NYUS.2: an automated machine learning prediction model for the large-scale real-time simulation of grapevine freezing tolerance in North America.

Accurate and real-time monitoring of grapevine freezing tolerance is crucial for the sustainability of the grape industry in cool climate viticultural regions. However, on-site data are limited due to the complexity of measurement. Current prediction models underperform under diverse climate conditions, which limits the large-scale deployment of these methods. We combined grapevine freezing tolerance data from multiple regions in North America and generated a predictive model based on hourly temperature-derived features and cultivar features using AutoGluon, an automated machine learning engine. Feature importance was quantified by AutoGluon and SHAP (SHapley Additive exPlanations) value. The final model was evaluated and compared with previous models for its performance under different climate conditions. The final model achieved an overall 1.36°C root-mean-square error during model testing and outperformed two previous models using three test cultivars at all testing regions. Two feature importance quantification methods identified five shared essential features. Detailed analysis of the features indicates that the model has adequately extracted some biological mechanisms during training. The final model, named NYUS.2, was deployed along with two previous models as an R shiny-based application in the 2022-23 dormancy season, enabling large-scale and real-time simulation of grapevine freezing tolerance in North America for the first time.

Information theory and machine learning illuminate large-scale metabolomic responses of Brachypodium distachyon to environmental change.

Plant responses to environmental change are mediated via changes in cellular metabolomes. However, <5% of signals obtained from liquid chromatography tandem mass spectrometry (LC-MS/MS) can be identified, limiting our understanding of how metabolomes change under biotic/abiotic stress. To address this challenge, we performed untargeted LC-MS/MS of leaves, roots, and other organs of Brachypodium distachyon (Poaceae) under 17 organ-condition combinations, including copper deficiency, heat stress, low phosphate, and arbuscular mycorrhizal symbiosis. We found that both leaf and root metabolomes were significantly affected by the growth medium. Leaf metabolomes were more diverse than root metabolomes, but the latter were more specialized and more responsive to environmental change. We found that 1 week of copper deficiency shielded the root, but not the leaf metabolome, from perturbation due to heat stress. Machine learning (ML)-based analysis annotated approximately 81% of the fragmented peaks versus approximately 6% using spectral matches alone. We performed one of the most extensive validations of ML-based peak annotations in plants using thousands of authentic standards, and analyzed approximately 37% of the annotated peaks based on these assessments. Analyzing responsiveness of each predicted metabolite class to environmental change revealed significant perturbations of glycerophospholipids, sphingolipids, and flavonoids. Co-accumulation analysis further identified condition-specific biomarkers. To make these results accessible, we developed a visualization platform on the Bio-Analytic Resource for Plant Biology website (https://bar.utoronto.ca/efp_brachypodium_metabolites/cgi-bin/efpWeb.cgi), where perturbed metabolite classes can be readily visualized. Overall, our study illustrates how emerging chemoinformatic methods can be applied to reveal novel insights into the dynamic plant metabolome and stress adaptation.

Phylogenomic analyses across land plants reveals motifs and coexpression patterns useful for functional prediction in the BAHD acyltransferase family.

The BAHD acyltransferase family is one of the largest enzyme families in flowering plants, containing dozens to hundreds of genes in individual genomes. Highly prevalent in angiosperm genomes, members of this family contribute to several pathways in primary and specialized metabolism. In this study, we performed a phylogenomic analysis of the family using 52 genomes across the plant kingdom to gain deeper insights into its functional evolution and enable function prediction. We found that BAHD expansion in land plants was associated with significant changes in various gene features. Using pre-defined BAHD clades, we identified clade expansions in different plant groups. In some groups, these expansions coincided with the prominence of metabolite classes such as anthocyanins (flowering plants) and hydroxycinnamic acid amides (monocots). Clade-wise motif-enrichment analysis revealed that some clades have novel motifs fixed on either the acceptor or the donor side, potentially reflecting historical routes of functional evolution. Co-expression analysis in rice and Arabidopsis further identified BAHDs with similar expression patterns, however, most co-expressed BAHDs belonged to different clades. Comparing BAHD paralogs, we found that gene expression diverges rapidly after duplication, suggesting that sub/neo-functionalization of duplicate genes occurs quickly via expression diversification. Analyzing co-expression patterns in Arabidopsis in conjunction with orthology-based substrate class predictions and metabolic pathway models led to the recovery of metabolic processes of most of the already-characterized BAHDs as well as definition of novel functional predictions for some uncharacterized BAHDs. Overall, this study provides new insights into the evolution of BAHD acyltransferases and sets up a foundation for their functional characterization.

