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Transient expression in Nicotiana benthamiana offers a robust platform for the rapid production of complex secondary metabolites. It has proven highly effective in helping identify genes associated with pathways responsible for synthesizing various valuable natural compounds. While this approach has seen considerable success, it has yet to be applied to uncovering genes involved in anthocyanin biosynthetic pathways. This is because only a single anthocyanin, delphinidin 3‐O‐rutinoside, can be produced in N. benthamiana by activation of anthocyanin biosynthesis using transcription factors. The production of other anthocyanins would necessitate the suppression of certain endogenous flavonoid biosynthesis genes while transiently expressing others. In this work, we present a series of tools for the reconstitution of anthocyanin biosynthetic pathways in N. benthamiana leaves. These tools include constructs for the expression or silencing of anthocyanin biosynthetic genes and a mutant N. benthamiana line generated using CRISPR. By infiltration of defined sets of constructs, the basic anthocyanins pelargonidin 3‐O‐glucoside, cyanidin 3‐O‐glucoside and delphinidin 3‐O‐glucoside could be obtained in high amounts in a few days. Additionally, co‐infiltration of supplementary pathway genes enabled the synthesis of more complex anthocyanins. These tools should be useful to identify genes involved in the biosynthesis of complex anthocyanins. They also make it possible to produce novel anthocyanins not found in nature. As an example, we reconstituted the pathway for biosynthesis of Arabidopsis anthocyanin A5, a cyanidin derivative and achieved the biosynthesis of the pelargonidin and delphinidin variants of A5, pelargonidin A5 and delphinidin A5.

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In biological discovery and engineering research, there is a need to spatially and/or temporally regulate transgene expression. However, the limited availability of promoter sequences that are uniquely active in specific tissue-types and/or at specific times often precludes co-expression of >multiple transgenes in precisely controlled developmental contexts. Here, we developed a system for use in rice that comprises synthetic designer transcription activator-like effectors (dTALEs) and cognate synthetic TALE-activated promoters (STAPs). The system allows multiple transgenes to be expressed from different STAPs, with the spatial and temporal context determined by a single promoter that drives expression of the dTALE. We show that two different systems—dTALE1-STAP1 and dTALE2-STAP2—can activate STAP-driven reporter gene expression in stable transgenic rice lines, with transgene transcript levels dependent on both dTALE and STAP sequence identities. The relative strength of individual STAP sequences is consistent between dTALE1 and dTALE2 systems but differs between cell-types, requiring empirical evaluation in each case. dTALE expression leads to off-target activation of endogenous genes but the number of genes affected is substantially less than the number impacted by the somaclonal variation that occurs during the regeneration of transformed plants. With the potential to design fully orthogonal dTALEs for any genome of interest, the dTALE-STAP system thus provides a powerful approach to fine-tune the expression of multiple transgenes, and to simultaneously introduce different synthetic circuits into distinct developmental contexts.

Agriculture is by far the biggest water consumer on our planet, accounting for 70 percent of all freshwater withdrawals. Climate change and a growing world population increase pressure on agriculture to use water more efficiently (‘more crop per drop’). Water‐use efficiency (WUE) and drought tolerance of crops are complex traits that are determined by many physiological processes whose interplay is not well understood. Here we describe a combinatorial engineering approach to optimize signaling networks involved in the control of stress tolerance. Screening a large population of combinatorially transformed plant lines, we identified a combination of calcium‐dependent protein kinase genes that confers enhanced drought stress tolerance and improved growth under water‐limiting conditions. Targeted introduction of this gene combination into plants increased plant survival under drought and enhanced growth under water‐limited conditions. Our work provides an efficient strategy for engineering complex signaling networks to improve plant performance under adverse environmental conditions, which does not depend on prior understanding of network function.

Hypericin is a molecule of high pharmaceutical importance that is synthesized and stored in dark glands (DGs) of St. John's wort (Hypericum perforatum). Understanding which genes are involved in dark gland development and hypericin biosynthesis is important for the development of new Hypericum extracts that are highly demanded for medical applications. We identified two transcription factors, whose expression is strictly synchronized with the differentiation of DGs. We correlated the content of hypericin, pseudohypericin, endocrocin, skyrin glycosides and several flavonoids with gene expression and DG development to obtain a revised model for hypericin biosynthesis. Here we report for the first‐time genotypes which are polymorphic for the presence/total‐absence (G+/G‐) of DGs in their placental tissues (PTs). DG development was characterized in PTs using several microscopy techniques. Fourier‐transformed infrared microscopy was established as a novel method to precisely locate polyaromatic compounds, such as hypericin, in plant tissues. In addition, we obtained transcriptome and metabolome profiles of unprecedented resolution in Hypericum. This study addresses for the first time the development of dark glands and identifies genes that constitute strong building blocks for the further elucidation of hypericin synthesis, its manipulation in plants, its engineering in microbial systems, and its applications in medical research.

