RNA-seq Analysis of Monoterpene Indole Alkaloid Biosynthetic Pathway Elucidation in Catharanthus roseus.

Increased affordability and availability of high-throughput next-generation sequencing (NGS) technologies have resulted in an explosion of available RNA-seq data, igniting a variety of data-mining methodologies, valuable for plant-specialized biosynthetic pathway discovery. When combined with traditional homology-based annotations, these methods can facilitate short-listing candidate genes for downstream functional validation screenings. Genes related to common pathways often display homogenous expression patterns across different tissue types and experimental conditions. Here, we describe bioinformatic protocols for exploiting such coexpression to shortlist candidate genes of the well-described monoterpene indole alkaloid (MIA) pathway of Catharanthus roseus. These methods aim to inspire researchers to utilize this publicly available RNA-seq treasure trove to guide their own endeavors in the characterization of missing steps in plant metabolic pathways.

Predicting Monoterpene Indole Alkaloid-Related Genes from Expression Data with Artificial Neural Networks.

Elucidation of biological pathways leading to specialized metabolites remains a complex task. It is however a mandatory step to allow bioproduction into heterologous hosts. Many steps have already been identified using conventional approaches, enlarging the space of known possible chemical steps. In the recent past years, identification of missing steps has been fueled by the generation of genomic and transcriptomic data for nonmodel species. The analysis of gene expression profiles has revealed that in many cases, genes encoding enzymes involved in the same biosynthetic pathways are coexpressed across different tissue types and environmental conditions. Hence, coexpressed studies, either in the form of differential gene expression, gene coexpression network, or unsupervised clustering methods, have helped deciphering missing steps to complete knowledge on biosynthetic pathways. Already identified biosynthetic steps can be used as baits to capture the remaining unknown steps. The present protocol shows how supervised machine learning in the form of artificial neural networks (ANNs) can efficiently classify genes as specialized metabolism related or not according to their expression levels. Using Catharanthus roseus as an example, we show that ANN trained on a minimal set of bait genes results in many true positives (correctly predicted genes) while keeping false positives low (containing possible candidate genes).

Visualization of Aspalathin in Rooibos (Aspalathus linearis) Plant and Herbal Tea Extracts Using Thin-Layer Chromatography.

Aspalathin, the main polyphenol of rooibos (Aspalathus linearis), is associated with diverse health promoting properties of the tea. During fermentation, aspalathin is oxidized and concentrations are significantly reduced. Standardized methods for quality control of rooibos products do not investigate aspalathin, since current techniques of aspalathin detection require expensive equipment and expertise. Here, we describe a simple and fast thin-layer chromatography (TLC) method that can reproducibly visualize aspalathin in rooibos herbal tea and plant extracts at a limit of detection (LOD) equal to 178.7 ng and a limit of quantification (LOQ) equal to 541.6 ng. Aspalathin is a rare compound, so far only found in A. linearis and its (rare) sister species A. pendula. Therefore, aspalathin could serve as a marker compound for authentication and quality control of rooibos products, and the described TLC method represents a cost-effective approach for high-throughput screening of plant and herbal tea extracts.