Engineering Escherichia coli towards de novo production of gatekeeper (2S)-flavanones: naringenin, pinocembrin, eriodictyol and homoeriodictyol.

Natural plant-based flavonoids have drawn significant attention as dietary supplements due to their potential health benefits, including anti-cancer, anti-oxidant and anti-asthmatic activities. Naringenin, pinocembrin, eriodictyol and homoeriodictyol are classified as (2S)-flavanones, an important sub-group of naturally occurring flavonoids, with wide-reaching applications in human health and nutrition. These four compounds occupy a central position as branch point intermediates towards a broad spectrum of naturally occurring flavonoids. Here, we report the development of Escherichia coli production chassis for each of these key gatekeeper flavonoids. Selection of key enzymes, genetic construct design and the optimization of process conditions resulted in the highest reported titers for naringenin (484 mg/l), improved production of pinocembrin (198 mg/l) and eriodictyol (55 mg/l from caffeic acid), and provided the first example of in vivo production of homoeriodictyol directly from glycerol (17 mg/l). This work provides a springboard for future production of diverse downstream natural and non-natural flavonoid targets.

Hepatotoxicity prediction by systems biology modeling of disturbed metabolic pathways using gene expression data.

The present study applies a systems biology approach for the in silico predictive modeling of drug toxicity on the basis of high-quality preclinical drug toxicity data with the aim of increasing the mechanistic understanding of toxic effects of compounds at different levels (pathway, cell, tissue, organ). The model development was carried out using 77 compounds for which gene expression data for treated primary human hepatocytes is available in the LINCS database and for which rodent in vivo hepatotoxicity information is available in the eTOX database. The data from LINCS were used to determine the type and number of pathways disturbed by each compound and to estimate the extent of disturbance (network perturbation elasticity), and were used to analyze the correspondence with the in vivo information from eTOX. Predictive models were developed through this integrative analysis, and their specificity and sensitivity were assessed. The quality of the predictions was determined on the basis of the area under the curve (AUC) of plots of true positive vs. false positive rates (ROC curves). The ROC AUC reached values of up to 0.9 (out of 1.0) for some hepatotoxicity endpoints. Moreover, the most frequently disturbed metabolic pathways were determined across the studied toxicants. They included, e.g., mitochondrial beta-oxidation of fatty acids and amino acid metabolism. The process was exemplified by successful predictions on various statins. In conclusion, an entirely new approach linking gene expression alterations to the prediction of complex organ toxicity was developed and evaluated.

Compound toxicity screening and structure-activity relationship modeling in Escherichia coli.

Synthetic biology and metabolic engineering are used to develop new strategies for producing valuable compounds ranging from therapeutics to biofuels in engineered microorganisms. When developing methods for high-titer production cells, toxicity is an important element to consider. Indeed the production rate can be limited due to toxic intermediates or accumulation of byproducts of the heterologous biosynthetic pathway of interest. Conversely, highly toxic molecules are desired when designing antimicrobials. Compound toxicity in bacteria plays a major role in metabolic engineering as well as in the development of new antibacterial agents. Here, we screened a diversified chemical library of 166 compounds for toxicity in Escherichia coli. The dataset was built using a clustering algorithm maximizing the chemical diversity in the library. The resulting assay data was used to develop a toxicity predictor that we used to assess the toxicity of metabolites throughout the metabolome. This new tool for predicting toxicity can thus be used for fine-tuning heterologous expression and can be integrated in a computational-framework for metabolic pathway design. Many structure-activity relationship tools have been developed for toxicology studies in eukaryotes [Valerio (2009), Toxicol Appl Pharmacol, 241(3): 356-370], however, to the best of our knowledge we present here the first E. coli toxicity prediction web server based on QSAR models (EcoliTox server: http://www.issb.genopole.fr/∼faulon/EcoliTox.php).