Chromosome-level Genome Assembly of Euphorbia peplus, a Model System for Plant Latex, Reveals that Relative Lack of Ty3 Transposons Contributed to Its Small Genome Size.

Euphorbia peplus (petty spurge) is a small, fast-growing plant that is native to Eurasia and has become a naturalized weed in North America and Australia. Euphorbia peplus is not only medicinally valuable, serving as a source for the skin cancer drug ingenol mebutate, but also has great potential as a model for latex production owing to its small size, ease of manipulation in the laboratory, and rapid reproductive cycle. To help establish E. peplus as a new model, we generated a 267.2-Mb Hi-C-anchored PacBio HiFi nuclear genome assembly with a BUSCO score of 98.5%, a genome annotation based on RNA-seq data from six organs, and publicly accessible tools including a genome browser and an interactive organ-specific expression atlas. Chromosome number is highly variable across Euphorbia species. Using a comparative analysis of our newly sequenced E. peplus genome with other Euphorbiaceae genomes, we show that variation in Euphorbia chromosome number between E. peplus and Euphorbia lathyris is likely due to fragmentation and rearrangement rather than chromosomal duplication followed by diploidization of the duplicated sequence. Moreover, we found that the E. peplus genome is relatively compact compared with related members of the genus in part due to restricted expansion of the Ty3 transposon family. Finally, we identify a large gene cluster that contains many previously identified enzymes in the putative ingenol mebutate biosynthesis pathway, along with additional gene candidates for this biosynthetic pathway. The genomic resources we have created for E. peplus will help advance research on latex production and ingenol mebutate biosynthesis in the commercially important Euphorbiaceae family.

A roadmap for the functional annotation of protein families: a community perspective.

Over the last 25 years, biology has entered the genomic era and is becoming a science of 'big data'. Most interpretations of genomic analyses rely on accurate functional annotations of the proteins encoded by more than 500 000 genomes sequenced to date. By different estimates, only half the predicted sequenced proteins carry an accurate functional annotation, and this percentage varies drastically between different organismal lineages. Such a large gap in knowledge hampers all aspects of biological enterprise and, thereby, is standing in the way of genomic biology reaching its full potential. A brainstorming meeting to address this issue funded by the National Science Foundation was held during 3-4 February 2022. Bringing together data scientists, biocurators, computational biologists and experimentalists within the same venue allowed for a comprehensive assessment of the current state of functional annotations of protein families. Further, major issues that were obstructing the field were identified and discussed, which ultimately allowed for the proposal of solutions on how to move forward.

Orthology-based analysis helps map evolutionary diversification and predict substrate class use of BAHD acyltransferases.

Large enzyme families catalyze metabolic diversification by virtue of their ability to use diverse chemical scaffolds. How enzyme families attain such functional diversity is not clear. Furthermore, duplication and promiscuity in such enzyme families limits their functional prediction, which has produced a burgeoning set of incompletely annotated genes in plant genomes. Here, we address these challenges using BAHD acyltransferases as a model. This fast-evolving family expanded drastically in land plants, increasing from one to five copies in algae to approximately 100 copies in diploid angiosperm genomes. Compilation of >160 published activities helped visualize the chemical space occupied by this family and define eight different classes based on structural similarities between acceptor substrates. Using orthologous groups (OGs) across 52 sequenced plant genomes, we developed a method to predict BAHD acceptor substrate class utilization as well as origins of individual BAHD OGs in plant evolution. This method was validated using six novel and 28 previously characterized enzymes and helped improve putative substrate class predictions for BAHDs in the tomato genome. Our results also revealed that while cuticular wax and lignin biosynthetic activities were more ancient, anthocyanin acylation activity was fixed in BAHDs later near the origin of angiosperms. The OG-based analysis enabled identification of signature motifs in anthocyanin-acylating BAHDs, whose importance was validated via molecular dynamic simulations, site-directed mutagenesis and kinetic assays. Our results not only describe how BAHDs contributed to evolution of multiple chemical phenotypes in the plant world but also propose a biocuration-enabled approach for improved functional annotation of plant enzyme families.

Illuminating the lineage-specific diversification of resin glycoside acylsugars in the morning glory (Convolvulaceae) family using computational metabolomics.

