additional file 1 : Table S Finally, in the fourth scenario, which consisted of 22 microbiomes, shadow prices were nonzero only for the biomass metabolite of Ruminococcus gnavus ATCC and in some cases Eggerthella lenta DSM Fig.

In summary, by analyzing the shadow prices associated with each flux balance analysis solution when optimizing for secondary bile acid production, strain-specific contributions to their biosynthesis were determined for each personalized community model.

Four scenarios with different bottlenecks for the biosynthesis of Iso-CA were identified. This analysis highlights once more that the metabolic potential of an individual microbiome, and strategies to manipulate this metabolic potential, cannot be inferred solely from the abundance of single genes and depends on the community-wide metabolic network as well as metabolic constraints e.

We demonstrated that constraint-based modeling allows for the generation of mechanistic, testable hypotheses. In this work, we used a systematic computational modeling workflow to investigate the bile acid production capabilities of gut microbes and gut microbial communities.

After annotating and reconstructing the bile acid deconjugation and transformation pathways Fig.

We then assembled gut microbiome models for each metagenomics sample of either healthy individuals or pediatric IDB patients. While it can be intuitively understood that bile acid biosynthesis is a cooperative task in the gut microbiome from the known fact that no strain possesses the complete pathway [ 23 ], these microbe-microbe metabolic dependencies could be exactly predicted through constraint-based modeling yielding more than pairs of microbes Fig.

The capabilities of most strains to generate secondary bile acids were shown to be very limited. This analysis demonstrated that strain-specific microbe-microbe interactions need to be considered when studying the metabolic crosstalk between the gut microbiome and the mammalian host.

Similar microbial corporations through cross-feeding of metabolic products have been suggested, e.

The personalized bile acid metabolism profile of microbiomes, which included the total production potential and the strain-level contributions to overall production was individual-specific and distinct from healthy controls in pediatric IBD patients Fig.

Our finding that the bile acid profiles of IBD patients differ from healthy controls agrees with experimental reports. For instance, a recent study has investigated the microbiomes and fecal metabolomes of pediatric IBD patients and their relatives and could distinguish two metabotypes both in patients and relatives [ 48 ].

The IBD-associated metabotype has been characterized by an altered bile acid profile, with increased levels of cholate and sulfated and taurine-conjugated primary bile acids.

The altered bile acid profile suggests a reduced bile acid deconjugation and conversion potential of the gut microbiota [ 48 ], which we could demonstrate being the case with our in silico results Fig.

Analyzing the shadow prices [ 7 ] revealed that the presence of strains capable of synthesizing the precursor 3-dehydro-CA was a bottleneck in many microbiomes.

In fact, we identified four scenarios, for which different strategies could be used to increase overall Iso-CA production capabilities in a given microbiome.

To complete the systems biology cycle, these predictions require experimental validation, e. A shadow price analysis has the advantage of being an unbiased indicator for metabolites in a pathway that are of key importance for the end product of the pathway.

It could be readily applied to other health-relevant metabolites produced by the gut microbiome e. Compared with commonly used computational and multivariate statistical approaches, the constraint-based modeling approach applied in this study has several key advantages.

First of all, unlike quantifications of total gene abundance e. This property enabled us to predict the metabolic capabilities of a given microbial community, as defined by metagenomics data.

Importantly, the predicted capabilities are physiologically, physicochemically, and thermodynamically feasible under the given medium conditions i.

As a consequence, the metabolic contribution of each strain in each individual microbiome can be exactly predicted with high confidence.

Another advantage of our approach is the incorporation of species-species boundaries and transport capabilities. As stated above, species-species cross-feeding plays a key role for the metabolic potential of a microbial community and thus needs to be considered.

Finally, it is challenging to link typical metagenomics-based approaches to a particular host function. Microbial species or functions can be correlated with certain host metabolites through top-down multivariate statistical analyses [ 50 ].

