A Global Coexpression Network Approach for Connecting Genes to Specialized Metabolic Pathways in Plants

Identification of UDP-dependent glycosyltransferases in the wallflower cardenolide biosynthesis pathway

Cardenolides are potent plant defensive metabolites that have been studied for decades for their significance in plant-insect interactions and their use in treating heart failure in humans. With recent advancements in genome and transcriptome sequencing, genes in the cardenolide biosynthetic pathway have begun to be identified. Here we employed gene co-expression network analysis using published data from the cardenolide-producing plant Erysimum cheiranthoides (wormseed wallflower) to identify two UDP-dependent glycosyltransferases, UGT73C44 and UGT73C45, that are capable of glucosylating the aglycone cardenolide digitoxigenin, as well as other predicted cardenolide pathway intermediates. In vitro and in planta assays revealed that UGT73C44 acted on cardenolide pathway intermediates with a low Km value of 7.0 μM for digitoxigenin, while UGT73C45 displayed broader substrate specificity in vitro and could glucosylate diverse steroid and flavonoid substrates. A phylogeny and comparisons of structural models of UGT73C44 and UGT73C45 suggest that the enzymes have divergent active site architectures, which may account for their different substrate specificities. These data report the first plant-derived UGT specific to cardenolides, advancing our understanding of cardenolide biosynthesis and the enzymes that drive specialized metabolite diversity. These findings lay the foundation for future efforts to reconstitute the cardenolide pathway in heterologous systems and design cardenolide analogs with the potential for improved therapeutic properties.

Time-course dual RNA-seq analyses and gene identification during early stages of plant-Phytophthora infestans interactions

Late blight caused by Phytophthora infestans is a major threat to global potato and tomato production. Sustainable management of late blight requires the development of resistant crop cultivars. This process can be facilitated by high-throughput identification of functional genes involved in late blight pathogenesis. In this study, we generated a high-quality transcriptomic time-course dataset focusing on the initial 24 h of contact between P. infestans and 3 solanaceous plant species, tobacco (Nicotiana benthamiana), tomato (Solanum lycopersicum), and potato (Solanum tuberosum). Our results demonstrate species-specific transcriptional regulation in early stages of the infection. Transient silencing of putative RIBOSE-5-PHOSPHATE ISOMERASE and HMG-CoA REDUCTASE genes in N. benthamiana lowered plant resistance against P. infestans. Furthermore, heterologous expression of a putative tomato Golgi-localized nucleosugar transporter-encoding gene exacerbated P. infestans infection of N. benthamiana. In comparison, bioassays using transgenic tomato lines showed that the quantitative disease resistance genes were required but insufficient for late blight resistance; genetic knock-out of the susceptibility gene enhanced resistance. The same RNA-seq dataset was exploited to examine the transcriptional landscape of P. infestans and revealed host-specific gene expression patterns in the pathogen. This temporal transcriptomic diversity, in combination with genomic distribution features, identified the P. infestans IPI-B family GLYCINE-RICH PROTEINs as putative virulence factors that promoted disease severity or induced plant tissue necrosis when transiently expressed in N. benthamiana. These functional genes underline the effectiveness of functional gene-mining through a time-course dual RNA-seq approach and provide insight into the molecular interactions between solanaceous plants and P. infestans.

Functional genomic perspectives on plant terrestrialization

Plant evolutionary research has made leaps in exploring the deep evolutionary roots of embryophytes. A solid phylogenomic framework was established, allowing evolutionary inferences. Comparative genomic approaches revealed that many genes coding for transcription factors, morphogenetic regulators, specialized metabolic enzymes, phytohormone signaling, and more are not innovations of land plants but have a deep streptophyte algal ancestry. Are these just spurious homologs, or do they actualize traits we deem important in embryophytes? Building on streptophyte algae genome data, current endeavors delve into the functional significance of whole cohorts of homologs by leveraging the power of comparative high-throughput approaches. This ushered in the identification of recurrent themes in function, ultimately providing a functional genomic definition for the toolkit of plant terrestrialization.

Multi-omics analysis reveals tissue-specific biosynthesis and accumulation of diterpene alkaloids in Aconitum japonicum

Aconitum japonicum, native to the mountainous regions of Japan, is a toxic perennial plant widely recognized for its therapeutic potential. Despite its pharmacological importance, the complete biosynthetic pathway of diterpene alkaloids, bioactive compounds with significant pharmaceutical implications and derived from Aconitum species, remains elusive. In this study, leveraging high-throughput metabolome and transcriptome analyses, we conducted a comprehensive investigation using four tissues of A. japonicum, including leaf, mother root, daughter root, and rootlet. By integrating these multi-omics datasets, we achieved a holistic insight into the gene expression patterns and metabolite profiles intricately linked with diterpene alkaloid biosynthesis. Our findings unveil potential regulatory networks and pinpoint key candidate genes pivotal in diterpene alkaloid synthesis. Through comparative analyses across tissues, we delineate tissue-specific variations in gene expression and metabolite accumulation, shedding light on the spatial regulation of these biosynthetic pathways within the plant. Furthermore, this study contributes to a deeper understanding of the molecular mechanisms dictating the production of diterpene alkaloids in A. japonicum. Besides advancing our knowledge of plant secondary metabolism in A. japonicum, this study also provides a high-quality multi-omics resource for future studies aimed at functionally characterizing the target genes involved in different metabolic processes.

Evolution of plant specialized metabolites: beyond ecological drivers

Plants produce a highly diverse array of specialized metabolites. Traditionally, the evolution of these metabolites has been studied primarily through the lens of plants' ecological interactions with herbivores, pathogens, and pollinators, as many of them exhibit defense and/or attraction functions. However, increasing evidence suggests that many specialized metabolites, along with their precursors, also act as cellular signals that regulate cell growth and differentiation. We propose that these intrinsic functions are at least equally important factors in shaping the evolution of plant chemical defenses. We further discuss how future research that combines modern single-cell techniques and evolutionary genomics will provide novel insights into the evolutionary process of specialized metabolism diversification.

The aerial epidermis is a major site of quinolizidine alkaloid biosynthesis in narrow-leafed lupin

Lupins are promising protein crops that accumulate toxic quinolizidine alkaloids (QAs) in the seeds, complicating their end-use. QAs are synthesized in green organs (leaves, stems, and pods) and a subset of them is transported to the seeds during fruit development. The exact sites of biosynthesis and accumulation remain unknown; however, mesophyll cells have been proposed as sources, and epidermal cells as sinks. We investigated the exact sites of QA biosynthesis and accumulation in biosynthetic organs of narrow-leafed lupin (Lupinus angustifolius) using mass spectrometry-based imaging (MSI), laser-capture microdissection coupled to RNA-Seq, and precursor feeding studies coupled to LC-MS and MSI. We found that the QAs that accumulate in seeds ('core' QAs) were evenly distributed across tissues; however, their esterified versions accumulated primarily in the epidermis. Surprisingly, RNA-Seq revealed strong biosynthetic gene expression in the epidermis, which was confirmed in leaves by quantitative real-time polymerase chain reaction. Finally, feeding studies using a stably labeled precursor showed that the lower leaf epidermis is highly biosynthetic. Our results indicate that the epidermis is a major site of QA biosynthesis in narrow-leafed lupin, challenging the current assumptions. Our work has direct implications for the elucidation of the QA biosynthesis pathway and the long-distance transport network from source to seed.

