Connecting extracellular metabolomic measurements to intracellular flux states in yeast

Mo, Monica L.; Palsson, Bernhard Ø.; Herrgård, Markus J.

doi:10.1186/1752-0509-3-37

Cited by 401 publications

(467 citation statements)

References 57 publications

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“…Sampling included filtering media for high-performance liquid chromatography (HPLC) to quantify extracellular metabolites, ethyl acetate extraction followed by gas chromatography-mass spectrometry (GC-MS) to quantify fatty acid products, and methanol/chloroform extraction followed by liquid chromatography-mass spectrometry (LC-MS/MS) to analyze 13 C labeling in metabolites. These 13 C labeling data were used to constrain the S. cerevisiae genome-scale model iMM904 (Mo et al, 2009) using two-scale 13 C Metabolic Flux Analysis (García Martín et al, 2015;Ghosh et al, 2016) with the open-source, python-based JBEI Quantitative Metabolic Modeling library (https://github.com/JBEI/jqmm) to model the metabolic flux distribution.…”

Section: Metabolic Flux Analysis Using 13 C-labeled Glucosementioning

confidence: 99%

“…To model the metabolic flux distribution in yL401, we measured metabolite levels and 13 C labeling patterns and used the JBEI Quantitative Metabolic Modeling library (García Martín et al, 2015) to model flux through genome-scale model iMM904 (Mo et al, 2009), adding fatty acyl thioesterase reactions (EC number 3.1.2.2). The reconstruction shows carbon from glucose mostly following glycolysis, then the pyruvate dehydrogenase (PDH) bypass from pyruvate to acetaldehyde, and lastly to ethanol (Fig.…”

Section: Microscopymentioning

confidence: 99%

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Engineering high-level production of fatty alcohols by Saccharomyces cerevisiae from lignocellulosic feedstocks

d’Espaux

Ghosh

Runguphan

et al. 2017

Metabolic Engineering

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A B S T R A C TFatty alcohols in the C12-C18 range are used in personal care products, lubricants, and potentially biofuels. These compounds can be produced from the fatty acid pathway by a fatty acid reductase (FAR), yet yields from the preferred industrial host Saccharomyces cerevisiae remain under 2% of the theoretical maximum from glucose. Here we improved titer and yield of fatty alcohols using an approach involving quantitative analysis of protein levels and metabolic flux, engineering enzyme level and localization, pull-push-block engineering of carbon flux, and cofactor balancing. We compared four heterologous FARs, finding highest activity and endoplasmic reticulum localization from a Mus musculus FAR. After screening an additional twenty-one single-gene edits, we identified increasing FAR expression; deleting competing reactions encoded by DGA1, HFD1, and ADH6; overexpressing a mutant acetyl-CoA carboxylase; limiting NADPH and carbon usage by the glutamate dehydrogenase encoded by GDH1; and overexpressing the Δ9-desaturase encoded by OLE1 as successful strategies to improve titer. Our final strain produced 1.2 g/L fatty alcohols in shake flasks, and 6.0 g/L in fed-batch fermentation, corresponding to~20% of the maximum theoretical yield from glucose, the highest titers and yields reported to date in S. cerevisiae. We further demonstrate high-level production from lignocellulosic feedstocks derived from ionic-liquid treated switchgrass and sorghum, reaching 0.7 g/L in shake flasks. Altogether, our work represents progress towards efficient and renewable microbial production of fatty acidderived products.