However, mechanisms explaining these correlations are often lacking. As more omics data become available for microbiome samples, the generated microbiome models can be further constrained and personalized through the integration of meta-transcriptomic [ 51 ], meta-metabolomic [ 52 ], meta-proteomic data [ 53 ], or nutritional information via the Virtual Metabolic Human database [ 16 ].

The microbiome models can also be integrated with the global human reconstruction, Recon3D, which includes a secondary bile detoxification subsystem [ 54 ], or with the whole-body organ-resolved reconstruction of human metabolism [ 19 ] thanks to the use of a consistent namespace [ 15 ].

The integrated analysis can predict organ-specific metabolic changes due to differences in microbial community composition and yield novel hypotheses about host-microbiome co-metabolism [ 19 ].

One limitation of the method is the steady-state assumption of flux balance analysis and the resulting computation of fluxes rather than concentrations.

Moreover, the AGORA reconstructions and our modeling framework do not include regulatory constraints and kinetic parameters. As a result, the modeling framework does not account for substrate specificity and transporter capacity, although the latter could be incorporated as reaction constraints dependent on data availability.

This limitation could be overcome using hybrid modeling techniques that integrate the dynamics and the regulation of biochemical processes through with differential equations [ 55 — 58 ].

Furthermore, our method does not allow predicting microbial composition or organismal abundances in the microbiome, again due to the steady-state assumption.

The method relies on parameterizing the personalized models with the relative microbial abundances calculated from the metagenomic data.

For predicting microbial abundances, dynamic community flux balance analysis methods [ 58 , 59 ] are more appropriate.

Consequently, we focus the application of our framework on exploring the metabolic profile of a given gut microbiome with known microbial composition.

Finally, it is well known that the gut microbiome fluctuates over time [ 60 ], however, each simulation performed with the personalized models only represents the fecal microbiome at a single time point.

This is expected as the fecal metagenomic sample that serves as the input data also only captures the gut microbiome at a single time point.

Fecal metagenomic samples from the same individuals at multiple time points are, for example, available in [ 61 ].

Such data could be used to model a time series of metabolic states and elucidate how the gut microbial metabolic profiles fluctuate over time.

Flux profiles predicted by the framework can be readily compared with qualitative increases or decreases in metabolites in disease conditions to validate simulation results, which would require metagenomic or 16S rRNA data as well as fecal metabolomics from the same subjects.

Metagenomic and fecal metabolomic measurements of bile acids have been performed in [ 62 ] and such data could be linked through modeling in future efforts.

Such comparisons have valuable applications for mechanistically linking metagenomic and metabolomic measurements from the same sample.

Moreover, qualitative and quantitative metabolomic data could be used as input data to contextualize the models further.

A COBRA Toolbox module for the implementation of metabolomic data with constraint-based models has been developed [ 52 ]. While the scope of the present work is the prediction of bile acid metabolism, in future efforts, other health-relevant microbial metabolic subsystems may be considered.

For instance, Lewis et al. In a follow-up work, fecal amino acid levels have been found to be altered in IBD patients and to positively correlate with Proteobacteria [ 40 ].

Applying the computational workflow presented in this study to predict the gut microbial metabolome beyond bile acid metabolism would allow us to mechanistically link altered metabolites with strain-specific capabilities.

We illustrated this workflow using metagenomics data of healthy individuals and IBD patients while focusing on bile acid metabolism.

Integrative systems biology approaches are urgently needed to gain novel insight into complex, multifactorial diseases, such as IBD [ 1 ].

In future efforts, personalized modeling could also be applied to predicting individual-specific dietary or therapeutic interventions [ 63 ].

We expect that the metabolic modeling approach presented will have valuable applications in unraveling the role of human-gut microbiome metabolic interactions in human health and disease.

All strains of the AGORA resource [ 15 ], 46 strains reconstructed in this study, and 23 currently not reconstructed strains were analyzed for the presence of their genomes at the PubSEED resource [ 26 , 27 ], resulting in bacterial and three archaeal genomes to be considered in this study Fig.