Homospermidine synthase evolution and the origin(s) of pyrrolizidine alkaloids in Apocynaceae

PREMISE

Enzymes that are encoded by paralogous genes and produce identical specialized metabolites in distantly related plant lineages are strong evidence of parallel phenotypic evolution. Inference of phenotypic homology for metabolites produced by orthologous genes is less straightforward, since orthologs may be recruited in parallel into novel pathways. In prior research on pyrrolizidine alkaloids (PAs), specialized metabolites of Apocynaceae, the evolution of homospermidine synthase (HSS), an enzyme of PA biosynthesis, was reconstructed and a single origin of PAs inferred because HSS enzymes of all known PA-producing Apocynaceae species are orthologous and descended from an ancestral enzyme with the motif (VXXXD) of an optimized HSS.

METHODS

We increased sampling, tested the effect of amino acid motif on HSS function, revisited motif evolution, and tested for selection to infer evolution of HSS function and its correlation with phenotype.

RESULTS

Some evidence supports a single origin of PAs: an IXXXD HSS-like gene, similar in function to VXXXD HSS, evolved in the shared ancestor of all PA-producing species; loss of HSS function occurred multiple times via pseudogenization and perhaps via evolution of an IXXXN motif. Other evidence indicates multiple origins: the VXXXD motif, highly correlated with the PA phenotype, evolved two or four times independently; the ancestral IXXXD gene was not under positive selection, while some VXXXD genes were; and substitutions at sites experiencing positive selection occurred on multiple branches in the HSS-like gene tree.

CONCLUSIONS

The complexity of the genotype-function-phenotype map confounds the inference of PA homology from HSS-like gene evolution in Apocynaceae.

Decoding resilience: ecology, regulation, and evolution of biosynthetic gene clusters

Secondary metabolism is crucial for plant survival and can generate chemistry with nutritional, therapeutic, and industrial value. Biosynthetic genes of selected secondary metabolites cluster within localised chromosomal regions. The arrangement of these biosynthetic gene clusters (BGCs) challenges the long-held model of random gene order in eukaryotes, raising questions about their regulation, ecological significance, and evolution. In this review, we address these questions by exploring the contribution of BGCs to ecologically relevant plant-biotic interactions, while also evaluating the molecular-(epi)genetic mechanisms controlling their coordinated stress- and tissue-specific expression. Based on evidence that BGCs have distinct chromatin signatures and are enriched with transposable elements (TEs), we integrate emerging hypotheses into an updated evolutionary model emphasising how stress-induced epigenetic processes have shaped BGC formation.

Pairing omics to decode the diversity of plant specialized metabolism

Plants have evolved complex bouquets of specialized natural products that are utilized in medicine, agriculture, and industry. Untargeted natural product discovery has benefitted from growing plant omics data resources. Yet, plant genome complexity limits the identification and curation of biosynthetic pathways via single omics. Pairing multi-omics types within experiments provides multiple layers of evidence for biosynthetic pathway mining. The extraction of paired biological information facilitates connecting genes to transcripts and metabolites, especially when captured across time points, conditions and chemotypes. Experimental design requires specific adaptations to enable effective paired-omics analysis. Ultimately, metadata standards are required to support the integration of paired and unpaired public datasets and to accelerate collaborative efforts for natural product discovery in the plant research community.

Evolution of plant metabolism: the state-of-the-art

Immense chemical diversity is one of the hallmark features of plants. This chemo-diversity is mainly underpinned by a highly complex and biodiverse biochemical machinery. Plant metabolic enzymes originated and were inherited from their eukaryotic and prokaryotic ancestors and further diversified by the unprecedentedly high rates of gene duplication and functionalization experienced in land plants. Unlike prokaryotic microbes, which display frequent horizontal gene transfer events and multiple inputs of energy and organic carbon, land plants predominantly rely on organic carbon generated from CO2 and have experienced relatively few gene transfers during their recent evolutionary history. As such, plant metabolic networks have evolved in a stepwise manner using existing networks as a starting point and under various evolutionary constraints. That said, until recently, the evolution of only a handful of metabolic traits had been extensively investigated and as such, the evolution of metabolism has received a fraction of the attention of, the evolution of development, for example. Advances in metabolomics and next-generation sequencing have, however, recently led to a deeper understanding of how a wide range of plant primary and specialized (secondary) metabolic pathways have evolved both as a consequence of natural selection and of domestication and crop improvement processes. This article is part of the theme issue 'The evolution of plant metabolism'.

A developmental gradient reveals biosynthetic pathways to eukaryotic toxins in monocot geophytes

Numerous eukaryotic toxins that accumulate in geophytic plants are valuable in the clinic, yet their biosynthetic pathways have remained elusive. A notable example is the >150 Amaryllidaceae alkaloids (AmAs), including galantamine, an FDA-approved treatment for Alzheimer's disease. We show that while AmAs accumulate to high levels in many daffodil tissues, biosynthesis is localized to nascent, growing tissue at the leaf base. A similar trend is found in the production of steroidal alkaloids (e.g., cyclopamine) in corn lily. This model of active biosynthesis enabled the elucidation of a complete set of biosynthetic genes that can be used to produce AmAs. Taken together, our work sheds light on the developmental and enzymatic logic of diverse alkaloid biosynthesis in daffodils. More broadly, it suggests a paradigm for biosynthesis regulation in monocot geophytes, where plants are protected from herbivory through active charging of newly formed cells with eukaryotic toxins that persist as above-ground tissue develops.

CLOCI: unveiling cryptic fungal gene clusters with generalized detection

Gene clusters are genomic loci that contain multiple genes that are functionally and genetically linked. Gene clusters collectively encode diverse functions, including small molecule biosynthesis, nutrient assimilation, metabolite degradation, and production of proteins essential for growth and development. Identifying gene clusters is a powerful tool for small molecule discovery and provides insight into the ecology and evolution of organisms. Current detection algorithms focus on canonical 'core' biosynthetic functions many gene clusters encode, while overlooking uncommon or unknown cluster classes. These overlooked clusters are a potential source of novel natural products and comprise an untold portion of overall gene cluster repertoires. Unbiased, function-agnostic detection algorithms therefore provide an opportunity to reveal novel classes of gene clusters and more precisely define genome organization. We present CLOCI (Co-occurrence Locus and Orthologous Cluster Identifier), an algorithm that identifies gene clusters using multiple proxies of selection for coordinated gene evolution. Our approach generalizes gene cluster detection and gene cluster family circumscription, improves detection of multiple known functional classes, and unveils non-canonical gene clusters. CLOCI is suitable for genome-enabled small molecule mining, and presents an easily tunable approach for delineating gene cluster families and homologous loci.

