The intra-cellular localization of the vanillin biosynthetic machinery in pods of Vanilla planifolia

Vanillin is the most important flavor compound in the vanilla pod. Vanilla planifolia vanillin synthase (VpVAN) catalyzes the conversion of ferulic acid and ferulic acid glucoside into vanillin and vanillin glucoside, respectively. Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) of vanilla pod sections demonstrates that vanillin glucoside is preferentially localized within the mesocarp and placental laminae whereas vanillin is preferentially localized within the mesocarp. VpVAN is present as the mature form (25 kDa) but, depending on the tissue and isolation procedure, small amounts of the immature unprocessed form (40 kDa) and putative oligomers (50, 75 and 100 kDa) may be observed by immunoblotting using an antibody specific to the C-terminal sequence of VpVAN. The VpVAN protein is localized within chloroplasts and re-differentiated chloroplasts termed phenyloplasts, as monitored during the process of pod development. Isolated chloroplasts were shown to convert [14C]phenylalanine and [14C]cinnamic acid into [14C]vanillin glucoside, indicating that the entire vanillin de novo biosynthetic machinery converting phenylalanine to vanillin glucoside is present in the chloroplast

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub

A draft map of the mouse pluripotent stem cell spatial proteome.

Knowledge of the subcellular distribution of proteins is vital for understanding cellular mechanisms. Capturing the subcellular proteome in a single experiment has proven challenging, with studies focusing on specific compartments or assigning proteins to subcellular niches with low resolution and/or accuracy. Here we introduce hyperLOPIT, a method that couples extensive fractionation, quantitative high-resolution accurate mass spectrometry with multivariate data analysis. We apply hyperLOPIT to a pluripotent stem cell population whose subcellular proteome has not been extensively studied. We provide localization data on over 5,000 proteins with unprecedented spatial resolution to reveal the organization of organelles, sub-organellar compartments, protein complexes, functional networks and steady-state dynamics of proteins and unexpected subcellular locations. The method paves the way for characterizing the impact of post-transcriptional and post-translational modification on protein location and studies involving proteome-level locational changes on cellular perturbation. An interactive open-source resource is presented that enables exploration of these data.The authors thank Andreas Hühmer, Philip Remes, Jesse Canterbury and Graeme McAlister of Thermo Fisher Scientific, San Jose, CA, USA, for their advice regarding operation of the Orbitrap Fusion. We also thank Mike Deery for assistance with checking sample integrity on the mass spectrometers in the Cambridge Centre for Proteomics on equipment purchased via a Wellcome Trust grant (099135/Z/12/Z ), and Brian Hendrich of the Wellcome Trust-MRC Stem Cell Institute in Cambridge and Sean Munro of the MRC Laboratory of Molecular Biology in Cambridge for insightful comments about the data. AC was supported by BBSRC grant (BB/D526088/1). C.M.M. and L.G. were supported by European Union 7th Framework Program (PRIMEXS project, grant agreement number 262067), L.M.B was supported by a BBSRC Tools and Resources Development Fund (Award BB/K00137X/1), and P.C.H. was supported by an ERC Advanced Investigator grant to A.M.A. A.G. was funded through the Alexander S. Onassis Public Benefit Foundation, the Foundation for Education and European Culture (IPEP) and the Embiricos Trust Scholarship of Jesus College Cambridge. T.H. was supported by Commonwealth Split Site PhD Scholarship. T.N. was supported by an ERASMUS Placement scholarshipThis is the final version of the article. It was first available from NPG via http://dx.doi.org/10.1038/ncomms999

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub

Kingdom-wide analysis of the evolution of the plant type III polyketide synthase superfamily

The emergence of type III polyketide synthases (PKSs) was a prerequisite for the conquest of land by the green lineage. Within the PKS superfamily, chalcone synthases (CHSs) provide the entry point reaction to the flavonoid pathway, while LESS ADHESIVE POLLEN 5 and 6 (LAP5/6) provide constituents of the outer exine pollen wall. To study the deep evolutionary history of this key family, we conducted phylogenomic synteny network and phylogenetic analyses of whole-genome data from 126 species spanning the green lineage including Arabidopsis thaliana, tomato (Solanum lycopersicum), and maize (Zea mays). This study thereby combined study of genomic location and context with changes in gene sequences. We found that the two major clades, CHS and LAP5/6 homologs, evolved early by a segmental duplication event prior to the divergence of Bryophytes and Tracheophytes. We propose that the macroevolution of the type III PKS superfamily is governed by whole-genome duplications and triplications. The combined phylogenetic and synteny analyses in this study provide insights into changes in the genomic location and context that are retained for a longer time scale with more recent functional divergence captured by gene sequence alterations

Diversity of Chemical Structures and Biosynthesis of Polyphenols in Nut-Bearing Species

Nuts, such as peanut, almond, and chestnut, are valuable food crops for humans being important sources of fatty acids, vitamins, minerals, and polyphenols. Polyphenols, such as flavonoids, stilbenoids, and hydroxycinnamates, represent a group of plant-specialized (secondary) metabolites which are characterized as health-beneficial antioxidants within the human diet as well as physiological stress protectants within the plant. In food chemistry research, a multitude of polyphenols contained in culinary nuts have been studied leading to the identification of their chemical properties and bioactivities. Although functional elucidation of the biosynthetic genes of polyphenols in nut species is crucially important for crop improvement in the creation of higher-quality nuts and stress-tolerant cultivars, the chemical diversity of nut polyphenols and the key biosynthetic genes responsible for their production are still largely uncharacterized. However, current technical advances in whole-genome sequencing have facilitated that nut plant species became model plants for omics-based approaches. Here, we review the chemical diversity of seed polyphenols in majorly consumed nut species coupled to insights into their biological activities. Furthermore, we present an example of the annotation of key genes involved in polyphenolic biosynthesis in peanut using comparative genomics as a case study outlining how we are approaching omics-based approaches of the nut plant species