Exploring Uncharted Territories of Plant Specialized Metabolism in the Postgenomic Era

Abstract

For millennia, humans have used plants for food, raw materials, and medicines, but only within the past two centuries have we begun to connect particular plant metabolites with specific properties and utilities. Since the utility of classical molecular genetics beyond model species is limited, the vast specialized metabolic systems present in the Earth's flora remain largely unstudied. With an explosion in genomics resources and a rapidly expanding toolbox over the past decade, exploration of plant specialized metabolism in nonmodel species is becoming more feasible than ever before. We review the state-of-the-art tools that have enabled this rapid progress. We present recent examples of de novo biosynthetic pathway discovery that employ various innovative approaches. We also draw attention to the higher-order organization of plant specialized metabolism at subcellular, cellular, tissue, interorgan, and interspecies levels, which will have important implications for the future design of comprehensive metabolic engineering strategies.

Versatility in acyltransferase activity completes chicoric acid biosynthesis in purple coneflower

“…For millennia, humans have used plant specialized metabolites as herbal medicines. During the past two decades, especially in the postgenomic era, strategies such as co-expression and genome mining have been used extensively for metabolic pathway identification 42 . In this study, however, we describe steps in a metabolic pathway identified using traditional biochemistry.…”

“…In this study, however, we describe steps in a metabolic pathway identified using traditional biochemistry. While RNA-seq, co-expression analysis and genome mining are becoming more popular approaches, discovery of genuinely novel activities may still be served best by strategies based on characterizing enzyme activities 42 .…”

Using Gene Expression to Study Specialized Metabolism—A Practical Guide

“…Fortunately, the last decade has seen the emergence of novel methods in the area of genomics, transcriptomics, proteomics, metabolomics, synthetic biology, and gene function prediction, which has fueled the identification of SM biosynthetic pathways (Jacobowitz and Weng, 2020;Mutwil, 2020). These additional approaches provide multipronged sources of information to predict the identity of the enzymes making a given SM, allowing rapid de novo biosynthetic pathway prediction in nonmodel plants (Torrens-Spence et al, 2016).…”

“…These additional approaches provide multipronged sources of information to predict the identity of the enzymes making a given SM, allowing rapid de novo biosynthetic pathway prediction in nonmodel plants (Torrens-Spence et al, 2016). These predictions can then be rapidly tested by synthesizing codon-optimized cDNA of the putative enzyme and expressed in a laboratory microbe or a more suitable plant, such as Nicotiana benthamiana [please see the excellent review on these approaches in Jacobowitz and Weng, 2020)]. The various computational approaches comprising sequence similarity, Quantitative Trait Loci/Genome-Wide Association Studies (QTL, GWAS), phylogenetic profiling, and machine learning have been extensively reviewed elsewhere (Jacobowitz and Weng, 2020;Mutwil, 2020).…”

Evolution-aided engineering of plant specialized metabolism

“…Overall, at least 122 plantderived compounds are used as drugs in their purest form (Fabricant and Farnsworth 2001;Kinghorn et al 2011), but between 10,000 and 53,000 plant species are estimated to be used in traditional medicine (Soejarto et al 2005;Gurib-Fakim 2006), revealing the enormous untapped potential of plants as sources of drugs. Unfortunately, despite their importance, extraction of specialized metabolites from plants creates new problems related to environmental sustainability, the balance of supply-demand, and equitable distribution of the extracted products (Reed and Osbourn 2018;Jacobowitz and Weng 2020). Chemical synthesis is frequently challenging and cost-inefficient because of the structural complexity of metabolites (Reed and Osbourn 2018).…”