Pangenome Identification and Analysis of Terpene Synthase Gene Family Members in Gossypium

Terpene synthases (TPSs), key gatekeepers in the biosynthesis of herbivore-induced terpenes, are pivotal in the diversity of terpene chemotypes across and within plant species. Here, we constructed a gene-based pangenome of the Gossypium genus by integrating the genomes of 17 diploid and 10 tetraploid species. Within this pangenome, 208 TPS syntelog groups (SGs) were identified, comprising 2 core SGs (TPS5 and TPS42) present in all 27 analyzed genomes, 6 softcore SGs (TPS11, TPS12, TPS13, TPS35, TPS37, and TPS47) found in 25 to 26 genomes, 131 dispensable SGs identified in 2 to 24 genomes, and 69 private SGs exclusive to a single genome. The mutational load analysis of these identified TPS genes across 216 cotton accessions revealed a great number of splicing variants and complex splicing patterns. The nonsynonymous/synonymous Ka/Ks value for all 52 analyzed TPS SGs was less than one, indicating that these genes were subject to purifying selection. Of 208 TPS SGs encompassing 1795 genes, 362 genes derived from 102 SGs were identified as atypical and truncated. The structural analysis of TPS genes revealed that gene truncation is a major mechanism contributing to the formation of atypical genes. An integrated analysis of three RNA-seq datasets from cotton plants subjected to herbivore infestation highlighted nine upregulated TPSs, which included six previously characterized TPSs in G. hirsutum (AD1_TPS10, AD1_TPS12, AD1_TPS40, AD1_TPS42, AD1_TPS89, and AD1_TPS104), two private TPSs (AD1_TPS100 and AD2_TPS125), and one atypical TPS (AD2_TPS41). Also, a TPS-associated coexpression module of eight genes involved in the terpenoid biosynthesis pathway was identified in the transcriptomic data of herbivore-infested G. hirsutum. These findings will help us understand the contributions of TPS family members to interspecific terpene chemotypes within Gossypium and offer valuable resources for breeding insect-resistant cotton cultivars.

Multiomic analyses reveal key sectors of jasmonate-mediated defense responses in rice

The phytohormone jasmonate (JA) plays a central role in plant defenses against biotic stressors. However, our knowledge of the JA signaling pathway in rice (Oryza sativa) remains incomplete. Here, we integrated multiomic data from three tissues to characterize the functional modules involved in organizing JA-responsive genes. In the core regulatory sector, MYC2 transcription factor transcriptional cascades are conserved in different species but with distinct regulators (e.g. bHLH6 in rice), in which genes are early expressed across all tissues. In the feedback sector, MYC2 also regulates the expression of JA repressor and catabolic genes, providing negative feedback that truncates the duration of JA responses. For example, the MYC2-regulated NAC (NAM, ATAF1/2, and CUC2) transcription factor genes NAC1, NAC3, and NAC4 encode proteins that repress JA signaling and herbivore resistance. In the tissue-specific sector, many late-expressed genes are associated with the biosynthesis of specialized metabolites that mediate particular defensive functions. For example, the terpene synthase gene TPS35 is specifically induced in the leaf sheath and TPS35 functions in defense against oviposition by brown planthoppers and the attraction of this herbivore's natural enemies. Thus, by characterizing core, tissue-specific, and feedback sectors of JA-elicited defense responses, this work provides a valuable resource for future discoveries of key JA components in this important crop.

Understanding the chemical language mediating maize immunity and environmental adaptation

Diverse networks of specialized metabolites promote plant fitness by mediating beneficial and antagonistic environmental interactions. In maize (Zea mays), constitutive and dynamically formed cocktails of terpenoids, benzoxazinoids, oxylipins, and phenylpropanoids contribute to plant defense and ecological adaptation. Recent research has highlighted the multifunctional nature of many specialized metabolites, serving not only as elaborate chemical defenses that safeguard against biotic and abiotic stress but also as regulators in adaptive developmental processes and microbiome interactions. Great strides have also been made in identifying the modular pathway networks that drive maize chemical diversity. Translating this knowledge into strategies for enhancing stress resilience traits has the potential to address climate-driven yield losses in one of the world's major food, feed, and bioenergy crops.

Beyond co-expression: pathway discovery for plant pharmaceuticals

Plant natural products have been an important source of medicinal molecules since ancient times. To gain access to the whole diversity of these molecules for pharmaceutical applications, it is important to understand their biosynthetic origins. Whilst co-expression is a reliable tool for identifying gene candidates, a variety of complementary methods can aid in screening or refining candidate selection. Here, we review recently employed plant biosynthetic pathway discovery approaches, and highlight future directions in the field.

Triumphs and Challenges of Natural Product Discovery in the Postgenomic Era

Natural products have played significant roles as medicine and food throughout human history. Here, we first provide a brief historical overview of natural products, their classification and biosynthetic origins, and the microbiological and genetic methods used for their discovery. We also describe and discuss the technologies that revolutionized the field, which transitioned from classic genetics to genome-centric discovery approximately two decades ago. We then highlight the most recent advancements and approaches in the current postgenomic era, in which genome mining is a standard operation and high-throughput analytical methods allow parallel discovery of genes and molecules at an unprecedented pace. Finally, we discuss the new challenges faced by the field of natural products and the future of systematic heterologous expression and strain-independent discovery, which promises to deliver more molecules in vials than ever before.

Prioritizing Maize Metabolic Gene Regulators through Multi-Omic Network Integration

Elucidating gene regulatory networks is a major area of study within plant systems biology. Phenotypic traits are intricately linked to specific gene expression profiles. These expression patterns arise primarily from regulatory connections between sets of transcription factors (TFs) and their target genes. Here, we integrated 46 co-expression networks, 283 protein-DNA interaction (PDI) assays, and 16 million SNPs used to identify expression quantitative trait loci (eQTL) to construct TF-target networks. In total, we analyzed ∼4.6M interactions to generate four distinct types of TF-target networks: co-expression, PDI, trans -eQTL, and cis -eQTL combined with PDIs. To functionally annotate TFs based on their target genes, we implemented three different network integration strategies. We evaluated the effectiveness of each strategy through TF loss-of function mutant inspection and random network analyses. The multi-network integration allowed us to identify transcriptional regulators of several biological processes. Using the topological properties of the fully integrated network, we identified potential functionally redundant TF paralogs. Our findings retrieved functions previously documented for numerous TFs and revealed novel functions that are crucial for informing the design of future experiments. The approach here-described lays the foundation for the integration of multi-omic datasets in maize and other plant systems.

GRAPHICAL ABSTRACT

Recent advances in exploring transcriptional regulatory landscape of crops

Crop breeding entails developing and selecting plant varieties with improved agronomic traits. Modern molecular techniques, such as genome editing, enable more efficient manipulation of plant phenotype by altering the expression of particular regulatory or functional genes. Hence, it is essential to thoroughly comprehend the transcriptional regulatory mechanisms that underpin these traits. In the multi-omics era, a large amount of omics data has been generated for diverse crop species, including genomics, epigenomics, transcriptomics, proteomics, and single-cell omics. The abundant data resources and the emergence of advanced computational tools offer unprecedented opportunities for obtaining a holistic view and profound understanding of the regulatory processes linked to desirable traits. This review focuses on integrated network approaches that utilize multi-omics data to investigate gene expression regulation. Various types of regulatory networks and their inference methods are discussed, focusing on recent advancements in crop plants. The integration of multi-omics data has been proven to be crucial for the construction of high-confidence regulatory networks. With the refinement of these methodologies, they will significantly enhance crop breeding efforts and contribute to global food security.

Cardiac glycosides protect wormseed wallflower (Erysimum cheiranthoides) against some, but not all, glucosinolate-adapted herbivores

The chemical arms race between plants and insects is foundational to the generation and maintenance of biological diversity. We asked how the evolution of a novel defensive compound in an already well-defended plant lineage impacts interactions with diverse herbivores. Erysimum cheiranthoides (Brassicaceae), which produces both ancestral glucosinolates and novel cardiac glycosides, served as a model. We analyzed gene expression to identify cardiac glycoside biosynthetic enzymes in E. cheiranthoides and characterized these enzymes via heterologous expression and CRISPR/Cas9 knockout. Using E. cheiranthoides cardiac glycoside-deficient lines, we conducted insect experiments in both the laboratory and field. EcCYP87A126 initiates cardiac glycoside biosynthesis via sterol side-chain cleavage, and EcCYP716A418 has a role in cardiac glycoside hydroxylation. In EcCYP87A126 knockout lines, cardiac glycoside production was eliminated. Laboratory experiments with these lines revealed that cardiac glycosides were highly effective defenses against two species of glucosinolate-tolerant specialist herbivores, but did not protect against all crucifer-feeding specialist herbivores in the field. Cardiac glycosides had lesser to no effect on two broad generalist herbivores. These results begin elucidation of the E. cheiranthoides cardiac glycoside biosynthetic pathway and demonstrate in vivo that cardiac glycoside production allows Erysimum to escape from some, but not all, specialist herbivores.

