Coupling S-adenosylmethionine–dependent methylation to growth: Design and uses

Luo, Haiyun; Hansen, Anne Sofie Lærke; Yang, Lei; Schneider, Konstantin; Kristensen, Mette; Christensen, Ulla; Christensen, Hanne Bjerre; Du, Bin; Özdemir, Emre; Feist, Adam M.; Keasling, Jay D.; Jensen, Michael K.; Herrgård, Markus J.; Palsson, Bernhard Ø.

doi:10.1371/journal.pbio.2007050

Cited by 48 publications

(53 citation statements)

References 31 publications

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“…To validate the correlation between biosensor reporter gene output and tryptophan production, we quantified extracellular tryptophan concentrations by HPLC 74 . Supernatants of cultivated strains were separated from the culture broth using AcroPrep Advance 96-Well Filter Plates (Pall Corporation) and centrifugation (5 min at 2200 × g ) following 24 h of cultivation in synthetic dropout medium without tryptophan and histidine.…”

Section: Methodsmentioning

confidence: 99%

Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism

et al. 2020

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Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms. From a single data-generation cycle, this enables successful forward engineering of complex aromatic amino acid metabolism in yeast, with the best machine learning-guided design recommendations improving tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training. Thus, this study highlights the power of combining mechanistic and machine learning models to effectively direct metabolic engineering efforts.

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Section: Methodsmentioning

confidence: 99%

Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism

et al. 2020

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“…A main source of difficulty lies in the conflict between fast, robust strain growth and the fitness-counterproductive repurposing of cellular resources to produce large amounts of a desirable compound. Nevertheless, ALE studies with proper experimental design can be used to overcome this conflict and optimize metabolite production and increase titer (Basso et al, 2011;Charusanti et al, 2012;Fong et al, 2005a;Fu et al, 2013;Grabar et al, 2006;Jiang et al, 2013;Lee et al, 2016Lee et al, , 2014Lu et al, 2012;Luo et al, 2019;Mahr et al, 2015;Otero et al, 2013;Pontrelli et al, 2018;Reyes et al, 2014;Royce et al, 2015;Shen et al, 2012;Smith and Liao, 2011;Vilela et al, 2015;Wang et al, 2012;Wisselink et al, 2007;Zhang et al, 2007;Zhao et al, 2013;Zhou et al, 2005Zhou et al, , 2003.…”

Section: Product Titer/yield Optimizationmentioning

confidence: 99%

“…Further, physiological characterization of evolved strains pointed to additional knockouts to decrease byproduct formation, ultimately yielding strains with even greater chemical yields. Properly designed growth-coupling strategies can even see utility across diverse organisms and enzyme types (Jensen et al, 2019), as exemplified by recent work coupling SAM-dependent methylation to growth (Luo et al, 2019). By tying production of the essential amino acid cysteine to the activity of methyltransferases, Luo et al were able to use ALE to select for both E. coli and S. cerevisiae strains with mutations providing 2-fold increases in heterologous methyltransferase activity.…”

Section: Product Titer/yield Optimizationmentioning

confidence: 99%

The emergence of adaptive laboratory evolution as an efficient tool for biological discovery and industrial biotechnology

Sandberg

Salazar

Weng

et al. 2019

Metabolic Engineering

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“…The benefit of folic acid in the central nervous system is attributed to its effect on homocysteine [ 8 – 10 ]. Folic acid can markedly increase the serum S-adenosylmethionine (SAM), which is a key MS that catalyzes methylation reactions in cells [ 4 , 44 ], and thereby declines the accumulation of homocysteine and mediates cytotoxicity, DNA damage and neurodegenerative disorders.…”

Section: Discussionmentioning

confidence: 99%

Novel genes associated with folic acid-mediated metabolism in mouse: A bioinformatics study

Zhao

Zou

2020

PLoS ONE

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Folic acid plays an essential role in the central nervous system and cancer. This study aimed to screen genes related to folic acid metabolism. Datasets (GSE80587, GSE65267 and GSE116299) correlated to folic acid were screened in the Gene Expression Omnibus. Weighed gene co-expression network analysis was performed to identify modules associated with sample traits of folic acid and organs (brain, prostate and kidney). Functional enrichment analysis was performed for the eigengenes in modules that were significantly correlated with sample traits. Accordingly, the hub genes and key nodes in the modules were identified using the protein interaction network. A total of 17,252 genes in three datasets were identified. One module, which included 97 genes that were highly correlated with sample traits (including folic acid treatment [cor =-0.57, P = 3e-04] and kidney [cor =-0.68, p = 4e-06]), was screened out. Hub genes, including tetratricopeptide repeat protein 38 (Ttc38) and miR-185, as well as those (including Sema3A, Insl3, Dll1, Msh4 and Snai1) associated with "neuropilin binding", "regulation of reproductive process" and "vitamin D metabolic process", were identified. Genes, including Ttc38, Sema3A, Insl3, Dll1, Msh4 and Snai1, were the novel factors that may be associated with the development of the kidneys and related to folic acid treatment.

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Coupling S-adenosylmethionine–dependent methylation to growth: Design and uses

Cited by 48 publications

References 31 publications

Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism

Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism

The emergence of adaptive laboratory evolution as an efficient tool for biological discovery and industrial biotechnology

Novel genes associated with folic acid-mediated metabolism in mouse: A bioinformatics study

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