Acylsugars are a class of plant defense compounds produced across many distantly related families. Members of the horticulturally important morning glory (Convolvulaceae) family produce a diverse sub-class of acylsugars called resin glycosides (RGs), which comprise oligosaccharide cores, hydroxyacyl chain(s), and decorating aliphatic and aromatic acyl chains. While many RG structures are characterized, the extent of structural diversity of this class in different genera and species is not known. In this study, we asked whether there has been lineage-specific diversification of RG structures in different Convolvulaceae species that may suggest diversification of the underlying biosynthetic pathways. Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) was performed from root and leaf extracts of 26 species sampled in a phylogeny-guided manner. LC-MS/MS revealed thousands of peaks with signature RG fragmentation patterns with one species producing over 300 signals, mirroring the diversity in Solanaceae-type acylsugars. A novel RG from Dichondra argentea was characterized using Nuclear Magnetic Resonance spectroscopy, supporting previous observations of RGs with open hydroxyacyl chains instead of closed macrolactone ring structures. Substantial lineage-specific differentiation in utilization of sugars, hydroxyacyl chains, and decorating acyl chains was discovered, especially among Ipomoea and Convolvulus - the two largest genera in Convolvulaceae. Adopting a computational, knowledge-based strategy, we further developed a high-recall workflow that successfully explained ~72% of the MS/MS fragments, predicted the structural components of 11/13 previously characterized RGs, and partially annotated ~45% of the RGs. Overall, this study improves our understanding of phytochemical diversity and lays a foundation for characterizing the evolutionary mechanisms underlying RG diversification.

Acylsugars protect Nicotiana benthamiana against insect herbivory and desiccation.

KEY MESSAGE: Nicotiana benthamiana acylsugar acyltransferase (ASAT) is required for protection against desiccation and insect herbivory. Knockout mutations provide a new resource for investigation of plant-aphid and plant-whitefly interactions. Nicotiana benthamiana is used extensively as a transient expression platform for functional analysis of genes from other species. Acylsugars, which are produced in the trichomes, are a hypothesized cause of the relatively high insect resistance that is observed in N. benthamiana. We characterized the N. benthamiana acylsugar profile, bioinformatically identified two acylsugar acyltransferase genes, ASAT1 and ASAT2, and used CRISPR/Cas9 mutagenesis to produce acylsugar-deficient plants for investigation of insect resistance and foliar water loss. Whereas asat1 mutations reduced accumulation, asat2 mutations caused almost complete depletion of foliar acylsucroses. Three hemipteran and three lepidopteran herbivores survived, gained weight, and/or reproduced significantly better on asat2 mutants than on wildtype N. benthamiana. Both asat1 and asat2 mutations reduced the water content and increased leaf temperature. Our results demonstrate the specific function of two ASAT proteins in N. benthamiana acylsugar biosynthesis, insect resistance, and desiccation tolerance. The improved growth of aphids and whiteflies on asat2 mutants will facilitate the use of N. benthamiana as a transient expression platform for the functional analysis of insect effectors and resistance genes from other plant species. Similarly, the absence of acylsugars in asat2 mutants will enable analysis of acylsugar biosynthesis genes from other Solanaceae by transient expression.

Growing a glue factory: Open questions in laticifer development.

Latex-containing cells called laticifers are present in at least 41 flowering plant families and are thought to have convergently evolved at least 12 times. These cells are known to function in defense, but little is known about the molecular genetic mechanisms of their development. The expansion of laticifers into their distinctive tube shape can occur through two distinct mechanisms, cell fusion and intrusive growth. The mechanism and extent of intrusive laticifer growth are still being investigated. Hormonal regulation by jasmonic acid and ethylene is important for both laticifer differentiation and latex biosynthesis. Current evidence suggests that laticifers can be specified independently of latex production, but extensive latex production requires specified laticifers. Laticifers are an emerging system for studying the intersection of cell identity specification and specialized metabolism.

Untargeted metabolomics of purple and orange-fleshed sweet potatoes reveals a large structural diversity of anthocyanins and flavonoids.