BackgroundMolecule identification is a crucial step in metabolomics and environmental sciences. Besides in silico fragmentation, as performed by MetFrag, also machine learning and statistical methods evolved, showing an improvement in molecule annotation based on MS/MS data. In this work we present a new statistical scoring method where annotations of m/z fragment peaks to fragment-structures are learned in a training step. Based on a Bayesian model, two additional scoring terms are integrated into the new MetFrag2.4.5 and evaluated on the test data set of the CASMI 2016 contest.ResultsThe results on the 87 MS/MS spectra from positive and negative mode show a substantial improvement of the results compared to submissions made by the former MetFrag approach. Top1 rankings increased from 5 to 21 and Top10 rankings from 39 to 55 both showing higher values than for CSI:IOKR, the winner of the CASMI 2016 contest. For the negative mode spectra, MetFrag’s statistical scoring outperforms all other participants which submitted results for this type of spectra.ConclusionsThis study shows how statistical learning can improve molecular structure identification based on MS/MS data compared on the same method using combinatorial in silico fragmentation only. MetFrag2.4.5 shows especially in negative mode a better performance compared to the other participating approaches.

BackgroundTranscriptional gene regulation is a fundamental process in nature, and the experimental and computational investigation of DNA binding motifs and their binding sites is a prerequisite for elucidating this process. Approaches for de-novo motif discovery can be subdivided in phylogenetic footprinting that takes into account phylogenetic dependencies in aligned sequences of more than one species and non-phylogenetic approaches based on sequences from only one species that typically take into account intra-motif dependencies. It has been shown that modeling (i) phylogenetic dependencies as well as (ii) intra-motif dependencies separately improves de-novo motif discovery, but there is no approach capable of modeling both (i) and (ii) simultaneously.ResultsHere, we present an approach for de-novo motif discovery that combines phylogenetic footprinting with motif models capable of taking into account intra-motif dependencies. We study the degree of intra-motif dependencies inferred by this approach from ChIP-seq data of 35 transcription factors. We find that significant intra-motif dependencies of orders 1 and 2 are present in all 35 datasets and that intra-motif dependencies of order 2 are typically stronger than those of order 1. We also find that the presented approach improves the classification performance of phylogenetic footprinting in all 35 datasets and that incorporating intra-motif dependencies of order 2 yields a higher classification performance than incorporating such dependencies of only order 1.ConclusionCombining phylogenetic footprinting with motif models incorporating intra-motif dependencies leads to an improved performance in the classification of transcription factor binding sites. This may advance our understanding of transcriptional gene regulation and its evolution.

BackgroundTranscriptional gene regulation is a fundamental process in nature, and the experimental and computational investigation of DNA binding motifs and their binding sites is a prerequisite for elucidating this process. Approaches for de-novo motif discovery can be subdivided in phylogenetic footprinting that takes into account phylogenetic dependencies in aligned sequences of more than one species and non-phylogenetic approaches based on sequences from only one species that typically take into account intra-motif dependencies. It has been shown that modeling (i) phylogenetic dependencies as well as (ii) intra-motif dependencies separately improves de-novo motif discovery, but there is no approach capable of modeling both (i) and (ii) simultaneously.ResultsHere, we present an approach for de-novo motif discovery that combines phylogenetic footprinting with motif models capable of taking into account intra-motif dependencies. We study the degree of intra-motif dependencies inferred by this approach from ChIP-seq data of 35 transcription factors. We find that significant intra-motif dependencies of orders 1 and 2 are present in all 35 datasets and that intra-motif dependencies of order 2 are typically stronger than those of order 1. We also find that the presented approach improves the classification performance of phylogenetic footprinting in all 35 datasets and that incorporating intra-motif dependencies of order 2 yields a higher classification performance than incorporating such dependencies of only order 1.ConclusionCombining phylogenetic footprinting with motif models incorporating intra-motif dependencies leads to an improved performance in the classification of transcription factor binding sites. This may advance our understanding of transcriptional gene regulation and its evolution.

BackgroundOntology-based enrichment analysis aids in the interpretation and understanding of large-scale biological data. Ontologies are hierarchies of biologically relevant groupings. Using ontology annotations, which link ontology classes to biological entities, enrichment analysis methods assess whether there is a significant over or under representation of entities for ontology classes. While many tools exist that run enrichment analysis for protein sets annotated with the Gene Ontology, there are only a few that can be used for small molecules enrichment analysis.ResultsWe describe BiNChE, an enrichment analysis tool for small molecules based on the ChEBI Ontology. BiNChE displays an interactive graph that can be exported as a high-resolution image or in network formats. The tool provides plain, weighted and fragment analysis based on either the ChEBI Role Ontology or the ChEBI Structural Ontology.ConclusionsBiNChE aids in the exploration of large sets of small molecules produced within Metabolomics or other Systems Biology research contexts. The open-source tool provides easy and highly interactive web access to enrichment analysis with the ChEBI ontology tool and is additionally available as a standalone library.