Harnessing the predicted maize pan-interactome for putative gene function prediction and prioritization of candidate genes for important traits

The recent assembly and annotation of the 26 maize nested association mapping population founder inbreds have enabled large-scale pan-genomic comparative studies. These studies have expanded our understanding of agronomically important traits by integrating pan-transcriptomic data with trait-specific gene candidates from previous association mapping results. In contrast to the availability of pan-transcriptomic data, obtaining reliable protein-protein interaction (PPI) data has remained a challenge due to its high cost and complexity. We generated predicted PPI networks for each of the 26 genomes using the established STRING database. The individual genome-interactomes were then integrated to generate core- and pan-interactomes. We deployed the PPI clustering algorithm ClusterONE to identify numerous PPI clusters that were functionally annotated using gene ontology (GO) functional enrichment, demonstrating a diverse range of enriched GO terms across different clusters. Additional cluster annotations were generated by integrating gene coexpression data and gene description annotations, providing additional useful information. We show that the functionally annotated PPI clusters establish a useful framework for protein function prediction and prioritization of candidate genes of interest. Our study not only provides a comprehensive resource of predicted PPI networks for 26 maize genomes but also offers annotated interactome clusters for predicting protein functions and prioritizing gene candidates. The source code for the Python implementation of the analysis workflow and a standalone web application for accessing the analysis results are available at https://github.com/eporetsky/PanPPI.

Machine learning assists prediction of genes responsible for plant specialized metabolite biosynthesis by integrating multi-omics data

BACKGROUND

Plant specialized (or secondary) metabolites (PSM), also known as phytochemicals, natural products, or plant constituents, play essential roles in interactions between plants and environment. Although many research efforts have focused on discovering novel metabolites and their biosynthetic genes, the resolution of metabolic pathways and identified biosynthetic genes was limited by rudimentary analysis approaches and enormous number of candidate genes.

RESULTS

Here we integrated state-of-the-art automated machine learning (ML) frame AutoGluon-Tabular and multi-omics data from Arabidopsis to predict genes encoding enzymes involved in biosynthesis of plant specialized metabolite (PSM), focusing on the three main PSM categories: terpenoids, alkaloids, and phenolics. We found that the related features of genomics and proteomics were the top two crucial categories of features contributing to the model performance. Using only these key features, we built a new model in Arabidopsis, which performed better than models built with more features including those related with transcriptomics and epigenomics. Finally, the built models were validated in maize and tomato, and models tested for maize and trained with data from two other species exhibited either equivalent or superior performance to intraspecies predictions.

CONCLUSIONS

Our external validation results in grape and poppy on the one hand implied the applicability of our model to the other species, and on the other hand showed enormous potential to improve the prediction of enzymes synthesizing PSM with the inclusion of valid data from a wider range of species.

StCoExpNet: a global co-expression network analysis facilitates identifying genes underlying agronomic traits in potatoes

KEY MESSAGE

We constructed a gene expression atlas and co-expression network for potatoes and identified several novel genes associated with various agronomic traits. This resource will accelerate potato genetics and genomics research. Potato (Solanum tuberosum L.) is the world's most crucial non-cereal food crop and ranks third in food production after wheat and rice. Despite the availability of several potato transcriptome datasets at public databases like NCBI SRA, an effort has yet to be put into developing a global transcriptome atlas and a co-expression network for potatoes. The objectives of our study were to construct a global expression atlas for potatoes using publicly available transcriptome datasets, identify housekeeping and tissue-specific genes, construct a global co-expression network and identify co-expression clusters, investigate the transcriptional complexity of genes involved in various essential biological processes related to agronomic traits, and provide a web server (StCoExpNet) to easily access the newly constructed expression atlas and co-expression network to investigate the expression and co-expression of genes of interest. In this study, we used data from 2299 publicly available potato transcriptome samples obtained from 15 different tissues to construct a global transcriptome atlas. We found that roughly 87% of the annotated genes exhibited detectable expression in at least one sample. Among these, we identified 281 genes with consistent and stable expression levels, indicating their role as housekeeping genes. Conversely, 308 genes exhibited marked tissue-specific expression patterns. We exemplarily linked some co-expression clusters to important agronomic traits of potatoes, such as self-incompatibility, anthocyanin biosynthesis, tuberization, and defense responses against multiple pathogens. The dataset compiled here constitutes a new resource (StCoExpNet), which can be accessed at https://stcoexpnet.julius-kuehn.de . This transcriptome atlas and the co-expression network will accelerate potato genetics and genomics research.

Four enzymes control natural variation in the steroid core of Erysimum cardenolides

Plants commonly produce families of structurally related metabolites with similar defensive functions. This apparent redundancy raises the question of underlying molecular mechanisms and adaptive benefits of such chemical variation. Cardenolides, a class defensive compounds found in the wallflower genus Erysimum (L., Brassicaceae) and scattered across other plant families, show substantial structural variation, with glycosylation and hydroxylation being common modifications of a steroid core, which itself may vary in terms of stereochemistry and saturation. Through a combination of chemical mutagenesis and analysis of gene coexpression networks, we identified four enzymes involved in cardenolide biosynthesis in Erysimum that work together to determine stereochemistry at carbon 5 of the steroid core: Ec3βHSD, a 3β-hydroxysteroid dehydrogenase, Ec3KSI, a ketosteroid isomerase, EcP5βR2, a progesterone 5β-reductase, and EcDET2, a steroid 5α-reductase. We biochemically characterized the activity of these enzymes in vitro and generated CRISPR/Cas9 knockout lines to confirm activity in vivo. Cardenolide biosynthesis was not eliminated in any of the knockouts. Instead, mutant plants accumulated cardenolides with altered saturation and stereochemistry of the steroid core. Furthermore, we found variation in carbon 5 configuration among the cardenolides of 44 species of Erysimum, where the occurrence of some 5β-cardenolides is associated with the expression and sequence of P5βR2. This may have allowed Erysimum species to fine-tune their defensive profiles to target specific herbivore populations over the course of evolution.

SIGNIFICANCE STATEMENT

Plants use an array of toxic compounds to defend themselves from attack against insects and other herbivores. One mechanism through which plants may evolve more toxic compounds is through modifications to the structure of compounds they already produce. In this study, we show how plants in the wallflower genus Erysimum use four enzymes to fine-tune the structure of toxic metabolites called cardenolides. Natural variation in the sequence and expression of a single enzyme called progesterone 5β-reductase 2 partly explains the variation in cardenolides observed across the Erysimum genus. These alterations to cardenolide structure over the course of evolution suggests that there may be context-dependent benefits to Erysimum to invest in one cardenolide variant over another.

