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Summary Plant‐specialized metabolism is complex, with frequent examples of highly branched biosynthetic pathways, and shared chemical intermediates. As such, many plant‐specialized metabolic networks are poorly characterized.TheN‐methyl Δ¹‐pyrrolinium cation is a simple pyrrolidine alkaloid and precursor of pharmacologically important tropane alkaloids. Silencing of pyrrolidine ketide synthase (AbPyKS) in the roots ofAtropa belladonna(Deadly Nightshade) reduces tropane alkaloid abundance and causes highN‐methyl Δ¹‐pyrrolinium cation accumulation. The consequences of this metabolic shift on alkaloid metabolism are unknown. In this study, we utilized discovery metabolomics coupled withAbPyKSsilencing to reveal major changes in the root alkaloid metabolome ofA. belladonna.We discovered and annotated almost 40 pyrrolidine alkaloids that increase whenAbPyKSactivity is reduced. Suppression of phenyllactate biosynthesis, combined with metabolic engineeringin planta, and chemical synthesis indicates several of these pyrrolidines share a core structure formed through the nonenzymatic Mannich‐like decarboxylative condensation of theN‐methyl Δ¹‐pyrrolinium cation with 2‐O‐malonylphenyllactate. Decoration of this core scaffold through hydroxylation and glycosylation leads to mono‐ and dipyrrolidine alkaloid diversity.This study reveals the previously unknown complexity of theA. belladonnaroot metabolome and creates a foundation for future investigation into the biosynthesis, function, and potential utility of these novel alkaloids. 

Plants collectively synthesize a huge repertoire of metabolites. General metabolites, also referred to as primary metabolites, are conserved across the plant kingdom and are required for processes essential to growth and development. These include amino acids, sugars, lipids, and organic acids. In contrast, specialized metabolites, historically termed secondary metabolites, are structurally diverse, exhibit lineage-specific distribution and provide selective advantage to host species to facilitate reproduction and environmental adaptation. Due to their potent bioactivities, plant specialized metabolites attract considerable attention for use as flavorings, fragrances, pharmaceuticals, and bio-pesticides. The Solanaceae (Nightshade family) consists of approximately 2700 species and includes crops of significant economic, cultural, and scientific importance: these include potato, tomato, pepper, eggplant, tobacco, and petunia. The Solanaceae has emerged as a model family for studying the biochemical evolution of plant specialized metabolism and multiple examples exist of lineage-specific metabolites that influence the senses and physiology of commensal and harmful organisms, including humans. These include, alcohols, phenylpropanoids, and carotenoids that contribute to fruit aroma and color in tomato (fruity), glandular trichome-derived terpenoids and acylsugars that contribute to plant defense (stinky & sticky, respectively), capsaicinoids in chilli-peppers that influence seed dispersal (spicy), and steroidal glycoalkaloids (bitter) from Solanum, nicotine (addictive) from tobacco, as well as tropane alkaloids (deadly) from Deadly Nightshade that deter herbivory. Advances in genomics and metabolomics, coupled with the adoption of comparative phylogenetic approaches, resulted in deeper knowledge of the biosynthesis and evolution of these metabolites. This review highlights recent progress in this area and outlines opportunities for – and challenges of-developing a more comprehensive understanding of Solanaceae metabolism. 

Abstract Plant alkaloids constitute an important class of bioactive chemicals with applications in medicine and agriculture. However, the knowledge gap of the diversity and biosynthesis of phytoalkaloids prevents systematic advances in biotechnology for engineered production of these high-value compounds. In particular, the identification of cytochrome P450s driving the structural diversity of phytoalkaloids has remained challenging. Here, we use a combination of reverse genetics with discovery metabolomics and multivariate statistical analysis followed byin plantatransient assays to investigate alkaloid diversity and functionally characterize two candidate cytochrome P450s genes fromAtropa belladonnawithout a priori knowledge of their functions or information regarding the identities of key pathway intermediates. This approach uncovered a largely unexplored root localized alkaloid sub-network that relies on pseudotropine as precursor. The two cytochrome P450s catalyzeN-demethylation and ring-hydroxylation reactions within the early steps in the biosynthesis of diverseN-demethylated modified tropane alkaloids. 

Abstract Plant specialized metabolites mediate interactions between plants and the environment and have significant agronomical/pharmaceutical value. Most genes involved in specialized metabolism (SM) are unknown because of the large number of metabolites and the challenge in differentiating SM genes from general metabolism (GM) genes. Plant models like Arabidopsis thaliana have extensive, experimentally derived annotations, whereas many non-model species do not. Here we employed a machine learning strategy, transfer learning, where knowledge from A. thaliana is transferred to predict gene functions in cultivated tomato with fewer experimentally annotated genes. The first tomato SM/GM prediction model using only tomato data performs well (F-measure = 0.74, compared with 0.5 for random and 1.0 for perfect predictions), but from manually curating 88 SM/GM genes, we found many mis-predicted entries were likely mis-annotated. When the SM/GM prediction models built with A. thaliana data were used to filter out genes where the A. thaliana-based model predictions disagreed with tomato annotations, the new tomato model trained with filtered data improved significantly (F-measure = 0.92). Our study demonstrates that SM/GM genes can be better predicted by leveraging cross-species information. Additionally, our findings provide an example for transfer learning in genomics where knowledge can be transferred from an information-rich species to an information-poor one. 

Summary Plant metabolites from diverse pathways are important for plant survival, human nutrition and medicine. The pathway memberships of most plant enzyme genes are unknown. While co‐expression is useful for assigning genes to pathways, expression correlation may exist only under specific spatiotemporal and conditional contexts.Utilising > 600 tomato (Solanum lycopersicum) expression data combinations, three strategies for predicting memberships in 85 pathways were explored.Optimal predictions for different pathways require distinct data combinations indicative of pathway functions. Naive prediction (i.e. identifying pathways with the most similarly expressed genes) is error prone. In 52 pathways, unsupervised learning performed better than supervised approaches, possibly due to limited training data availability. Using gene‐to‐pathway expression similarities led to prediction models that outperformed those based simply on expression levels. Using 36 experimental validated genes, the pathway‐best model prediction accuracy is 58.3%, significantly better compared with that for predicting annotated genes without experimental evidence (37.0%) or random guess (1.2%), demonstrating the importance of data quality.Our study highlights the need to extensively explore expression‐based features and prediction strategies to maximise the accuracy of metabolic pathway membership assignment. The prediction framework outlined here can be applied to other species and serves as a baseline model for future comparisons.