MMinte: an application for predicting metabolic interactions among the microbial species in a community

Mendes‐Soares, Helena; Mundy, Michael B.; Soares, Luis Mendes; Chia, Nicholas

doi:10.1186/s12859-016-1230-3

Cited by 68 publications

(55 citation statements)

References 61 publications

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“…Namely, we used these models to generate hypotheses about associations between predicted metabolic interactions among these organisms and patterns in their cooccurrence or exclusion. Details of this analysis are discussed in references 28, 34, and 47, but briefly, we conducted a BLAST sequence similarity search (63) comparing each enriched OTU to a database of 16S rRNA gene amplicon sequences for prokaryotic taxa with whole-genome sequences in NCBI, compiled by Mendes-Soares et al (47). The ModelSEED framework (46) was used to reconstruct and gap-fill metabolic models for the genomes most similar to eelgrass-enriched OTUs.…”

Section: Methodsmentioning

confidence: 99%

“…But, while predictions from metabolic modeling are consistent with prior studies of host-supplied compounds on seagrass surfaces (e.g., see reference 23), these predictions come with caveats and should be interpreted as hypotheses. The metabolic models analyzed herein are derived from 16S rRNA sequences and involve automated metabolic network reconstruction (46,47). This approach may be less accurate than manual curation of metabolic models and is limited by the availabilities of sequenced prokaryotic genomes, but it permits the analysis of a large number of microbial taxa that would otherwise be intractable and indeed reflects a large proportion of the metabolic needs of these organisms (47).…”

Section: Figmentioning

confidence: 99%

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Global-scale structure of the eelgrass microbiome

Fahimipour

Kardish

Eisen

et al. 2016

Preprint

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Plant-associated microorganisms are essential for their hosts' survival and performance. Yet, most plant microbiome studies to date have focused on terrestrial species sampled across relatively small spatial scales. Here, we report the results of a global-scale analysis of microbial communities associated with leaf and root surfaces of the marine eelgrass Zostera marina throughout its range in the Northern Hemisphere. By contrasting host microbiomes with those of surrounding seawater and sediment, we uncovered the structure, composition, and variability of microbial communities associated with eelgrass. We also investigated hypotheses about the assembly of the eelgrass microbiome using a metabolic modeling approach. Our results reveal leaf communities displaying high variability and spatial turnover that mirror their adjacent coastal seawater microbiomes. By contrast, roots showed relatively low compositional turnover and were distinct from surrounding sediment communities, a result driven by the enrichment of predicted sulfur-oxidizing bacterial taxa on root surfaces. Predictions from metabolic modeling of enriched taxa were consistent with a habitat-filtering community assembly mechanism whereby similarity in resource use drives taxonomic cooccurrence patterns on belowground, but not aboveground, host tissues. Our work provides evidence for a core eelgrass root microbiome with putative functional roles and highlights potentially disparate processes influencing microbial community assembly on different plant compartments.IMPORTANCE Plants depend critically on their associated microbiome, yet the structure of microbial communities found on marine plants remains poorly understood in comparison to that for terrestrial species. Seagrasses are the only flowering plants that live entirely in marine environments. The return of terrestrial seagrass ancestors to oceans is among the most extreme habitat shifts documented in plants, making them an ideal testbed for the study of microbial symbioses with plants that experience relatively harsh abiotic conditions. In this study, we report the results of a global sampling effort to extensively characterize the structure of microbial communities associated with the widespread seagrass species Zostera marina, or eelgrass, across its geographic range. Our results reveal major differences in the structure and composition of above-versus belowground microbial communities on eelgrass surfaces, as well as their relationships with the environment and host.

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Section: Methodsmentioning

confidence: 99%

Section: Figmentioning

confidence: 99%

Global-scale structure of the eelgrass microbiome

Fahimipour

Kardish

Eisen

et al. 2016

Preprint

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“…Microbial metabolic models and the tools built around these models provide an opportunity to examine microbial community interactions [38–40] and test the relationship between microbial hydrogen sulfide production and CRC. Genome-scale metabolic models (GEMs) are reconstructed metabolic networks based on complete annotated genome sequences [41].…”

Section: Introductionmentioning

confidence: 99%

“…Recently developed tools allow users to assess pairwise (e.g. MMINTE [38]) or community interactions (e.g. MICOM [40,43]) between microbial GEMs.…”

