SLIMEr: probing flexibility of lipid metabolism in yeast with an improved constraint-based modeling framework

Sánchez, Benjamín J.; Li, Feiran; Kerkhoven, Eduard J.; Nielsen, Jens

doi:10.1101/324863

Cited by 9 publications

(8 citation statements)

References 32 publications

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“…For our FBA simulations, we used the consensus genome-scale metabolic model of Saccharomyces cerevisiae , yeastGEM, version 8.3.0 [46]. The simulations were performed with the COBRApy python package [47], using yeastGEM definition of growth as the objective function to be maximized.…”

Section: Methodsmentioning

confidence: 99%

Effects of MCHM on yeast metabolism

Pupo

Gallagher

2019

PLoS ONE

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On January 2014 approximately 10,000 gallons of crude 4-Methylcyclohexanemethanol (MCHM) and propylene glycol phenol ether (PPH) were accidentally released into the Elk River, West Virginia, contaminating the tap water of around 300,000 residents. Crude MCHM is an industrial chemical used as flotation reagent to clean coal. At the time of the spill, MCHM's toxicological data were limited, an issue that has been addressed by different studies focused on understanding the immediate and long-term effects of MCHM on human health and the environment. Using S. cerevisiae as a model organism we study the effect of acute exposure to crude MCHM on metabolism. Yeasts were treated with MCHM 550 ppm in YPD for 30 minutes. Polar and lipid metabolites were extracted from cells by a chloroform-methanol-water mixture. The extracts were then analyzed by direct injection ESI-MS and by GC-MS. The metabolomics analysis was complemented with flux balance analysis simulations done with genome-scale metabolic network models (GSMNM) of MCHM treated vs non-treated control. We integrated the effect of MCHM on yeast gene expression from RNA-Seq data within these GSMNM. A total of 215 and 73 metabolites were identified by the ESI-MS and GC-MS procedures, respectively. From these 26 and 23 relevant metabolites were selected from ESI-MS and GC-MS respectively, for 49 unique compounds. MCHM induced amino acid accumulation, via its effects on amino acid metabolism, as well as a potential impairment of ribosome biogenesis. MCHM affects phospholipid biosynthesis, with a potential impact on the biophysical properties of yeast cellular membranes. The FBA simulations were able to reproduce the deleterious effect of MCHM on cellular growth and suggest that the effect of MCHM on ubiquinol:ferricytochrome c reductase reaction, caused by the under-expression of CYT1 gene, could be the driven force behind the observed effect on yeast metabolism and growth.

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Section: Methodsmentioning

confidence: 99%

Effects of MCHM on yeast metabolism

Pupo

Gallagher

2019

PLoS ONE

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“…The biological classification for the genes identified by the feature selection methods and SHAP was obtained with the PANTHER classification system (46). The KEGG pathway annotation (61) for GSMM reactions was obtained from a curated S. cerevisiae GSMM (62). The statistical enrichment tests on PANTHER were run with default parameters.…”

Section: Genome-scale Metabolic Modelingmentioning

confidence: 99%

A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth

Culley

Vijayakumar

Zampieri

et al. 2020

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

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Metabolic modeling and machine learning are key components in the emerging next generation of systems and synthetic biology tools, targeting the genotype–phenotype–environment relationship. Rather than being used in isolation, it is becoming clear that their value is maximized when they are combined. However, the potential of integrating these two frameworks for omic data augmentation and integration is largely unexplored. We propose, rigorously assess, and compare machine-learning–based data integration techniques, combining gene expression profiles with computationally generated metabolic flux data to predict yeast cell growth. To this end, we create strain-specific metabolic models for 1,143 Saccharomyces cerevisiae mutants and we test 27 machine-learning methods, incorporating state-of-the-art feature selection and multiview learning approaches. We propose a multiview neural network using fluxomic and transcriptomic data, showing that the former increases the predictive accuracy of the latter and reveals functional patterns that are not directly deducible from gene expression alone. We test the proposed neural network on a further 86 strains generated in a different experiment, therefore verifying its robustness to an additional independent dataset. Finally, we show that introducing mechanistic flux features improves the predictions also for knockout strains whose genes were not modeled in the metabolic reconstruction. Our results thus demonstrate that fusing experimental cues with in silico models, based on known biochemistry, can contribute with disjoint information toward biologically informed and interpretable machine learning. Overall, this study provides tools for understanding and manipulating complex phenotypes, increasing both the prediction accuracy and the extent of discernible mechanistic biological insights.

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“…Due its complexity the lipid metabolism in GEMs is typically represented by lumped reactions, which involve either an artificial average or the most dominant type of fatty acid. Adding special pseudo‐reactions based on data from lipid profiling and fatty acid methyl ester analysis could further help to improve the representation of the lipid metabolism …”

Section: Discussionmentioning

confidence: 99%

Microbial Methylotrophic Metabolism: Recent Metabolic Modeling Efforts and Their Applications In Industrial Biotechnology

Lieven

Sonnenschein

2018

Biotechnology Journal

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Developing methylotrophic bacteria into cell factories that meet the chemical demand of the future could be both economical and environmentally friendly. Methane is not only an abundant, low-cost resource but also a potent greenhouse gas, the capture of which could help to reduce greenhouse gas emissions. Rational strain design workflows rely on the availability of carefully combined knowledge often in the form of genome-scale metabolic models to construct high-producer organisms. In this review, the authors present the most recent genome-scale metabolic models in aerobic methylotrophy and their applications. Further, the authors present models for the study of anaerobic methanotrophy through reverse methanogenesis and suggest organisms that may be of interest for expanding one-carbon industrial biotechnology. Metabolic models of methylotrophs are scarce, yet they are important first steps toward rational strain-design in these organisms.

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SLIMEr: probing flexibility of lipid metabolism in yeast with an improved constraint-based modeling framework

Cited by 9 publications

References 32 publications

Effects of MCHM on yeast metabolism

Effects of MCHM on yeast metabolism

A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth

Microbial Methylotrophic Metabolism: Recent Metabolic Modeling Efforts and Their Applications In Industrial Biotechnology

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