Aphid Resistance Segregates Independently of Cardenolide and Glucosinolate Content in an Erysimum cheiranthoides (Wormseed Wallflower) F2 Population

Plants in the genus Erysimum produce both glucosinolates and cardenolides as a defense mechanism against herbivory. Two natural isolates of Erysimum cheiranthoides (wormseed wallflower) differed in their glucosinolate content, cardenolide content, and their resistance to Myzus persicae (green peach aphid), a broad generalist herbivore. Both classes of defensive metabolites were produced constitutively and were not further induced by aphid feeding. To investigate the relative importance of glucosinolates and cardenolides in E. cheiranthoides defense, we generated an improved genome assembly, genetic map, and segregating F2 population. The genotypic and phenotypic analysis of the F2 plants identified quantitative trait loci, which affected glucosinolates and cardenolides, but not the aphid resistance. The abundance of most glucosinolates and cardenolides was positively correlated in the F2 population, indicating that similar processes regulate their biosynthesis and accumulation. Aphid reproduction was positively correlated with glucosinolate content. Although the overall cardenolide content had little effect on aphid growth and survival, there was a negative correlation between aphid reproduction and helveticoside abundance. However, this variation in defensive metabolites could not explain the differences in aphid growth on the two parental lines, suggesting that processes other than the abundance of glucosinolates and cardenolides have a predominant effect on aphid resistance in E. cheiranthoides.

CryptoCEN: A Co-Expression Network for Cryptococcus neoformans reveals novel proteins involved in DNA damage repair

Elucidating gene function is a major goal in biology, especially among non-model organisms. However, doing so is complicated by the fact that molecular conservation does not always mirror functional conservation, and that complex relationships among genes are responsible for encoding pathways and higher-order biological processes. Co-expression, a promising approach for predicting gene function, relies on the general principal that genes with similar expression patterns across multiple conditions will likely be involved in the same biological process. For Cryptococcus neoformans, a prevalent human fungal pathogen greatly diverged from model yeasts, approximately 60% of the predicted genes in the genome lack functional annotations. Here, we leveraged a large amount of publicly available transcriptomic data to generate a C. neoformans Co-Expression Network (CryptoCEN), successfully recapitulating known protein networks, predicting gene function, and enabling insights into the principles influencing co-expression. With 100% predictive accuracy, we used CryptoCEN to identify 13 new DNA damage response genes, underscoring the utility of guilt-by-association for determining gene function. Overall, co-expression is a powerful tool for uncovering gene function, and decreases the experimental tests needed to identify functions for currently under-annotated genes.

Aphid resistance segregates independently of cardiac glycoside and glucosinolate content in an Erysimum cheiranthoides (wormseed wallflower) F2 population

Plants in the genus Erysimum produce both glucosinolates and cardiac glycosides as defense against herbivory. Two natural isolates of Erysimum cheiranthoides (wormseed wallflower) differed in their glucosinolate content, cardiac glycoside content, and resistance to Myzus persicae (green peach aphid), a broad generalist herbivore. Both classes of defensive metabolites were produced constitutively and were not induced further by aphid feeding. To investigate the relative importance of glucosinolates and cardiac glycosides in E. cheiranthoides defense, we generated an improved genome assembly, genetic map, and segregating F2 population. Genotypic and phenotypic analysis of the F2 plants identified quantitative trait loci affecting glucosinolates and cardiac glycosides, but not aphid resistance. The abundance of most glucosinolates and cardiac glycosides was positively correlated in the F2 population, indicating that similar processes regulate their biosynthesis and accumulation. Aphid reproduction was positively correlated with glucosinolate content. Although overall cardiac glycoside content had little effect on aphid growth and survival, there was a negative correlation between aphid reproduction and helveticoside abundance. However, this variation in defensive metabolites could not explain the differences in aphid growth on the two parental lines, suggesting that processes other than the abundance of glucosinolates and cardiac glycosides have a predominant effect on aphid resistance in E. cheiranthoides.

Gene Co-expression Network Analysis to Mine Genes Involved in Plant Secondary Metabolite Biosynthesis and Regulation

Plants produce diverse specialized metabolites (SMs) that do not participate in plant growth and development but help them adapt to various environmental conditions. In addition to aiding in plant adaptation, different SMs serve as active ingredients for pharmaceutical and cosmetics products. However, despite their significant role in plant adaptation and industrial importance, the genes involved in the biosynthesis and regulation of many SMs remain largely unknown. This hinders deciphering the specific role of SMs in plant adaptation and limits their industrial utilization. Since many SMs pathway genes are expected to act in tight association with each other within a coexpression network, the network biology approach, such as weighted gene coexpression network analysis, could be used to identify the unknown genes. This chapter describes a workflow for constructing a gene coexpression network to identify genes that could be associated with the biosynthesis and regulation of SMs.

Inferring Interaction Networks from Transcriptomic Data: Methods and Applications

Transcriptomic data is a treasure trove in modern molecular biology, as it offers a comprehensive viewpoint into the intricate nuances of gene expression dynamics underlying biological systems. This genetic information must be utilized to infer biomolecular interaction networks that can provide insights into the complex regulatory mechanisms underpinning the dynamic cellular processes. Gene regulatory networks and protein-protein interaction networks are two major classes of such networks. This chapter thoroughly investigates the wide range of methodologies used for distilling insightful revelations from transcriptomic data that include association-based methods (based on correlation among expression vectors), probabilistic models (using Bayesian and Gaussian models), and interologous methods. We reviewed different approaches for evaluating the significance of interactions based on the network topology and biological functions of the interacting molecules and discuss various strategies for the identification of functional modules. The chapter concludes with highlighting network-based techniques of prioritizing key genes, outlining the centrality-based, diffusion- based, and subgraph-based methods. The chapter provides a meticulous framework for investigating transcriptomic data to uncover assembly of complex molecular networks for their adaptable analyses across a broad spectrum of biological domains.

Cardiac glycosides protect wormseed wallflower (Erysimum cheiranthoides) against some, but not all, glucosinolate-adapted herbivores

The chemical arms race between plants and insects is foundational to the generation and maintenance of biological diversity. We asked how the evolution of a novel defensive compound in an already well-defended plant lineage impacts interactions with diverse herbivores. Erysimum cheiranthoides (Brassicaceae), which produces both ancestral glucosinolates and novel cardiac glycosides, served as a model.We analyzed gene expression to identify cardiac glycoside biosynthetic enzymes in E. cheiranthoides and characterized these enzymes via heterologous expression and CRISPR/Cas9 knockout. Using E. cheiranthoides cardiac glycoside-deficient lines, we conducted insect experiments in both the laboratory and field.EcCYP87A126 initiates cardiac glycoside biosynthesis via sterol side chain cleavage, and EcCYP716A418 has a role in cardiac glycoside hydroxylation. In EcCYP87A126 knockout lines, cardiac glycoside production was eliminated. Laboratory experiments with these lines revealed that cardiac glycosides were highly effective defenses against two species of glucosinolate-tolerant specialist herbivores but did not protect against all crucifer-feeding specialist herbivores in the field. Cardiac glycosides had lesser to no effect on two broad generalist herbivores.These results begin elucidation of the E. cheiranthoides cardiac glycoside biosynthetic pathway and demonstrate in vivo that cardiac glycoside production allows Erysimum to escape from some, but not all, specialist herbivores.