Section: Introductionmentioning

confidence: 99%

Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer

Hale

Jeraldo

Mundy

et al. 2018

Methods

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Multi-omic data and genome-scale microbial metabolic models have allowed us to examine microbial communities, community function, and interactions in ways that were not available to us historically. Now, one of our biggest challenges is determining how to integrate data and maximize data potential. Our study demonstrates one way in which to test a hypothesis by combining multi-omic data and community metabolic models. Specifically, we assess hydrogen sulfide production in colorectal cancer based on stool, mucosa, and tissue samples collected on and off the tumor site within the same individuals. 16S rRNA microbial community and abundance data were used to select and inform the metabolic models. We then used MICOM, an open source platform, to track the metabolic flux of hydrogen sulfide through a defined microbial community that either represented on-tumor or off-tumor sample communities. We also performed targeted and untargeted metabolomics, and used the former to quantitatively evaluate our model predictions. A deeper look at the models identified several unexpected but feasible reactions, microbes, and microbial interactions involved in hydrogen sulfide production for which our 16S and metabolomic data could not account. These results will guide future in vitro, in vivo, and in silico tests to establish why hydrogen sulfide production is increased in tumor tissue.

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“…In fact, if the system given in Eqs. (14) and (15) is 217 evolved forward until w Ii (c i (t)) becomes infeasible, the time at which the system 218 becomes infeasible is the time at which we have some (w Ii ) j = 0 for j ∈ I i . Thus, we 219 need to resolve Eq.…”

mentioning

confidence: 99%

Minimizing the number of optimizations for efficient community dynamic flux balance analysis

Brunner

Chia

2020

Preprint

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Dynamic flux balance analysis uses a quasi-steady state assumption to calculate an organism's metabolic activity at each time-step of a dynamic simulation, using the well-know technique of flux balance analysis. For microbial communities, this calculation is especially costly and involves solving a linear constrained optimization problem for each member of the community at each time step. However, this is unnecessary and inefficient, as prior solutions can be used to inform future time steps.Here, we show that a basis for the space of internal fluxes can be chosen for each microbe in a community and this basis can be used to simulate forward by solving a relatively inexpensive system of linear equations at most time steps, instead of the full optimization problem. Using our method, we can use this solution as long as the resulting metabolic activity remains within the optimization problem's constraints (i.e. the solution remains feasible). As the solution becomes infeasible, it first becomes a feasible but degenerate solution to the optimization problem, and we can solve a different but related optimization problem to choose an appropriate basis to continue forward simulation. We show using an eight species community that this is an efficient and robust method for computing dynamic flux balance analysis simulations, and so is capable of simulating communities of organisms. We demonstrate that the method gives an approximately 85% speed-up per organism over the standard and widely used method. Our method has been implemented in the Python language and source code is available at https://github.com/jdbrunner/surfin_fba and in the Python Package Index (PyPI) as surfinFBA. Author summary.The standard method in the field for dynamic flux balance analysis carries a prohibitively high computational cost because it requires solving a linear optimization problem at each time-step. We have developed a novel method for producing solutions to this dynamical system which greatly reduces the number of optimization problems that must be solved. We prove mathematically that we can solve the optimization problem once and simulate the system forward as an ordinary differential equation for some time interval, and solutions to this ODE provide solutions to the optimization problem. Eventually, the system reaches an easily checkable condition which implies that another optimization problem must be solved. We compare our method with the classical method to validate that it provides equivalent solutions in much lower computational time.March 7, 2020 1/18 Introduction. 1 Microbial communities and human health. 2 The makeup of microbial communities is often complex, dynamic, and hard to predict. 3 However, microbial community structure has a profound effect on human health and 4 disease [1-7]. These two facts have lead to significant interest in mathematical models 5 which can predict relative abundances among microbes in a community. Recently, 6 advances in genetic sequencing have allowed the creation of genome-scale models 7 (GEMs) wh...

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MMinte: an application for predicting metabolic interactions among the microbial species in a community

Cited by 68 publications

References 61 publications

Global-scale structure of the eelgrass microbiome

Global-scale structure of the eelgrass microbiome

Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer

Minimizing the number of optimizations for efficient community dynamic flux balance analysis

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