Anthocyanins are economically valuable phytochemicals of significant relevance to human health. Industrially extracted from multiple fruit and vegetable sources, anthocyanin yield and profiles can vary between sources and growing conditions. In this study, we focused on three purple-fleshed and one orange-fleshed cultivars of sweet potato-a warm-weather, nutritious crop of substantial interest to growers in northern, cooler latitudes-to determine the yield and diversity of anthocyanins and flavonoids. Acidified ethanol extraction of lyophilized roots yielded ~ 800 mg average anthocyanins/100 g dry weight from all three cultivars. UHPLC-DAD-Orbitrap analysis of sweet potato extracts identified 18 high-confidence, mostly acylated peonidin and cyanidin derivatives contributing to > 90% of the total anthocyanin signal. Further assessment of the untargeted Liquid Chromatography-Tandem Mass Spectrometry data using deep learning and molecular networking identified over 350 flavonoid peaks with variable distributions in different sweet potato cultivars. These results provide a novel insight into anthocyanin content of purple-fleshed sweet potatoes grown in the northern latitudes, and reveal the large structural diversity of anthocyanins and flavonoids in this popular crop.

Migration through a Major Andean Ecogeographic Disruption as a Driver of Genetic and Phenotypic Diversity in a Wild Tomato Species.

Evolutionary dynamics at the population level play a central role in creating the diversity of life on our planet. In this study, we sought to understand the origins of such population-level variation in mating systems and defensive acylsugar chemistry in Solanum habrochaites-a wild tomato species found in diverse Andean habitats in Ecuador and Peru. Using Restriction-site-Associated-DNA-Sequencing (RAD-seq) of 50 S. habrochaites accessions, we identified eight population clusters generated via isolation and hybridization dynamics of 4-6 ancestral populations. Detailed characterization of mating systems of these clusters revealed emergence of multiple self-compatible (SC) groups from progenitor self-incompatible populations in the northern part of the species range. Emergence of these SC groups was also associated with fixation of deleterious alleles inactivating acylsugar acetylation. The Amotape-Huancabamba Zone-a geographical landmark in the Andes with high endemism and isolated microhabitats-was identified as a major driver of differentiation in the northern species range, whereas large geographical distances contributed to population structure and evolution of a novel SC group in the central and southern parts of the range, where the species was also inferred to have originated. Findings presented here highlight the role of the diverse ecogeography of Peru and Ecuador in generating population differentiation, and enhance our understanding of the microevolutionary processes that create biological diversity.

Evolution-aided engineering of plant specialized metabolism.

The evolution of new traits in living organisms occurs via the processes of mutation, recombination, genetic drift, and selection. These processes that have resulted in the immense biological diversity on our planet are also being employed in metabolic engineering to optimize enzymes and pathways, create new-to-nature reactions, and synthesize complex natural products in heterologous systems. In this review, we discuss two evolution-aided strategies for metabolic engineering-directed evolution, which improves upon existing genetic templates using the evolutionary process, and combinatorial pathway reconstruction, which brings together genes evolved in different organisms into a single heterologous host. We discuss the general principles of these strategies, describe the technologies involved and the molecular traits they influence, provide examples of their use, and discuss the roadblocks that need to be addressed for their wider adoption. A better understanding of these strategies can provide an impetus to research on gene function discovery and biochemical evolution, which is foundational for improved metabolic engineering. These evolution-aided approaches thus have a substantial potential for improving our understanding of plant metabolism in general, for enhancing the production of plant metabolites, and in sustainable agriculture.

Machine learning: A powerful tool for gene function prediction in plants.

Recent advances in sequencing and informatic technologies have led to a deluge of publicly available genomic data. While it is now relatively easy to sequence, assemble, and identify genic regions in diploid plant genomes, functional annotation of these genes is still a challenge. Over the past decade, there has been a steady increase in studies utilizing machine learning algorithms for various aspects of functional prediction, because these algorithms are able to integrate large amounts of heterogeneous data and detect patterns inconspicuous through rule-based approaches. The goal of this review is to introduce experimental plant biologists to machine learning, by describing how it is currently being used in gene function prediction to gain novel biological insights. In this review, we discuss specific applications of machine learning in identifying structural features in sequenced genomes, predicting interactions between different cellular components, and predicting gene function and organismal phenotypes. Finally, we also propose strategies for stimulating functional discovery using machine learning-based approaches in plants.

Specialized Metabolism in a Nonmodel Nightshade: Trichome Acylinositol Biosynthesis.

