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Combination of Plant Metabolic Modules Yields Synthetic Synergies.

ABSTRACT: The great potential of pharmacologically active secondary plant metabolites is often limited by low yield and availability of the producing plant. Chemical synthesis of these complex compounds is often too expensive. Plant cell fermentation offers an alternative strategy to overcome these limitations. However, production in batch cell cultures remains often inefficient. One reason might be the fact that different cell types have to interact for metabolite maturation, which is poorly mimicked in suspension cell lines. Using alkaloid metabolism of tobacco, we explore an alternative strategy, where the metabolic interactions of different cell types in a plant tissue are technically mimicked based on different plant-cell based metabolic modules. In this study, we simulate the interaction found between the nicotine secreting cells of the root and the nicotine-converting cells of the senescent leaf, generating the target compound nornicotine in the model cell line tobacco BY-2. When the nicotine demethylase NtomCYP82E4 was overexpressed in tobacco BY-2 cells, nornicotine synthesis was triggered, but only to a minor extent. However, we show here that we can improve the production of nornicotine in this cell line by feeding the precursor, nicotine. Engineering of another cell line overexpressing the key enzyme NtabMPO1 allows to stimulate accumulation and secretion of this precursor. We show that the nornicotine production of NtomCYP82E4 cells can be significantly stimulated by feeding conditioned medium from NtabMPO1 overexpressors without any negative effect on the physiology of the cells. Co-cultivation of NtomCYP82E4 with NtabMPO1 stimulated nornicotine accumulation even further, demonstrating that the physical presence of cells was superior to just feeding the conditioned medium collected from the same cells. These results provide a proof of concept that combination of different metabolic modules can improve the productivity for target compounds in plant cell fermentation.

SUBMITTER: Rajabi F

PROVIDER: S-EPMC5231347 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Combination of Plant Metabolic Modules Yields Synthetic Synergies.

Rajabi Fatemeh F Heene Ernst E Maisch Jan J Nick Peter P

PloS one 20170112 1

The great potential of pharmacologically active secondary plant metabolites is often limited by low yield and availability of the producing plant. Chemical synthesis of these complex compounds is often too expensive. Plant cell fermentation offers an alternative strategy to overcome these limitations. However, production in batch cell cultures remains often inefficient. One reason might be the fact that different cell types have to interact for metabolite maturation, which is poorly mimicked in ...[more]

PMID: 28081182

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Project description:BackgroundDrug combinations have the potential to improve efficacy while limiting toxicity. To robustly identify synergistic combinations, high-throughput screens using full dose-response surface are desirable but require an impractical number of data points. Screening of a sparse number of doses per drug allows to screen large numbers of drug pairs, but complicates statistical assessment of synergy. Furthermore, since the number of pairwise combinations grows with the square of the number of drugs, exploration of large screens necessitates advanced visualization tools.ResultsWe describe a statistical and visualization framework for the analysis of large-scale drug combination screens. We developed an approach suitable for datasets with large number of drugs pairs even if small number of data points are available per drug pair. We demonstrate our approach using a systematic screen of all possible pairs among 108 cancer drugs applied to melanoma cell lines. In this dataset only two dose-response data points per drug pair and two data points per single drug test were available. We used a Bliss-based linear model, effectively borrowing data from the drug pairs to obtain robust estimations of the singlet viabilities, consequently yielding better estimates of drug synergy. Our method improves data consistency across dosing thus likely reducing the number of false positives. The approach allows to compute p values accounting for standard errors of the modeled singlets and combination viabilities. We further develop a synergy specificity score that distinguishes specific synergies from those arising with promiscuous drugs. Finally, we developed a summarized interactive visualization in a web application, providing efficient access to any of the 439,000 data points in the combination matrix ( http://www.cmtlab.org:3000/combo_app.html ). The code of the analysis and the web application is available at https://github.com/arnaudmgh/synergy-screen .ConclusionsWe show that statistical modeling of single drug response from drug combination data can help determine significance of synergy and antagonism in drug combination screens with few data point per drug pair. We provide a web application for the rapid exploration of large combinatorial drug screen. All codes are available to the community, as a resource for further analysis of published data and for analysis of other drug screens.

Dataset Information

Combination of Plant Metabolic Modules Yields Synthetic Synergies.

Publications

Combination of Plant Metabolic Modules Yields Synthetic Synergies.

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