A persistent major mutation in canonical jasmonate signaling is embedded in an herbivory-elicited gene network

When insect herbivores attack plants, elicitors from oral secretions and regurgitants (OS) enter wounds during feeding, eliciting defense responses. These generally require plant jasmonate (JA) signaling, specifically, a jasmonoyl-L-isoleucine (JA-Ile) burst, for their activation and are well studied in the native tobacco Nicotiana attenuata. We used intraspecific diversity captured in a 26-parent MAGIC population planted in nature and an updated genome assembly to impute natural variation in the OS-elicited JA-Ile burst linked to a mutation in the JA-Ile biosynthetic gene NaJAR4. Experiments revealed that NaJAR4 variants were associated with higher fitness in the absence of herbivores but compromised foliar defenses, with two NaJAR homologues (4 and 6) complementing each other spatially and temporally. From decade-long seed collections of natural populations, we uncovered enzymatically inactive variants occurring at variable frequencies, consistent with a balancing selection regime maintaining variants. Integrative analyses of OS-induced transcriptomes and metabolomes of natural accessions revealed that NaJAR4 is embedded in a nonlinear complex gene coexpression network orchestrating responses to OS, which we tested by silencing four hub genes in two connected coexpressed networks and examining their OS-elicited metabolic responses. Lines silenced in two hub genes (NaGLR and NaFB67) co-occurring in the NaJAR4/6 module showed responses proportional to JA-Ile accumulations; two from an adjacent module (NaERF and NaFB61) had constitutively expressed defenses with high resistance. We infer that mutations with large fitness consequences can persist in natural populations due to compensatory responses from gene networks, which allow for diversification in conserved signaling pathways and are generally consistent with predictions of an omnigene model.

CryptoCEN: A Co-Expression Network for Cryptococcus neoformans reveals novel proteins involved in DNA damage repair

Elucidating gene function is a major goal in biology, especially among non-model organisms. However, doing so is complicated by the fact that molecular conservation does not always mirror functional conservation, and that complex relationships among genes are responsible for encoding pathways and higher-order biological processes. Co-expression, a promising approach for predicting gene function, relies on the general principal that genes with similar expression patterns across multiple conditions will likely be involved in the same biological process. For Cryptococcus neoformans, a prevalent human fungal pathogen greatly diverged from model yeasts, approximately 60% of the predicted genes in the genome lack functional annotations. Here, we leveraged a large amount of publicly available transcriptomic data to generate a C. neoformans Co-Expression Network (CryptoCEN), successfully recapitulating known protein networks, predicting gene function, and enabling insights into the principles influencing co-expression. With 100% predictive accuracy, we used CryptoCEN to identify 13 new DNA damage response genes, underscoring the utility of guilt-by-association for determining gene function. Overall, co-expression is a powerful tool for uncovering gene function, and decreases the experimental tests needed to identify functions for currently under-annotated genes.

Plant terpene specialized metabolism: complex networks or simple linear pathways?

From the perspectives of pathway evolution, discovery and engineering of plant specialized metabolism, the nature of the biosynthetic routes represents a critical aspect. Classical models depict biosynthesis typically from an end-point angle and as linear, for example, connecting central and specialized metabolism. As the number of functionally elucidated routes increased, the enzymatic foundation of complex plant chemistries became increasingly well understood. The perception of linear pathway models has been severely challenged. With a focus on plant terpenoid specialized metabolism, we review here illustrative examples supporting that plants have evolved complex networks driving chemical diversification. The completion of several diterpene, sesquiterpene and monoterpene routes shows complex formation of scaffolds and their subsequent functionalization. These networks show that branch points, including multiple sub-routes, mean that metabolic grids are the rule rather than the exception. This concept presents significant implications for biotechnological production.

A developmental gradient reveals biosynthetic pathways to eukaryotic toxins in monocot geophytes

Numerous eukaryotic toxins that accumulate in geophytic plants are valuable in the clinic, yet their biosynthetic pathways have remained elusive. A lead example is the >150 Amaryllidaceae alkaloids (AmAs) including galantamine, an FDA-approved treatment for Alzheimer's disease. We show that while AmAs accumulate to high levels in many tissues in daffodils, biosynthesis is localized to nascent, growing tissue at the base of leaves. A similar trend is found for the production of steroidal alkaloids (e.g. cyclopamine) in corn lily. This model of active biosynthesis enabled elucidation of a complete set of biosynthetic genes for the production of AmAs. Taken together, our work sheds light on the developmental and enzymatic logic of diverse alkaloid biosynthesis in daffodil. More broadly, it suggests a paradigm for biosynthesis regulation in monocot geophytes where plants are protected from herbivory through active charging of newly formed cells with eukaryotic toxins that persist as aboveground tissue develops.

"Simple Tidy GeneCoEx": A gene co-expression analysis workflow powered by tidyverse and graph-based clustering in R

Gene co-expression analysis is an effective method to detect groups (or modules) of co-expressed genes that display similar expression patterns, which may function in the same biological processes. Here, we present "Simple Tidy GeneCoEx", a gene co-expression analysis workflow written in the R programming language. The workflow is highly customizable across multiple stages of the pipeline including gene selection, edge selection, clustering resolution, and data visualization. Powered by the tidyverse package ecosystem and network analysis functions provided by the igraph package, the workflow detects gene co-expression modules whose members are highly interconnected. Step-by-step instructions with two use case examples as well as source code are available at https://github.com/cxli233/SimpleTidy_GeneCoEx.

Mining Chromodoris quadricolor symbionts for biosynthesis of novel secondary metabolites

Many secondary metabolites with medicinal potential are produced by various animals, plants, and microorganisms. Because marine creatures have a greater proportion of unexplored biodiversity than their terrestrial counterparts, they have emerged as a key research focus for the discovery of natural product drugs. Several studies have revealed that bacteria isolated from Chromodoris quadricolor (C. quadricolor) have antibiotic and anticancer properties. In this study, meta-transcriptomics and meta-proteimic analysis were combined to identify biosynthetic gene clusters (BGCs) in the symbiotic bacteria of the C. quadricolor mantle. Symbiotic bacteria were separated from the host by differential pelleting, and then total RNA was extracted, purified, and sequenced. Meta-transcriptomic analysis was done using different natural product mining tools to identify biosynthetic transcript clusters (BTCs). Furthermore, proteins were extracted from the same cells and then analyzed by LC-MS. A meta-proteomic analysis was performed to find proteins that are translated from BCGs. Finally, only 227 proteins have been translated from 40,742 BTCs. The majority of these clusters were polyketide synthases (PKSs) with antibacterial activity. Ten novel potential metabolic clusters with the ability to produce antibiotics have been identified in Novosphingobium and Microbacteriaceae, including members of the ribosomal synthesized and post-translationally modified peptides (RiPPs), polyketide synthases, and others. We realized that using a meta-proteomic approach to identify BGCs that have already been translated makes it easier to concentrate on BGCs that are utilized by bacteria. The symbiotic bacteria associated with C. quadricolor could be a source of novel antibiotics.

System network analysis of Rosmarinus officinalis transcriptome and metabolome-Key genes in biosynthesis of secondary metabolites

Medicinal plants contain valuable compounds that have attracted worldwide interest for their use in the production of natural drugs. The presence of compounds such as rosmarinic acid, carnosic acid, and carnosol in Rosmarinus officinalis has made it a plant with unique therapeutic effects. The identification and regulation of the biosynthetic pathways and genes will enable the large-scale production of these compounds. Hence, we studied the correlation between the genes involved in biosynthesis of the secondary metabolites in R. officinalis using proteomics and metabolomics data by WGCNA. We identified three modules as having the highest potential for the metabolite engineering. Moreover, the hub genes highly connected to particular modules, TFs, PKs, and transporters were identified. The TFs of MYB, C3H, HB, and C2H2 were the most likely candidates associated with the target metabolic pathways. The results indicated that the hub genes including Copalyl diphosphate synthase (CDS), Phenylalanine ammonia lyase (PAL), Cineole synthase (CIN), Rosmarinic acid synthase (RAS), Tyrosine aminotransferase (TAT), Cinnamate 4-hydroxylase (C4H), and MYB58 are responsible for biosynthesis of important secondary metabolites. Thus, we confirmed these results using qRT-PCR after treating R. officinalis seedlings with methyl jasmonate. These candidate genes may be employed for genetic and metabolic engineering research to increase R. officinalis metabolite production.

