Dataset Information

Metabolic Dynamics in Escherichia coli-Based Cell-Free Systems

ABSTRACT: The field of metabolic engineering has yielded remarkable accomplishments in using cells to produce valuable molecules, and cell-free expression (CFE) systems have the potential to push the field even further. However, CFE systems still face some outstanding challenges, including endogenous metabolic activity that is poorly understood yet has a significant impact on CFE productivity. Here, we use metabolomics to characterize the temporal metabolic changes in CFE systems and their constituent components, including significant metabolic activity in central carbon and amino acid metabolism. We find that while changing the reaction starting state via lysate preincubation impacts protein production, it has a comparatively small impact on metabolic state. We also demonstrate that changes to lysate preparation have a larger effect on protein yield and temporal metabolic profiles, though general metabolic trends are conserved. Finally, while we improve protein production through targeted supplementation of metabolic enzymes, we show that the endogenous metabolic activity is fairly resilient to these enzymatic perturbations. Overall, this work highlights the robust nature of CFE reaction metabolism as well as the importance of understanding the complex interdependence of metabolites and proteins in CFE systems to guide optimization efforts.

INSTRUMENT(S): Tandem Gas Chromatography MS - positive, Tandem Gas Chromatography MS -

SUBMITTER: April Miguez

PROVIDER: MTBLS2630 | MetaboLights | 2023-02-22

REPOSITORIES: MetaboLights

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Publications

Metabolic Dynamics in <i>Escherichia coli</i>-Based Cell-Free Systems.

Miguez April M AM Zhang Yan Y Piorino Fernanda F Styczynski Mark P MP

ACS synthetic biology 20210903 9

The field of metabolic engineering has yielded remarkable accomplishments in using cells to produce valuable molecules, and cell-free expression (CFE) systems have the potential to push the field even further. However, CFE systems still face some outstanding challenges, including endogenous metabolic activity that is poorly understood yet has a significant impact on CFE productivity. Here, we use metabolomics to characterize the temporal metabolic changes in CFE systems and their constituent com ...[more]

PMID: 34478281

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Project description:The liver x receptor (LXR) and peroxisome proliferator-activated receptor gamma (PPARG) nuclear receptor transcription factors (TFs) are master regulators of energy homeostasis that impact tumor cell metabolism and proliferation. To better define the underlying molecular processes governing the genetic control of cellular growth in response to extracellular metabolic signals, we performed a comprehensive, genome-wide characterization of the temporal regulatory cascades mediated by LXR and PPARG signaling in HT29 colorectal cancer cells. For this analysis we applied a multi-tiered approach that incorporated cellular phenotypic assays, gene expression profiles, chromatin state dynamics and nuclear receptor binding patterns. By integrating these orthogonal datasets over time, we uncovered the regulatory architecture of LXR and PPARG activity. Both sets of nuclear receptors inhibited cell proliferation and increased cellular oxidative stress, validating results from independent studies. Despite this metabolic reprogramming, the gene regulatory network programs initiated by these nuclear receptors were widely distinct. PPARG generated a rapid and short-term response while maintaining a gene activator role. By contrast, LXR signaling was prolonged, with initial, predominantly activating functions altering to repressive gene regulatory activities at late time points through RNA polymerase II (RNAP2) promoter pausing. This exhaustive dataset illustrates the complexity of genome function and structure by elucidating how common phenotypic outcomes are genetically encoded through diverse transcriptional programs. Our results further provide a detailed, molecular framework that can be applied to better understanding how extracellular nutrients impact cancer cell physiology and highlight the importance of study designs that incorporate complementary genomic features in a dynamic manner. ChIP-seq and RNA-seq experiments were performed in HT29 cells using vehicle control (DMSO) or after drug treatment (GW3965 and rosiglitazone).

Dataset Information

Metabolic Dynamics in Escherichia coli-Based Cell-Free Systems

Publications

Metabolic Dynamics in <i>Escherichia coli</i>-Based Cell-Free Systems.

Similar Datasets

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