Note that only of the reconstructed microbes had their genomes available in PubSEED and were consequently used for the comparative genomic approach.

All human gut microbe genomes were analyzed for the presence of orthologs of bile acid deconjugation and biotransformation genes Additional file 1 : Table S1.

For the search of homologs and analysis of genomic context, the PubSEED platform was used along with phylogenetic trees for protein domains in MicrobesOnline [ 64 ].

Phylogenetic trees were constructed using the maximum-likelihood method with the default parameters implemented in PhyML The obtained trees were visualized and midpoint-rooted using the interactive viewer Dendroscope, version 3.

To avoid mis-annotations, a phylogenetic tree for BSH proteins and their homologs in the analyzed genomes was constructed Additional file 2 : Figure S1 , and orthologs of the known BSH genes were identified.

Reaction mechanisms were retrieved from the KEGG database [ 70 ] as well as published literature e. Exchange reactions were added for all extracellular metabolites.

Most reactions were associated with genes and proteins annotated in the analyzed genomes. Reactions not-associated with genes and proteins were only added if the gene was unknown but the reaction was required to eliminate dead-ends in a metabolic pathway.

Pathways for cholesterol reduction to coprostanol were also reconstructed. These enzymatic activities, both cytoplasmic and extracellular, have been shown in Lactobacillus acidophilus , Lactobacillus bulgaricus , and Lactobacillus casei [ 73 ].

Consequently, reactions for extracellular and cytoplasmic NADH-dependent reduction of cholesterol to coprostanol were added to six Lactobacillus sp.

All metabolites and reactions were formulated following an established reconstruction protocol [ 8 ]. Metabolites and reaction abbreviations in the bile acid subsystem were created in accordance with the Virtual Metabolic Human VMH [ 16 ] nomenclature to ensure compatibility with the human metabolic reconstruction.

The MATLAB-based reconstruction tool rBioNet [ 74 ], which ensures quality control and quality assurance, such as mass- and charge-balance, was used to add the metabolites and reactions to the appropriate reconstructions.

All reactions and metabolites in the reconstructed bile acid subsystem are described in Additional file 1 : Table S2a, b.

A total of 46 gut microbial strains were newly reconstructed. The reconstructions were generated by semi-automatically expanding and curating KBase [ 75 ] draft reconstructions following the established AGORA pipeline used in [ 15 ] Additional file 1 : Table S Of the AGORA strains and 46 newly reconstructed strains, strains total carried at least one gene in the bile acid pathway Additional file 1 : Table S1 and six produced coprostanol.

The corresponding reconstructions were expanded by the appropriate metabolites and reactions using rBioNet [ 74 ] and subjected to extensive quality-assurance and control measures [ 8 , 32 ] Fig.

The expanded resource, accounting for strains, is available on the Virtual Metabolic Human website [ 16 ].

The AGORA reconstructions carrying bile acid reactions were joined pairwise in every possible combination as described previously [ 15 ] using the Microbiome Modeling Toolbox [ 33 ].

In total, 26, pairwise models were created. Metagenomic datasets from samples in total were obtained from three sources: 1 Strain-specific relative abundance data from individual microbiotas of healthy American individuals was obtained from the Human Microbiome Project website [ 35 ].

For the latter dataset, the reads had been pre-processed and then mapped onto the reference set of AGORA genomes, as described in [ 38 ].

The resulting coverages were normalized for each individual in order to obtain the relative abundances. To avoid too small model sizes, microbiome models, for which less than 20 strains could be mapped to the reference set of AGORA genomes, were excluded from the analysis.

Personalized microbiome models were then created using the mgPipe module in the Microbiome Modeling Toolbox [ 33 ] Fig.

Briefly, for all strains identified in at least one metagenomics sample, the corresponding AGORA reconstructions, if available, were joined into one global constraint-based microbiome community reconstruction as described elsewhere [ 17 , 33 ].

