Bioengineering of the plant culture of Capsicum frutescens with vanillin synthase gene for the production of vanillin

Production of vanillin by bioengineering has gained popularity due to consumer demand towards vanillin produced by biological systems. Natural vanillin from vanilla beans is very expensive to produce compared to its synthetic counterpart. Current bioengineering works mainly involve microbial biotechnology. Therefore, alternative means to the current approaches are constantly being explored. This work describes the use of vanillin synthase (VpVAN), to bioconvert ferulic acid to vanillin in a plant system. The VpVAN enzyme had been shown to directly convert ferulic acid and its glucoside into vanillin and its glucoside, respectively. As the ferulic acid precursor and vanillin were found to be the intermediates in the phenylpropanoid biosynthetic pathway of Capsicum species, this work serves as a proof-of-concept for vanillin production using Capsicum frutescens (C. frutescens or hot chili pepper). The cells of C. frutescens were genetically transformed with a codon optimized VpVAN gene via biolistics. Transformed explants were selected and regenerated into callus. Successful integration of the gene cassette into the plant genome was confirmed by polymerase chain reaction. High performance liquid chromatography was used to quantify the phenolic compounds detected in the callus tissues. The vanillin content of transformed calli was 0.057% compared to 0.0003% in untransformed calli

Deciphering the Preference and Predicting the Viability of Circular Permutations in Proteins

Circular permutation (CP) refers to situations in which the termini of a protein are relocated to other positions in the structure. CP occurs naturally and has been artificially created to study protein function, stability and folding. Recently CP is increasingly applied to engineer enzyme structure and function, and to create bifunctional fusion proteins unachievable by tandem fusion. CP is a complicated and expensive technique. An intrinsic difficulty in its application lies in the fact that not every position in a protein is amenable for creating a viable permutant. To examine the preferences of CP and develop CP viability prediction methods, we carried out comprehensive analyses of the sequence, structural, and dynamical properties of known CP sites using a variety of statistics and simulation methods, such as the bootstrap aggregating, permutation test and molecular dynamics simulations. CP particularly favors Gly, Pro, Asp and Asn. Positions preferred by CP lie within coils, loops, turns, and at residues that are exposed to solvent, weakly hydrogen-bonded, environmentally unpacked, or flexible. Disfavored positions include Cys, bulky hydrophobic residues, and residues located within helices or near the protein's core. These results fostered the development of an effective viable CP site prediction system, which combined four machine learning methods, e.g., artificial neural networks, the support vector machine, a random forest, and a hierarchical feature integration procedure developed in this work. As assessed by using the hydrofolate reductase dataset as the independent evaluation dataset, this prediction system achieved an AUC of 0.9. Large-scale predictions have been performed for nine thousand representative protein structures; several new potential applications of CP were thus identified. Many unreported preferences of CP are revealed in this study. The developed system is the best CP viability prediction method currently available. This work will facilitate the application of CP in research and biotechnology

Comparative proteomic analysis on fruit ripening processes in two varieties of tropical mango (Mangifera indica)

Mango (Mangifera indica L.) is an economically important fruit. However, the marketability of mango is affected by the perishable nature and short shelf-life of the fruit. Therefore, a better understanding of the mango ripening process is of great importance towards extending its postharvest shelf life. Proteomics is a powerful tool that can be used to elucidate the complex ripening process at the cellular and molecular levels. This study utilized 2-dimensional gel electrophoresis (2D-GE) coupled with MALDI-TOF/TOF to identify differentially abundant proteins during the ripening process of the two varieties of tropical mango, Mangifera indica cv. ‘Chokanan’ and Mangifera indica cv ‘Golden Phoenix’. The comparative analysis between the ripe and unripe stages of mango fruit mesocarp revealed that the differentially abundant proteins identified could be grouped into the three categories namely, ethylene synthesis and aromatic volatiles, cell wall degradation and stress-response proteins. There was an additional category for differential proteins identified from the ‘Chokanan’ variety namely, energy and carbohydrate metabolism. However, of all the differential proteins identified, only methionine gamma-lyase was found in both ‘Chokanan’ and ‘Golden Phoenix’ varieties. Six differential proteins were selected from each variety for validation by analysing their respective transcript expression using reverse transcription-quantitative PCR (RT-qPCR). The results revealed that two genes namely, glutathione S-transferase (GST) and alpha-1,4 glucan phosphorylase (AGP) were found to express in concordant with protein abundant. The findings will provide an insight into the fruit ripening process of different varieties of mango fruits, which is important for postharvest management

The Somatic Genomic Landscape of Glioblastoma

We describe the landscape of somatic genomic alterations based on multi-dimensional and comprehensive characterization of more than 500 glioblastoma tumors (GBMs). We identify several novel mutated genes as well as complex rearrangements of signature receptors including EGFR and PDGFRA. TERT promoter mutations are shown to correlate with elevated mRNA expression, supporting a role in telomerase reactivation. Correlative analyses confirm that the survival advantage of the proneural subtype is conferred by the G-CIMP phenotype, and MGMT DNA methylation may be a predictive biomarker for treatment response only in classical subtype GBM. Integrative analysis of genomic and proteomic profiles challenges the notion of therapeutic inhibition of a pathway as an alternative to inhibition of the target itself. These data will facilitate the discovery of therapeutic and diagnostic target candidates, the validation of research and clinical observations and the generation of unanticipated hypotheses that can advance our molecular understanding of this lethal cancer