Gene coexpression networks allow the discovery of two strictosidine synthases underlying monoterpene indole alkaloid biosynthesis in Uncaria rhynchophylla

Plant-derived monoterpene indole alkaloids (MIAs) from Uncaria rhynchophylla (UR) have huge medicinal properties in treating Alzheimer's disease, Parkinson's disease, and depression. Although many bioactive UR-MIA products have been isolated as drugs, their biosynthetic pathway remains largely unexplored. In this study, untargeted metabolome identified 79 MIA features in UR tissues (leaf, branch stem, hook stem, and stem), of which 30 MIAs were differentially accumulated among different tissues. Short time series expression analysis captured 58 pathway genes and 12 hub regulators responsible for UR-MIA biosynthesis and regulation, which were strong links with main UR-MIA features. Coexpression networks further pointed to two strictosidine synthases (UrSTR1/5) that were coregulated with multiple MIA-related genes and highly correlated with UR-MIA features (r > 0.7, P < 0.005). Both UrSTR1/5 catalyzed the formation of strictosidine with tryptamine and secologanin as substrates, highlighting the importance of key residues (UrSTR1: Glu309, Tyr155; UrSTR5: Glu295, Tyr141). Further, overexpression of UrSTR1/5 in UR hairy roots constitutively increased the biosynthesis of bioactive UR-MIAs (rhynchophylline, isorhynchophylline, corynoxeine, etc), whereas RNAi of UrSTR1/5 significantly decreased UR-MIA biosynthesis. Collectively, our work not only provides candidates for reconstituting the biosynthesis of bioactive UR-MIAs in heterologous hosts but also highlights a powerful strategy for mining natural product biosynthesis in medicinal plants.

Analysis of Arabidopsis non-reference accessions reveals high diversity of metabolic gene clusters and discovers new candidate cluster members

Metabolic gene clusters (MGCs) are groups of genes involved in a common biosynthetic pathway. They are frequently formed in dynamic chromosomal regions, which may lead to intraspecies variation and cause phenotypic diversity. We examined copy number variations (CNVs) in four Arabidopsis thaliana MGCs in over one thousand accessions with experimental and bioinformatic approaches. Tirucalladienol and marneral gene clusters showed little variation, and the latter was fixed in the population. Thalianol and especially arabidiol/baruol gene clusters displayed substantial diversity. The compact version of the thalianol gene cluster was predominant and more conserved than the noncontiguous version. In the arabidiol/baruol cluster, we found a large genomic insertion containing divergent duplicates of the CYP705A2 and BARS1 genes. The BARS1 paralog, which we named BARS2, encoded a novel oxidosqualene synthase. The expression of the entire arabidiol/baruol gene cluster was altered in the accessions with the duplication. Moreover, they presented different root growth dynamics and were associated with warmer climates compared to the reference-like accessions. In the entire genome, paired genes encoding terpene synthases and cytochrome P450 oxidases were more variable than their nonpaired counterparts. Our study highlights the role of dynamically evolving MGCs in plant adaptation and phenotypic diversity.

Uncovering a miltiradiene biosynthetic gene cluster in the Lamiaceae reveals a dynamic evolutionary trajectory

The spatial organization of genes within plant genomes can drive evolution of specialized metabolic pathways. Terpenoids are important specialized metabolites in plants with diverse adaptive functions that enable environmental interactions. Here, we report the genome assemblies of Prunella vulgaris, Plectranthus barbatus, and Leonotis leonurus. We investigate the origin and subsequent evolution of a diterpenoid biosynthetic gene cluster (BGC) together with other seven species within the Lamiaceae (mint) family. Based on core genes found in the BGCs of all species examined across the Lamiaceae, we predict a simplified version of this cluster evolved in an early Lamiaceae ancestor. The current composition of the extant BGCs highlights the dynamic nature of its evolution. We elucidate the terpene backbones generated by the Callicarpa americana BGC enzymes, including miltiradiene and the terpene (+)-kaurene, and show oxidization activities of BGC cytochrome P450s. Our work reveals the fluid nature of BGC assembly and the importance of genome structure in contributing to the origin of metabolites.

Genotyping by sequencing and a newly developed mRNA-GBS approach to link population genetic and transcriptome analyses reveal pattern differences between sites and treatments in red clover (Trifolium pratense L.)

The important worldwide forage crop red clover (Trifolium pratense L.) is widely cultivated as cattle feed and for soil improvement. Wild populations and landraces have great natural diversity that could be used to improve cultivated red clover. However, to date, there is still insufficient knowledge about the natural genetic and phenotypic diversity of the species. Here, we developed a low-cost complexity reduced mRNA analysis (mRNA-GBS) and compared the results with population genetic (GBS) and previously published mRNA-Seq data, to assess whether analysis of intraspecific variation within and between populations and transcriptome responses is possible simultaneously. The mRNA-GBS approach was successful. SNP analyses from the mRNA-GBS approach revealed comparable patterns to the GBS results, but due to site-specific multifactorial influences of environmental responses as well as conceptual and methodological limitations of mRNA-GBS, it was not possible to link transcriptome analyses with reduced complexity and sequencing depth to previously published greenhouse and field expression studies. Nevertheless, the use of short sequences upstream of the poly(A) tail of mRNA to reduce complexity are promising approaches that combine population genetics and expression profiling to analyze many individuals with trait differences simultaneously and cost-effectively, even in non-model species. Nevertheless, our study design across different regions in Germany was also challenging. The use of reduced complexity differential expression analyses most likely overlays site-specific patterns due to highly complex plant responses under natural conditions.

Identification of key genes involved in secondary metabolite biosynthesis in Digitalis purpurea

The medicinal plant Digitalis purpurea produces cardiac glycosides that are useful in the pharmaceutical industry. These bioactive compounds are in high demand due to ethnobotany's application to therapeutic procedures. Recent studies have investigated the role of integrative analysis of multi-omics data in understanding cellular metabolic status through systems metabolic engineering approach, as well as its application to genetically engineering metabolic pathways. In spite of numerous omics experiments, most molecular mechanisms involved in metabolic pathways biosynthesis in D. purpurea remain unclear. Using R Package Weighted Gene Co-expression Network Analysis, co-expression analysis was performed on the transcriptome and metabolome data. As a result of our study, we identified transcription factors, transcriptional regulators, protein kinases, transporters, non-coding RNAs, and hub genes that are involved in the production of secondary metabolites. Since jasmonates are involved in the biosynthesis of cardiac glycosides, the candidate genes for Scarecrow-Like Protein 14 (SCL14), Delta24-sterol reductase (DWF1), HYDRA1 (HYD1), and Jasmonate-ZIM domain3 (JAZ3) were validated under methyl jasmonate treatment (MeJA, 100 μM). Despite early induction of JAZ3, which affected downstream genes, it was dramatically suppressed after 48 hours. SCL14, which targets DWF1, and HYD1, which induces cholesterol and cardiac glycoside biosynthesis, were both promoted. The correlation between key genes and main metabolites and validation of expression patterns provide a unique insight into the biosynthesis mechanisms of cardiac glycosides in D. purpurea.