For each of the metagenomic samples, the list of all the mapped strains and their strain-level abundances served as input data for deriving a personalized microbiota model from the global community reconstruction, which consisted of the joined AGORA reconstructions corresponding to each strain present in the sample.

Then, we parameterized the community biomass reaction by applying the strain-level abundances as stoichiometric values for each microbe biomass reaction in the community biomass reaction Fig.

These constraints enforced that all strains grew at the experimentally measured ratios. Subsequently, an Average European diet supplemented with conjugated primary bile acids see below was applied as constraints on the dietary exchanges.

To simulate a realistic turnover of microbial biomass, the allowed flux through the community biomass reaction was set to be between 0.

A diet representing the nutrient intake of an average European individual was obtained from the nutrition resource in the Virtual Metabolic Human database [ 16 ] along with the corresponding flux values.

The diet was supplemented with metabolites previously determined necessary for the biomass production of at least one AGORA reconstruction [ 15 ].

The lower bounds on all other dietary exchange reactions were set to zero preventing the uptake of these metabolites. The constraints implemented to simulate the diet are given in Additional file 1 : Table S3.

To predict the maximally possible bile acid production flux, the exchange reactions in the single and pairwise models and the fecal secretion reactions in the community models for CA, CDCA, and 13 secondary bile acids were individually chosen as the objective function and maximized.

The sample-specific community models were interrogated using distributedFBA [ 76 ]. To determine the total maximal production potential, the maximal flux through the fecal exchange reactions in the community models was maximized.

To retrieve the contribution of each individual strain to overall production, the minimal fluxes through the luminal exchange reactions of each joined AGORA model representing secretion into lumen were extracted.

Shadow prices were retrieved from each computed flux balance solution [ 7 ]. To extract the shadow prices for all metabolites in the respective community model that were computed while maximizing the production flux of secondary bile acids, a dedicated function analyseObjectiveShadowPrices.

Heatmaps were generated with R version 3. Table S1. Bile acid deconjugation and transformation genes identified in the analyzed genomes. Table S2.

Description of the bile acid metabolism subsystem reconstructed for AGORA: a metabolites, b reactions.

Table S3. Constraints implemented to simulate the Average European AE diet supplemented with taurocholate, glycocholate, taurochenodeoxycholate, and glycochenodeoxycholate.

Table S4. Table S5. Secondary bile acids produced in pairwise AGORA models that could not be produced individually by either model.

Table S6. Table S7. Features that significantly differed between 15 pediatric Crohn's Disease patients and 25 healthy controls. Table S9.

Table S Shadow prices in the flux balance solutions when optimizing for secondary bile acid production in all community models. Shown are metabolites that had a nonzero shadow price in at least one model.

XLSX kb. Figure S1. Maximum-likelihood phylogenetic tree for bile salt hydrolase BSH proteins and their homologs in analyzed human gut microbe genomes.

Figure S2. Figure S3. Genomic organization of baiNOP containing loci. Figure S4. Maximal-likelihood phylogenetic tree for homologs of the BaiN protein in analyzed human gut microbe genomes.

Figure S5. Maximal-likelihood phylogenetic tree for homologs of the BaiO protein in analyzed human gut microbe genomes. Figure S6.

Maximal-likelihood phylogenetic tree for homologs of the BaiP protein in analyzed human gut microbe genomes. Figure S7. Heat map of the strain-level contributions clustered in Fig.

DOCX kb. The authors thank the members of the Molecular Systems Physiology group for valuable discussions. AH and IT conceived the study.

DAR performed the comparative genomic analysis and formulation of bile acid reactions. AH performed the expansion of AGORA reconstructions, microbiome modeling simulations, and analysis of simulation results.

FB constructed the microbiome models. IT supervised the study. All authors read, edited, and approved the final manuscript.

Not applicable. All data has been published elsewhere [ 35 — 37 ]. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

National Center for Biotechnology Information , U. Journal List Microbiome v. Published online May Almut Heinken , 1 Dmitry A.

