Synthetic biology identifies the minimal gene set required for paclitaxel biosynthesis in a plant chassis

The diterpenoid paclitaxel (Taxol) is a chemotherapy medication widely used as a first-line treatment against several types of solid cancers. The supply of paclitaxel from natural sources is limited. However, missing knowledge about the genes involved in several specific metabolic steps of paclitaxel biosynthesis has rendered it difficult to engineer the full pathway. In this study, we used a combination of transcriptomics, cell biology, metabolomics, and pathway reconstitution to identify the complete gene set required for the heterologous production of paclitaxel. We identified the missing steps from the current model of paclitaxel biosynthesis and confirmed the activity of most of the missing enzymes via heterologous expression in Nicotiana benthamiana. Notably, we identified a new C4β-C20 epoxidase that could overcome the first bottleneck of metabolic engineering. We used both previously characterized and newly identified oxomutases/epoxidases, taxane 1β-hydroxylase, taxane 9α-hydroxylase, taxane 9α-dioxygenase, and phenylalanine-CoA ligase, to successfully biosynthesize the key intermediate baccatin III and to convert baccatin III into paclitaxel in N. benthamiana. In combination, these approaches establish a metabolic route to taxoid biosynthesis and provide insights into the unique chemistry that plants use to generate complex bioactive metabolites

Chloroplast translational regulation uncovers nonessential photosynthesis genes as key players in plant cold acclimation

Plants evolved efficient multifaceted acclimation strategies to cope with low temperatures. Chloroplasts respond to temperature stimuli and participate in temperature sensing and acclimation. However, very little is known about the involvement of chloroplast genes and their expression in plant chilling tolerance. Here we systematically investigated cold acclimation in tobacco seedlings over 2 days of exposure to low temperatures by examining responses in chloroplast genome copy number, transcript accumulation and translation, photosynthesis, cell physiology, and metabolism. Our time-resolved genome-wide investigation of chloroplast gene expression revealed substantial cold-induced translational regulation at both the initiation and elongation levels, in the virtual absence of changes at the transcript level. These cold-triggered dynamics in chloroplast translation are widely distinct from previously described high light-induced effects. Analysis of the gene set responding significantly to the cold stimulus suggested nonessential plastid-encoded subunits of photosynthetic protein complexes as novel players in plant cold acclimation. Functional characterization of one of these cold-responsive chloroplast genes by reverse genetics demonstrated that the encoded protein, the small cytochrome b6f complex subunit PetL, crucially contributes to photosynthetic cold acclimation. Together, our results uncover an important, previously underappreciated role of chloroplast translational regulation in plant cold acclimation

Molecular regulation of fruit ripening

Peer reviewedFruit ripening is a highly coordinated developmental process that coincides with seed maturation. The ripening process is regulated by thousands of genes that control progressive softening and/or lignification of pericarp layers, accumulation of sugars, acids, pigments, and release of volatiles. Key to crop improvement is a deeper understanding of the processes underlying fruit ripening. In tomato, mutations blocking the transition to ripe fruits have provided insights into the role of ethylene and its associated molecular networks involved in the control of ripening. However, the role of other plant hormones is still poorly understood. In this review, we describe how plant hormones, transcription factors, and epigenetic changes are intimately related to provide a tight control of the ripening process. Recent findings from comparative genomics and system biology approaches are discussed.This work was supported in part by grants from the Max-Planck-Gesellschaft (to Sonia Osorio and Alisdair R. Fernie), and by Ministerio de Ciencia e Innovación, Spain (Ramón and Cajal contract). Federico Scossa acknowledges the support of CRA-Young Investigator Program.Peer Reviewe

Variability of Metabolite Levels Is Linked to Differential Metabolic Pathways in Arabidopsis's Responses to Abiotic Stresses

<div><p>Constraint-based approaches have been used for integrating data in large-scale metabolic networks to obtain insights into metabolism of various organisms. Due to the underlying steady-state assumption, these approaches are usually not suited for making predictions about metabolite levels. Here, we ask whether we can make inferences about the variability of metabolite levels from a constraint-based analysis based on the integration of transcriptomics data. To this end, we analyze time-resolved transcriptomics and metabolomics data from <i>Arabidopsis thaliana</i> under a set of eight different light and temperature conditions. In a previous study, the gene expression data have already been integrated in a genome-scale metabolic network to predict pathways, termed modulators and sustainers, which are differentially regulated with respect to a biochemically meaningful data-driven null model. Here, we present a follow-up analysis which bridges the gap between flux- and metabolite-centric methods. One of our main findings demonstrates that under certain environmental conditions, the levels of metabolites acting as substrates in modulators or sustainers show significantly lower temporal variations with respect to the remaining measured metabolites. This observation is discussed within the context of a systems-view of plasticity and robustness of metabolite contents and pathway fluxes. Our study paves the way for investigating the existence of similar principles in other species for which both genome-scale networks and high-throughput metabolomics data of high quality are becoming increasingly available.</p></div

Significance test for the coefficients of variation (CVs) for different groups of <i>metabolites</i>, or <i>products</i>, or <i>substrates</i> across the considered environmental conditions.

<p>Given are <i>Wilcoxon</i>-test -values from testing for significantly lower CVs for <b><i>metabolites</i></b> or <b><i>products</i></b> or <b><i>substrates</i></b> in the given six comparisons, respectively. A denotes that the sample size was too small for a statistic test (0 or 1 product in the respective group). The table entries in bold mark the significant comparisons at level 0.05.</p

Schematic representation of the analysis framework.

<p>Transcriptomics and metabolomics data capturing <i>Arabidopsis thaliana</i>'s temporal response to eight different environmental conditions (combinations of different light and/or temperature regimes) are collected for a time-series of 24 hours. The transcriptomic data are used to constrain flux boundaries of the respective reactions in a large-scale network by assuming a correlation between the transcript abundance and the upper flux boundary through the respective enzyme-catalyzed reaction. Based on a model with randomized flux boundaries (null model), pathways are classified as differential for a given condition if they exhibit an absolute 2. Differentially up-regulated (down-regulated) pathways are termed sustainers (modulators) of the metabolic state, respectively. Independently from this categorization, the temporal variation of the metabolite profiles was determined. Under certain conditions, substrates in the differential pathways exhibit a significantly lower temporal variation with respect to other groups of metabolites.</p

Time-series of the metabolites acting as substrates in differential metabolic functions.

<p>Profiles of the relative metabolite content of those measured metabolites that act as substrates in a pathway classified as differential with respect to the null model under the respective condition. The horizontal line indicates time point 60-organization and transition into a new metabolic state.</p

Histogram of temporal coefficient of variation for metabolites.

<p>Distribution of CVs over all measured metabolites (green) and all metabolites identified as substrates in a differentially behaving functions for the respective conditions (red). The plot summarizes the distributions over all eight considered conditions.</p