Genetic variation underlying differential ammonium and nitrate responses in Arabidopsis thaliana

Nitrogen is an essential element required for plant growth and productivity. Understanding the mechanisms and natural genetic variation underlying nitrogen use in plants will facilitate the engineering of plant nitrogen use to maximize crop productivity while minimizing environmental costs. To understand the scope of natural variation that may influence nitrogen use, we grew 1,135 Arabidopsis thaliana natural genotypes on two nitrogen sources, nitrate and ammonium, and measured both developmental and defense metabolite traits. By using different environments and focusing on multiple traits, we identified a wide array of different nitrogen responses. These responses are associated with numerous genes, most of which were not previously associated with nitrogen responses. Only a small portion of these genes appear to be shared between environments or traits, while most are predominantly specific to a developmental or defense trait under a specific nitrogen source. Finally, by using a large population, we were able to identify unique nitrogen responses, such as preferring ammonium or nitrate, which appear to be generated by combinations of loci rather than a few large-effect loci. This suggests that it may be possible to obtain novel phenotypes in complex nitrogen responses by manipulating sets of genes with small effects rather than solely focusing on large-effect single gene manipulations.

Transcript and metabolite network perturbations in lignin biosynthetic mutants of Arabidopsis

Lignin, one of the most abundant polymers in plants, is derived from the phenylpropanoid pathway, which also gives rise to an array of metabolites that are essential for plant fitness. Genetic engineering of lignification can cause drastic changes in transcription and metabolite accumulation with or without an accompanying development phenotype. To understand the impact of lignin perturbation, we analyzed transcriptome and metabolite data from the rapidly lignifying stem tissue in 13 selected phenylpropanoid mutants and wild-type Arabidopsis (Arabidopsis thaliana). Our dataset contains 20,974 expressed genes, of which over 26% had altered transcript levels in at least one mutant, and 18 targeted metabolites, all of which displayed altered accumulation in at least one mutant. We found that lignin biosynthesis and phenylalanine supply via the shikimate pathway are tightly co-regulated at the transcriptional level. The hierarchical clustering analysis of differentially expressed genes (DEGs) grouped the 13 mutants into 5 subgroups with similar profiles of mis-regulated genes. Functional analysis of the DEGs in these mutants and correlation between gene expression and metabolite accumulation revealed system-wide effects on transcripts involved in multiple biological processes.

Emerging trends in genomic and epigenomic regulation of plant specialised metabolism

Regulation of specialised metabolism genes is multilayered and complex, influenced by an array of genomic, epigenetic and epigenomic mechanisms. Here, we review the most recent knowledge in this field, drawing from discoveries in several plant species. Our aim is to improve understanding of how plant genome structure and function influence specialised metabolism. We also highlight key areas for future exploration. Gene regulatory mechanisms influencing specialised metabolism include gene duplication and neo-functionalization, conservation of operon-like clusters of specialised metabolism genes, local chromatin modifications, and the organisation of higher order chromatin structures within the nucleus. Genomic and epigenomic research to-date in the discipline have focused on a relatively small number of plant species, primarily at whole organ or tissue level. This is largely due to the technical demands of the experimental methods needed. However, a high degree of cell-type specificity of function exists in specialised metabolism, driven by similarly specific gene regulation. In this review we focus on the genomic characteristics of genes that are found in different types of clusters within the genome. We propose that acquisition of cell-resolution epigenomic datasets in emerging models, such as the glandular trichomes of Cannabis sativa, will yield important advances. Data such as chromatin accessibility and histone modification profiles can pinpoint which regulatory sequences are active in individual cell types and at specific times in development. These could provide fundamental biological insight as well as novel targets for genetic engineering and crop improvement.

Metabolization and sequestration of plant specialized metabolites in insect herbivores: Current and emerging approaches

Herbivorous insects encounter diverse plant specialized metabolites (PSMs) in their diet, that have deterrent, anti-nutritional, or toxic properties. Understanding how they cope with PSMs is crucial to understand their biology, population dynamics, and evolution. This review summarizes current and emerging cutting-edge methods that can be used to characterize the metabolic fate of PSMs, from ingestion to excretion or sequestration. It further emphasizes a workflow that enables not only to study PSM metabolism at different scales, but also to tackle and validate the genetic and biochemical mechanisms involved in PSM resistance by herbivores. This review thus aims at facilitating research on PSM-mediated plant-herbivore interactions.

A gene cluster in Ginkgo biloba encodes unique multifunctional cytochrome P450s that initiate ginkgolide biosynthesis

The ginkgo tree (Ginkgo biloba) is considered a living fossil due to its 200 million year's history under morphological stasis. Its resilience is partly attributed to its unique set of specialized metabolites, in particular, ginkgolides and bilobalide, which are chemically complex terpene trilactones. Here, we use a gene cluster-guided mining approach in combination with co-expression analysis to reveal the primary steps in ginkgolide biosynthesis. We show that five multifunctional cytochrome P450s with atypical catalytic activities generate the tert-butyl group and one of the lactone rings, characteristic of all G. biloba trilactone terpenoids. The reactions include scarless C-C bond cleavage as well as carbon skeleton rearrangement (NIH shift) occurring on a previously unsuspected intermediate. The cytochrome P450s belong to CYP families that diversifies in pre-seed plants and gymnosperms, but are not preserved in angiosperms. Our work uncovers the early ginkgolide pathway and offers a glance into the biosynthesis of terpenoids of the Mesozoic Era.

NetREx: Network-based Rice Expression Analysis Server for abiotic stress conditions

Recent focus on transcriptomic studies in food crops like rice, wheat and maize provide new opportunities to address issues related to agriculture and climate change. Re-analysis of such data available in public domain supplemented with annotations across molecular hierarchy can be of immense help to the plant research community, particularly co-expression networks representing transcriptionally coordinated genes that are often part of the same biological process. With this objective, we have developed NetREx, a Network-based Rice Expression Analysis Server, that hosts ranked co-expression networks of Oryza sativa using publicly available messenger RNA sequencing data across uniform experimental conditions. It provides a range of interactable data viewers and modules for analysing user-queried genes across different stress conditions (drought, flood, cold and osmosis) and hormonal treatments (abscisic and jasmonic acid) and tissues (root and shoot). Subnetworks of user-defined genes can be queried in pre-constructed tissue-specific networks, allowing users to view the fold change, module memberships, gene annotations and analysis of their neighbourhood genes and associated pathways. The web server also allows querying of orthologous genes from Arabidopsis, wheat, maize, barley and sorghum. Here, we demonstrate that NetREx can be used to identify novel candidate genes and tissue-specific interactions under stress conditions and can aid in the analysis and understanding of complex phenotypes linked to stress response in rice. Database URL: https://bioinf.iiit.ac.in/netrex/index.html.

Plant biosynthetic gene clusters in the context of metabolic evolution

Covering: up to 2022Plants produce a wide range of structurally and biosynthetically diverse natural products to interact with their environment. These specialised metabolites typically evolve in limited taxonomic groups presumably in response to specific selective pressures. With the increasing availability of sequencing data, it has become apparent that in many cases the genes encoding biosynthetic enzymes for specialised metabolic pathways are not randomly distributed on the genome. Instead they are physically linked in structures such as arrays, pairs and clusters. The exact function of these clusters is debated. In this review we take a broad view of gene arrangement in plant specialised metabolism, examining types of structures and variation. We discuss the evolution of biosynthetic gene clusters in the wider context of metabolism, populations and epigenetics. Finally, we synthesise our observations to propose a new hypothesis for biosynthetic gene cluster formation in plants.