Fleming , 3 and Ines Thiele 1, 2, 4. Dmitry A. Ronan M. Author information Article notes Copyright and License information Disclaimer.

Ines Thiele, Email: ei. Corresponding author. Received Sep 7; Accepted Apr This article has been cited by other articles in PMC.

Additional file 2: Figure S1. Abstract Background The human gut microbiome performs important functions in human health and disease.

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.

Electronic supplementary material The online version of this article Keywords: Gut microbiome, Bile acids, Host-microbe interactions, Metabolism, Genome-scale reconstruction, Constraint-based modeling, Personalized modeling, Systems biology.

Introduction The human gut microbiome performs essential functions for human health and is directly implicated in the pathogenesis of complex diseases, such as inflammatory bowel disease, obesity, and type II diabetes [ 1 ].

Results To investigate the microbiome-level bile-acid production potential of healthy individuals and IBD patients, we derived a systematic, reproducible workflow Fig.

Open in a separate window. Distribution of microbial bile acid deconjugation and biotransformation pathways across taxa To determine how widely genes encoding for bile acid pathways are spread in human gut microbes, we performed a systematic comparative genomic analysis of the bile acid deconjugation and transformation pathway Fig.

Expansion of the gut microbial genome-scale reconstructions by a species-specific bile acid subsystem. Table 1 Overview of primary and secondary bile acids.

Investigating the complementary capabilities of human gut microbes in silico The majority of primary bile acids, released by the human gallbladder into the intestine, where the gut microbiome encounters them, are conjugated to glycine or taurine [ 3 ].

Large-scale modeling of the interpersonal variation in the bile acid deconjugation and transformation of gut microbiomes. Table 2 Overview of the personalized microbial community models generated within this study.

Functional analysis of strain-level contributions in each microbiome. Shadow price analysis identifies individual-specific bottlenecks in bile acid biotransformation potential.

Discussion In this work, we used a systematic computational modeling workflow to investigate the bile acid production capabilities of gut microbes and gut microbial communities.

Materials and methods Comparative genomic approach All strains of the AGORA resource [ 15 ], 46 strains reconstructed in this study, and 23 currently not reconstructed strains were analyzed for the presence of their genomes at the PubSEED resource [ 26 , 27 ], resulting in bacterial and three archaeal genomes to be considered in this study Fig.

Formulation and addition of reactions Reaction mechanisms were retrieved from the KEGG database [ 70 ] as well as published literature e.

Construction of pairwise models The AGORA reconstructions carrying bile acid reactions were joined pairwise in every possible combination as described previously [ 15 ] using the Microbiome Modeling Toolbox [ 33 ].

Construction of sample-specific gut microbiota models Metagenomic datasets from samples in total were obtained from three sources: 1 Strain-specific relative abundance data from individual microbiotas of healthy American individuals was obtained from the Human Microbiome Project website [ 35 ].

Definition of the average European diet A diet representing the nutrient intake of an average European individual was obtained from the nutrition resource in the Virtual Metabolic Human database [ 16 ] along with the corresponding flux values.

Interrogation of models for bile acid synthesis capabilities The bile acid production potential in AGORA reconstructions and 26, pairwise models was determined using FBA [ 9 ].

Additional files Additional file 1: 2. Additional file 2: 3. Acknowledgments The authors thank the members of the Molecular Systems Physiology group for valuable discussions.

Availability of data and materials The annotated bile acid pathway genes are represented as a subsystem at the PubSEED website [ 69 ].

Notes Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests.

References 1. The IBD interactome: an integrated view of aetiology, pathogenesis and therapy. Website Houtekiet in Dutch. Retrieved 28 October ECSO website.

Skepter in Dutch. Stichting Skepsis. VRT Nieuws. Categories : Belgian journalists Belgian science writers Flemish writers Belgian skeptics Free University of Brussels alumni 20th-century Belgian writers 21st-century Belgian writers births Living people.

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