Enhancing Microbiome Research through Genome-Scale Metabolic Modeling

Ankrah, Nana Y. D.; Bernstein, David B.; Biggs, Matthew; Carey, Maureen A.; Engevik, Melinda A.; García-Jiménez, Beatriz; Pacheco, Alan R.; Sulheim, Snorre; Medlock, Gregory L.

doi:10.1128/msystems.00599-21

Cited by 23 publications

(22 citation statements)

References 62 publications

(60 reference statements)

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“…Such an approach is fundamentally superior over 16S-based genome computation, because the genomes are derived directly from the sample, without referring to a database, and allow an authentic look at the metabolic activities in native communities ( 57 ). Further improvement of model predictions might be gained from going beyond genomic and metagenomic data and considering additional multiomics data, i.e., metatranscriptomics, metaproteomics, and/or metabolomics ( 31 ). The integration of these additional layers of information into the model will further constrain the solution space and direct simulations toward feasible solutions.…”

Section: Discussionmentioning

confidence: 99%

“…Key factors not encountered by the models included soil conditions that are not directly translated into nutritional content, such as pH and temperature, and nonmetabolic interactions such as quorum-sensing formation and toxin-mediated inhibition. Finally, scaling of the scope of simulations to communities and ecosystems poses numerous conceptual and technical uncertainties ( 31 ). Considering their scopes and limitations, the metabolic models can be viewed as tools for generating testable predictions through the contextualization of big data.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, metabolic modeling has been increasingly used for exploring and improving selected metabolic performances of microbial species ( 26 , 27 ). Modeling is based on in silico representation of genomic data obtained from field samples, supporting the conductance of multiple simulations leading to organism- and community-level phenotyping and the subsequent development of metabolic engineering strategies for biostimulation ( 28 – 31 ). Simulations can be designed to screen for the stimulation potential of metabolites for particular catabolic pathways, microbial species, and combinations of both.…”

Section: Introductionmentioning

confidence: 99%

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“…By integrating a linear-based programming framework, the GSMMs can be used to computationally determine enzyme rates under various environmental conditions. Furthermore, the GSMMs not only allow in vitro prediction of the effects of gene deletion, gene overexpression, or underexpression but can also identify metabolic targets to reduce by-product formation, and combine multi-omics data types in a computational framework [13,14].…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of the Genome-Scale Metabolic Model of Saccharopolyspora erythraea and Its Application in the Overproduction of Erythromycin

et al. 2022

Metabolites

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Saccharopolyspora erythraea is considered to be an effective host for erythromycin. However, little is known about the regulation in terms of its metabolism. To develop an accurate model-driven strategy for the efficient production of erythromycin, a genome-scale metabolic model (iJL1426) was reconstructed for the industrial strain. The final model included 1426 genes, 1858 reactions, and 1687 metabolites. The accurate rates of the growth predictions for the 27 carbon and 31 nitrogen sources available were 92.6% and 100%, respectively. Moreover, the simulation results were consistent with the physiological observation and 13C metabolic flux analysis obtained from the experimental data. Furthermore, by comparing the single knockout targets with earlier published results, four genes coincided within the range of successful knockouts. Finally, iJL1426 was used to guide the optimal addition strategy of n-propanol during industrial erythromycin fermentation to demonstrate its ability. The experimental results showed that the highest erythromycin titer was 1442.8 μg/mL at an n-propanol supplementation rate of 0.05 g/L/h, which was 45.0% higher than that without n-propanol supplementation, and the erythromycin-specific synthesis rate was also increased by 30.3%. Therefore, iJL1426 will lead to a better understanding of the metabolic capabilities and, thus, is helpful in a systematic metabolic engineering approach.

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“…These approaches are appealing since they rely solely on genomic information. However, their performance depends on the availability of well-annotated genomes, and they typically do not account for non-metabolic interaction modalities, such as the secretion of antibiotics or pH modifications 27 .…”

Section: Introductionmentioning

confidence: 99%

Interactions between culturable bacteria are predicted by individual species’ growth

Nestor

Toledano

Friedman

2022

Preprint

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Predicting interspecies interactions is a key challenge in microbial ecology, as such interactions shape the composition and functioning of microbial communities. However, predicting microbial interactions is challenging since they can vary considerably depending on species’ metabolic capabilities and environmental conditions. Here, we employ machine learning models to predict pairwise interactions between culturable bacteria based on their phylogeny, monoculture growth capabilities, and interactions with other species. We trained our models on one of the largest available pairwise interactions dataset containing over 7500 interactions between 20 species from 2 taxonomic groups that were cocultured in 40 different carbon environments. Our models accurately predicted both the sign (accuracy of 88%) and the strength of effects (R2 of 0.87) species had on each other’s growth. Encouragingly, predictions with comparable accuracy could be made even when not relying on information about interactions with other species, which are often hard to measure. However, species’ monoculture growth was essential to the model, as predictions based solely on species’ phylogeny and inferred metabolic capabilities were significantly less accurate. These results bring us a step closer to a predictive understanding of microbial communities, which is essential for engineering beneficial microbial consortia.

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Enhancing Microbiome Research through Genome-Scale Metabolic Modeling

Cited by 23 publications

References 62 publications

Modeling-Guided Amendments Lead to Enhanced Biodegradation in Soil

Modeling-Guided Amendments Lead to Enhanced Biodegradation in Soil

Reconstruction of the Genome-Scale Metabolic Model of Saccharopolyspora erythraea and Its Application in the Overproduction of Erythromycin

Interactions between culturable bacteria are predicted by individual species’ growth

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