Proteomics and Co-expression Network Analysis Reveal the Importance of Hub Proteins and Metabolic Pathways in Nicotine Synthesis and Accumulation in Tobacco (Nicotiana tabacum L.)

Proteomics: An Essential Tool to Study Plant-Specialized Metabolism

Plants are a valuable source of specialized metabolites that provide a plethora of therapeutic applications. They are natural defenses that plants use to adapt and respond to their changing environment. Decoding their biosynthetic pathways and understanding how specialized plant metabolites (SPMs) respond to biotic or abiotic stress will provide vital knowledge for plant biology research and its application for the future sustainable production of many SPMs of interest. Here, we focus on the proteomic approaches and strategies that help with the study of plant-specialized metabolism, including the: (i) discovery of key enzymes and the clarification of their biosynthetic pathways; (ii) study of the interconnection of both primary (providers of carbon and energy for SPM production) and specialized (secondary) metabolism; (iii) study of plant responses to biotic and abiotic stress; (iv) study of the regulatory mechanisms that direct their biosynthetic pathways. Proteomics, as exemplified in this review by the many studies performed to date, is a powerful tool that forms part of omics-driven research. The proteomes analysis provides an additional unique level of information, which is absent from any other omics studies. Thus, an integrative analysis, considered versus a single omics analysis, moves us more closely toward a closer interpretation of real cellular processes. Finally, this work highlights advanced proteomic technologies with immediate applications in the field.

Current Status and De Novo Synthesis of Anti-Tumor Alkaloids in Nicotiana

Alkaloids are the most diversified nitrogen-containing secondary metabolites, having antioxidant and antimicrobial properties, and are extensively used in pharmaceuticals to treat different types of cancer. Nicotiana serves as a reservoir of anti-cancer alkaloids and is also used as a model plant for the de novo synthesis of various anti-cancer molecules through genetic engineering. Up to 4% of the total dry weight of Nicotiana was found to be composed of alkaloids, where nicotine, nornicotine, anatabine, and anabasine are reported as the dominant alkaloids. Additionally, among the alkaloids present in Nicotiana, β-carboline (Harmane and Norharmane) and Kynurenines are found to show anti-tumor effects, especially in the cases of colon and breast cancers. Creating new or shunting of existing biosynthesis pathways in different species of Nicotiana resulted in de novo or increased synthesis of different anti-tumor molecules or their derivatives or precursors including Taxadiane (~22.5 µg/g), Artemisinin (~120 μg/g), Parthenolide (~2.05 ng/g), Costunolide (~60 ng/g), Etoposide (~1 mg/g), Crocin (~400 µg/g), Catharanthine (~60 ng/g), Tabersonine (~10 ng/g), Strictosidine (~0.23 mg/g), etc. Enriching the precursor pool, especially Dimethylallyl Diphosphate (DMAPP), down-regulating other bi-product pathways, compartmentalization or metabolic shunting, or organelle-specific reconstitution of the precursor pool, might trigger the enhanced accumulation of the targeted anti-cancer alkaloid in Nicotiana.

From Classical to Modern Computational Approaches to Identify Key Genetic Regulatory Components in Plant Biology

The selection of plant genotypes with improved productivity and tolerance to environmental constraints has always been a major concern in plant breeding. Classical approaches based on the generation of variability and selection of better phenotypes from large variant collections have improved their efficacy and processivity due to the implementation of molecular biology techniques, particularly genomics, Next Generation Sequencing and other omics such as proteomics and metabolomics. In this regard, the identification of interesting variants before they develop the phenotype trait of interest with molecular markers has advanced the breeding process of new varieties. Moreover, the correlation of phenotype or biochemical traits with gene expression or protein abundance has boosted the identification of potential new regulators of the traits of interest, using a relatively low number of variants. These important breakthrough technologies, built on top of classical approaches, will be improved in the future by including the spatial variable, allowing the identification of gene(s) involved in key processes at the tissue and cell levels.