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Section: Metabolic Flux Analysis Using 13 C-labeled Glucosementioning

confidence: 99%

Section: Microscopymentioning

confidence: 99%

Engineering high-level production of fatty alcohols by Saccharomyces cerevisiae from lignocellulosic feedstocks

d’Espaux

Ghosh

Runguphan

et al. 2017

Metabolic Engineering

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“…As such, EDGE scores are computed only for genes that take part in the metabolic model, and their predictive power is dependent on the model's quality. Manually curated GSMMs, which have demonstrated their predictive power, exist for many model organisms, including industrial microorganisms (28,54) and human (55), and undergo constant improvement; plant models have been recently published as well (56). The Model SEED platform was the first to generate GSMMs automatically (57).…”

Section: Discussionmentioning

confidence: 99%

Computational evaluation of cellular metabolic costs successfully predicts genes whose expression is deleterious

Wagner¹,

Zarecki²,

Reshef

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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Gene suppression and overexpression are both fundamental tools in linking genotype to phenotype in model organisms. Computational methods have proven invaluable in studying and predicting the deleterious effects of gene deletions, and yet parallel computational methods for overexpression are still lacking. Here, we present Expression-Dependent Gene Effects (EDGE), an in silico method that can predict the deleterious effects resulting from overexpression of either native or foreign metabolic genes. We first test and validate EDGE's predictive power in bacteria through a combination of small-scale growth experiments that we performed and analysis of extant large-scale datasets. Second, a broad cross-species analysis, ranging from microorganisms to multiple plant and human tissues, shows that genes that EDGE predicts to be deleterious when overexpressed are indeed typically downregulated. This reflects a universal selection force keeping the expression of potentially deleterious genes in check. Third, EDGEbased analysis shows that cancer genetic reprogramming specifically suppresses genes whose overexpression impedes proliferation. The magnitude of this suppression is large enough to enable an almost perfect distinction between normal and cancerous tissues based solely on EDGE results. We expect EDGE to advance our understanding of human pathologies associated with up-regulation of particular transcripts and to facilitate the utilization of gene overexpression in metabolic engineering.systems metabolic engineering | metabolic modeling | constraint-based modeling | flux balance analysis | horizontal gene transfer

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“…In the human muscle ageing data sets in which the media is unknown, a rich media in which all the uptakes are available was simulated. In addition, for modelling E. coli's metabolism we have used the iAF1260 model 55 , for yeast we have used the iMM904 model 56 (ref. 14).…”

Section: Discussionmentioning

confidence: 99%

“…The metabolic models used in this study comprises a few thousands of reactions 12,55,56 . Notably, a subset of the reactions in each model (20-30%) is defined as dead end (that is, cannot carry metabolic flux because of the incompleteness of the model), and is therefore removed from the set of allowed knockouts.…”

Section: Discussionmentioning

confidence: 99%

Model-based identification of drug targets that revert disrupted metabolism and its application to ageing

et al. 2013

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The growing availability of 'omics' data and high-quality in silico genome-scale metabolic models (GSMMs) provide a golden opportunity for the systematic identification of new metabolic drug targets. Extant GSMM-based methods aim at identifying drug targets that would kill the target cell, focusing on antibiotics or cancer treatments. However, normal human metabolism is altered in many diseases and the therapeutic goal is fundamentally different-to retrieve the healthy state. Here we present a generic metabolic transformation algorithm (MTA) addressing this issue. First, the prediction accuracy of MTA is comprehensively validated using data sets of known perturbations. Second, two predicted yeast lifespan-extending genes, GRE3 and ADH2, are experimentally validated, together with their associated hormetic effect. Third, we show that MTA predicts new drug targets for human ageing that are enriched with orthologs of known lifespan-extending genes and with genes downregulated following caloric restriction mimetic treatments. MTA offers a promising new approach for the identification of drug targets in metabolically related disorders.

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Connecting extracellular metabolomic measurements to intracellular flux states in yeast

Cited by 401 publications

References 57 publications

Engineering high-level production of fatty alcohols by Saccharomyces cerevisiae from lignocellulosic feedstocks

Engineering high-level production of fatty alcohols by Saccharomyces cerevisiae from lignocellulosic feedstocks

Computational evaluation of cellular metabolic costs successfully predicts genes whose expression is deleterious

Model-based identification of drug targets that revert disrupted metabolism and its application to ageing

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