Plants make many biologically active, specialized metabolites, which vary in structure, biosynthesis, and the processes they influence. An increasing number of these compounds are documented to protect plants from insects, pathogens, or herbivores or to mediate interactions with beneficial organisms, including pollinators and nitrogen-fixing microbes. Acylsugars, one class of protective compounds, are made in glandular trichomes of plants across the Solanaceae family. While most described acylsugars are acylsucroses, published examples also include acylsugars with hexose cores. The South American fruit crop naranjilla (lulo; Solanum quitoense) produces acylsugars containing a myoinositol core. We identified an enzyme that acetylates triacylinositols, a function homologous to the last step in the acylsucrose biosynthetic pathway of tomato (Solanum lycopersicum). Our analysis reveals parallels between S. lycopersicum acylsucrose and S. quitoense acylinositol biosynthesis, suggesting a common evolutionary origin.

A massively parallel barcoded sequencing pipeline enables generation of the first ORFeome and interactome map for rice.

Systematic mappings of protein interactome networks have provided invaluable functional information for numerous model organisms. Here we develop PCR-mediated Linkage of barcoded Adapters To nucleic acid Elements for sequencing (PLATE-seq) that serves as a general tool to rapidly sequence thousands of DNA elements. We validate its utility by generating the ORFeome for Oryza sativa covering 2,300 genes and constructing a high-quality protein-protein interactome map consisting of 322 interactions between 289 proteins, expanding the known interactions in rice by roughly 50%. Our work paves the way for high-throughput profiling of protein-protein interactions in a wide range of organisms.

Evolutionary characteristics of intergenic transcribed regions indicate rare novel genes and widespread noisy transcription in the Poaceae.

Extensive transcriptional activity occurring in intergenic regions of genomes has raised the question whether intergenic transcription represents the activity of novel genes or noisy expression. To address this, we evaluated cross-species and post-duplication sequence and expression conservation of intergenic transcribed regions (ITRs) in four Poaceae species. Among 43,301 ITRs across the four species, 34,460 (80%) are species-specific. ITRs found across species tend to be more divergent in expression and have more recent duplicates compared to annotated genes. To assess if ITRs are functional (under selection), machine learning models were established in Oryza sativa (rice) that could accurately distinguish between phenotype genes and pseudogenes (area under curve-receiver operating characteristic = 0.94). Based on the models, 584 (8%) and 4391 (61%) rice ITRs are classified as likely functional and nonfunctional with high confidence, respectively. ITRs with conserved expression and ancient retained duplicates, features that were not part of the model, are frequently classified as likely-functional, suggesting these characteristics could serve as pragmatic rules of thumb for identifying candidate sequences likely to be under selection. This study also provides a framework to identify novel genes using comparative transcriptomic data to improve genome annotation that is fundamental for connecting genotype to phenotype in crop and model systems.

Coexpression Analysis Identifies Two Oxidoreductases Involved in the Biosynthesis of the Monoterpene Acid Moiety of Natural Pyrethrin Insecticides in Tanacetum cinerariifolium.

Flowers of Tanacetum cinerariifolium produce a set of compounds known collectively as pyrethrins, which are commercially important pesticides that are strongly toxic to flying insects but not to most vertebrates. A pyrethrin molecule is an ester consisting of either trans-chrysanthemic acid or its modified form, pyrethric acid, and one of three alcohols, jasmolone, pyrethrolone, and cinerolone, that appear to be derived from jasmonic acid. Chrysanthemyl diphosphate synthase (CDS), the first enzyme involved in the synthesis of trans-chrysanthemic acid, was characterized previously and its gene isolated. TcCDS produces free trans-chrysanthemol in addition to trans-chrysanthemyl diphosphate, but the enzymes responsible for the conversion of trans-chrysanthemol to the corresponding aldehyde and then to the acid have not been reported. We used an RNA sequencing-based approach and coexpression correlation analysis to identify several candidate genes encoding putative trans-chrysanthemol and trans-chrysanthemal dehydrogenases. We functionally characterized the proteins encoded by these genes using a combination of in vitro biochemical assays and heterologous expression in planta to demonstrate that TcADH2 encodes an enzyme that oxidizes trans-chrysanthemol to trans-chrysanthemal, while TcALDH1 encodes an enzyme that oxidizes trans-chrysanthemal into trans-chrysanthemic acid. Transient coexpression of TcADH2 and TcALDH1 together with TcCDS in Nicotiana benthamiana leaves results in the production of trans-chrysanthemic acid as well as several other side products. The majority (58%) of trans-chrysanthemic acid was glycosylated or otherwise modified. Overall, these data identify key steps in the biosynthesis of pyrethrins and demonstrate the feasibility of metabolic engineering to produce components of these defense compounds in a heterologous host.