The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes

Seaver, Samuel M. D.; Liu, Filipe; Zhang, Qizhi; Jeffryes, James G.; Faria, José P.; Edirisinghe, Janaka N.; Mundy, Michael B.; Chia, Nicholas; Noor, Elad; Beber, Moritz Emanuel; Best, Aaron A.; DeJongh, Matthew; Kimbrel, Jeffrey A.; D’haeseleer, Patrik; McCorkle, S.; Bolton, Jay R.; Pearson, Erik; Canon, Shane; Wood‐Charlson, Elisha M.; Cottingham, Robert W.; Arkin, Adam P.; Henry, Christopher S.

doi:10.1093/nar/gkaa746

Cited by 151 publications

(119 citation statements)

References 69 publications

(37 reference statements)

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“…Our approach of combining surrogate traits with the selection niche makes such traits amenable for adaptive evolution. As genome-scale metabolic models are becoming easier to reconstruct (44)(45)(46)(47), our approach can be applied to any organism amenable for experimental evolution.…”

Section: Discussionmentioning

confidence: 99%

Predictive evolution of metabolic phenotypes using model-designed selection niches

Jouhten

Konstantinidis

Pereira

et al. 2021

Preprint

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Traits lacking fitness benefit cannot be directly selected for under Darwinian evolution. Thus, features such as metabolite secretion are currently inaccessible to adaptive laboratory evolution. Here, we utilize environment-dependency of trait correlations to enable Darwinian selection of fitness-neutral or costly traits. We use metabolic models to design selection niches and to identify surrogate traits that are genetically correlated with cell fitness in the selection niche but coupled to the desired trait in the target niche. Adaptive evolution in the selection niche and subsequent return to the target niche is thereby predicted to enhance the desired trait. We experimentally validate the theory by evolving Saccharomyces cerevisiae for increased secretion of aroma compounds in wine fermentation. Genomic, transcriptomic, and proteomic changes in the evolved strains confirmed the predicted flux re-routing to aroma biosynthesis. The use of model-designed selection niches facilitates the predictive evolution of fitness-costly traits for ecological and biotechnological applications.

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Section: Discussionmentioning

confidence: 99%

Predictive evolution of metabolic phenotypes using model-designed selection niches

Jouhten

Konstantinidis

Pereira

et al. 2021

Preprint

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“…It needs to be noted, that since we have retrieved the biochemistry database from ModelSEED for the development of gapseq, the ModelSEED database has been comprehensively improved and extended [95]. During the development of gapseq we have transferred corrections made by the ModelSEED community also to our database.…”

Section: Biochemistry Database Curation and Construction Of Universalmentioning

confidence: 99%

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

2021

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Genome-scale metabolic models of microorganisms are powerful frameworks to predict phenotypes from an organism’s genotype. While manual reconstructions are laborious, automated reconstructions often fail to recapitulate known metabolic processes. Here we present (https://github.com/jotech/gapseq), a new tool to predict metabolic pathways and automatically reconstruct microbial metabolic models using a curated reaction database and a novel gap-filling algorithm. On the basis of scientific literature and experimental data for 14,931 bacterial phenotypes, we demonstrate that gapseq outperforms state-of-the-art tools in predicting enzyme activity, carbon source utilisation, fermentation products, and metabolic interactions within microbial communities.

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“…Databases exist for published genome-scale reconstructions such as BiGG (132) and ModelSEED (133). Significant progress has been made in cataloging reconstructions for the human gut microbiome and human body at large (129,134,135).…”

Section: Automated Genome-scale Reconstructions: Scaffolds For Genome-scale Modelsmentioning

confidence: 99%

Integrating Systems and Synthetic Biology to Understand and Engineer Microbiomes

Leggieri

Liu

Hayes

et al. 2021

Annu. Rev. Biomed. Eng.

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Microbiomes are complex and ubiquitous networks of microorganisms whose seemingly limitless chemical transformations could be harnessed to benefit agriculture, medicine, and biotechnology. The spatial and temporal changes in microbiome composition and function are influenced by a multitude of molecular and ecological factors. This complexity yields both versatility and challenges in designing synthetic microbiomes and perturbing natural microbiomes in controlled, predictable ways. In this review, we describe factors that give rise to emergent spatial and temporal microbiome properties and the meta-omics and computational modeling tools that can be used to understand microbiomes at the cellular and system levels. We also describe strategies for designing and engineering microbiomes to enhance or build novel functions. Throughout the review,we discuss key knowledge and technology gaps for elucidating the networks and deciphering key control points for microbiome engineering, and highlight examples where multiple omics and modeling approaches can be integrated to address these gaps. Expected final online publication date for the Annual Review of Biomedical Engineering, Volume 23 is June 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes

Cited by 151 publications

References 69 publications

Predictive evolution of metabolic phenotypes using model-designed selection niches

Predictive evolution of metabolic phenotypes using model-designed selection niches

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

Integrating Systems and Synthetic Biology to Understand and Engineer Microbiomes

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