BacArena: Individual-based metabolic modeling of heterogeneous microbes in complex communities

Bauer, Eugen; Zimmermann, Johannes; Baldini, Federico; Thiele, Ines; Kaleta, Christoph

doi:10.1371/journal.pcbi.1005544

Cited by 202 publications

(195 citation statements)

References 78 publications

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“…Several approaches that integrate the metabolic and spatial environment have been developed that account for context dependency [46][47][48] , but limitations in biochemical knowledge across the bacterial tree of life limit their broad application to other organisms. Similar approaches have been applied to simplified versions of complex communities such as the human gut microbiota [48][49][50] . However, the poor experimental tractability of these systems makes testing predicted interspecies interactions challenging and thus they are left unvalidated.…”

Section: Discussionmentioning

confidence: 99%

Metabolic mechanisms of interaction within a defined gut microbiota

Medlock

Carey

McDuffie

et al. 2018

Preprint

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Metabolic interactions among species are ubiquitous in nature, and the fitness costs and benefits they impose often reinforce and stabilize them over time. These interactions are of particular importance in the human gut, where they have functions ranging from enhancing digestion to preventing (or exacerbating) infections. The diversity and sheer number of species present lead to the potential for a multitude of metabolic interactions among species to occur. However, identifying the mechanism and consequences of metabolic interactions between even two species is incredibly challenging. Here, we develop, apply, and experimentally test a framework for identifying potential metabolic mechanisms associated with interspecies interactions. We perform pairwise growth and metabolome profiling of cocultures of strains from the altered Schaedler flora (ASF), a defined murine microbiota. We then apply our novel framework, which we call the Constant Yield Expectation (ConYE) model, to dissect emergent metabolic behaviors that occur in coculture. Using the ConYE model, we identify and interrogate an amino acid crossfeeding interaction that is likely to confer a growth benefit to one ASF strain ( Clostridium sp. ASF356 ) in coculture with another strain ( Parabacteroides goldsteinii ASF519 ). We experimentally validate that the proposed interaction leads to a growth benefit for this strain via media supplementation experiments. Our results reveal the type and extent of emergent metabolic behavior in microbial communities and demonstrate how metabolomic data can be used to identify potential metabolic interactions between organisms such as gut microbes. Our in vitro characterization of the ASF strains and interactions between them also enhances our ability to interpret and design experiments that utilize ASFcolonized animals. We anticipate that this work will improve the tractability of studies utilizing mice colonized with the ASF. Here, we focus on growthmodulating interactions, but the framework we develop can be applied to generate specific hypotheses about mechanisms of interspecies interaction involved in any phenotype of interest within a microbial community.

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Section: Discussionmentioning

confidence: 99%

Metabolic mechanisms of interaction within a defined gut microbiota

Medlock

Carey

McDuffie

et al. 2018

Preprint

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“…In addition, the following R packages are needed: data.table [19], stringr [91], sybil [28], getopt [17], reshape2 [89], doParallel [16], foreach [53], R.utils [5], stringi [23], glpkAPI [27], and BioStrings [58]. Models can be exported as SBML [9] file using sybilSBML [28] or R data format (RDS) for further analysis in R, for example with sybil [28] or BacArena [3].…”

Section: Technical Detailsmentioning

confidence: 99%

“…Only pathways relevant for the formation of acetate, hydrogen, Figure 6: Predicted human gut microbial community metabolism using gapseq models. The metabolism of a community consisting of 19 bacterial species and one archaeon (Methanosarcina barkeri) was predicted using BacArena [3]. All bacterial models were reconstructed using gapseq and a published manually curated model was used for M. barkeri.…”

Section: Sample Application II -Pathway Prediction Of Soil and Gut Mimentioning

confidence: 99%

gapseq: Informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models

Zimmermann

Kaleta

Waschina

2020

Preprint

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Microbial metabolic processes greatly impact ecosystem functioning and the physiology of multi-cellular host organisms. The inference of metabolic capabilities and phenotypes from genome sequences with the help of prior biomolecular knowledge stored in online databases remains a major challenge in systems biology. Here, we present gapseq: a novel tool for automated pathway prediction and metabolic network reconstruction from microbial genome sequences. gapseq combines databases of reference protein sequences (UniProt, TCDB), in tandem with pathway and reaction databases (MetaCyc, KEGG, ModelSEED). This enables the statistical prediction of an organism's metabolic capabilities from sequence homology and pathway topology criteria. By incorporating a novel LP-based gap-filling algorithm, gapseq facilitates the construction of genomescale metabolic models that are suitable for metabolic phenotype predictions by using constraint-based flux analysis. We validated gapseq by comparing predictions to experimental data for more than 1, 000 bacterial organisms comprising over 10, 000 phenotypic traits that include enzyme activity, energy sources, fermentation products, and gene essentiality. This large-scale phenotypic trait prediction test showed, that gapseq yields an overall accuracy of 80% and thereby outperforming other commonly used reconstruction tools. Furthermore, we illustrate the application of gapseq-reconstructed models to simulate biochemical interactions between microorganisms in multi-species communities. Altogether, gapseq (https://github.com/jotech/gapseq) is a new method that improves the predictive potential of automated metabolic network reconstructions and further increases their applicability in biotechnological, ecological, and medical research.

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“…This is achieved through modeling each single cell and simulating its metabolism with respect to its spatial position within the microbiota environment. The combination of the COBRA tool and individual‐based modeling to create BacArena was used to simulate the spatiotemporal dynamics of metabolic interactions in microbial communities, which involve the exchange of fermentation products. This tool enabled the simultaneous exploration of different metabolic phenotypes of multiple individual strain at different times and locations within a microbial community, which has moved on to predicting dietary supplements for Crohn's disease …”

Section: Metabolic Modelingmentioning

confidence: 99%

Meta‐Omics‐ and Metabolic Modeling‐Assisted Deciphering of Human Microbiota Metabolism

Sieow

Nurminen

Ling

2019

Biotechnology Journal

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The human microbiota is a complex community of commensal, symbiotic, and pathogenic microbes that play a crucial role in maintaining the homeostasis of human health. Such a homeostasis is maintained through the collective functioning of enzymatic genes responsible for the production of metabolites, enabling the interaction and signaling within microbiota as well as between microbes and the human host. Understanding microbial genes, their associated chemistries and functions would be valuable for engineering systemic metabolic pathways within the microbiota to manage human health and diseases. Given that there are many unknown gene metabolic functions and interactions, increasing efforts have been made to gain insights into the underlying functions of microbiota metabolism. This can be achieved through culture‐independent metagenomic approaches and metabolic modeling to simulate the microenvironment of human microbiota. In this article, the recent advances in metagenome mining and functional profiling for the discovery of the genetic and biochemical links in human microbiota metabolism as well as metabolic modeling for simulation and prediction of metabolic fluxes in the human microbiota are reviewed. This review provides useful insights into the understanding, reconstruction, and modulation of the human microbiota guided by the knowledge acquired from the basic understanding of the human microbiota metabolism.

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BacArena: Individual-based metabolic modeling of heterogeneous microbes in complex communities

Cited by 202 publications

References 78 publications

Metabolic mechanisms of interaction within a defined gut microbiota

Metabolic mechanisms of interaction within a defined gut microbiota

gapseq: Informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models

Meta‐Omics‐ and Metabolic Modeling‐Assisted Deciphering of Human Microbiota Metabolism

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