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Background: Mitigating the effects of climate stress on crops is important for global food security. The microbiome associated with plant roots, henceforth, the rhizobiome, can harbor beneficial microbes that alleviate stress impacts. However, the factors influencing the recruitment of the rhizobiome during stress are unclear. We conducted an experiment to understand bacterial rhizobiome responses to short-term drought for two crop species: switchgrass and common bean. We used 16S rRNA and 16S rRNA gene sequencing to investigate the impact of drought severity on the recruitment of active bacterial rhizobiome members. We included planted and unplanted conditions to distinguish the environment- versus plant mediated drivers of the active rhizobiome. Results: Though each crop had a distinct rhizobiome, there were differences in the active microbiome structure between drought and watered and between planted and unplanted treatments. Despite their different community structures, the drought rhizobiome dynamics were similar across the two crops. However, the presence of a plant more strongly explained the rhizobiome variation in bean (17%) than in switchgrass (3%), with a small effect of plant mediation during drought only observed for the bean rhizobiome. The switchgrass rhizobiome was stable despite differences in the rhizosphere metabolite profiles between planted and unplanted treatments. Specifically, steroidal saponins and diterpennoids were enriched in drought, planted switchgrass soils. Conclusions: We conclude that rhizobiome benefits to resist short-term drought are crop-specific, with the possibility of decoupling of plant exudation and rhizobiome responses, as we observed in switchgrass. We propose bacterial taxa uniquely associated with common bean plants during the short-term drought, which could be further evaluated to determine any plant benefit during drought. 

Tremendous plant metabolic diversity arises from phylogenetically restricted specialized metabolic pathways. Specialized metabolites are synthesized in dedicated cells or tissues, with pathway genes sometimes colocalizing in biosynthetic gene clusters (BGCs). However, the mechanisms by which spatial expression patterns arise and the role of BGCs in pathway evolution remain underappreciated. In this study, we investigated the mechanisms driving acylsugar evolution in the Solanaceae. Previously thought to be restricted to glandular trichomes, acylsugars were recently found in cultivated tomato roots. We demonstrated that acylsugars in cultivated tomato roots and trichomes have different sugar cores, identified root-enriched paralogs of trichome acylsugar pathway genes, and characterized a key paralog required for root acylsugar biosynthesis,SlASAT1-LIKE(SlASAT1-L), which is nested within a previously reported trichome acylsugar BGC. Last, we provided evidence thatASAT1-Larose through duplication of its paralog,ASAT1, and was trichome-expressed before acquiring root-specific expression in theSolanumgenus. Our results illuminate the genomic context and molecular mechanisms underpinning metabolic diversity in plants. 

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

Plants produce phylogenetically and spatially restricted, as well as structurally diverse specialized metabolites via multistep metabolic pathways. Hallmarks of specialized metabolic evolution include enzymatic promiscuity and recruitment of primary metabolic enzymes and examples of genomic clustering of pathway genes. Solanaceae glandular trichomes produce defensive acylsugars, with sidechains that vary in length across the family. We describe a tomato gene cluster on chromosome 7 involved in medium chain acylsugar accumulation due to trichome specific acyl-CoA synthetase and enoyl-CoA hydratase genes. This cluster co-localizes with a tomato steroidal alkaloid gene cluster and is syntenic to a chromosome 12 region containing another acylsugar pathway gene. We reconstructed the evolutionary events leading to this gene cluster and found that its phylogenetic distribution correlates with medium chain acylsugar accumulation across the Solanaceae. This work reveals insights into the dynamics behind gene cluster evolution and cell-type specific metabolite diversity. 

The target of rapamycin (TOR) kinase is an evolutionarily conserved hub of nutrient sensing and metabolic signaling. In plants, a functional connection of TOR activation with glucose availability was demonstrated, while it is yet unclear whether branched-chain amino acids (BCAAs) are a primary input of TOR signaling as they are in yeast and mammalian cells. Here, we report on the characterization of an Arabidopsis mutant over-accumulating BCAAs. Through chemical interventions targeting TOR and by examining mutants of BCAA biosynthesis and TOR signaling, we found that BCAA over-accumulation leads to up-regulation of TOR activity, which causes reorganization of the actin cytoskeleton and actin-associated endomembranes. Finally, we show that activation of TOR is concomitant with alteration of cell expansion, proliferation and specialized metabolism, leading to pleiotropic effects on plant growth and development. These results demonstrate that BCAAs contribute to plant TOR activation and reveal previously uncharted downstream subcellular processes of TOR signaling. 

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

Specialized metabolites are structurally diverse and cell‐ or tissue‐specific molecules produced in restricted plant lineages. In contrast, primary metabolic pathways are highly conserved in plants and produce metabolites essential for all of life, such as amino acids and nucleotides. Substrate promiscuity – the capacity to accept non‐native substrates – is a common characteristic of enzymes, and its impact is especially apparent in generating specialized metabolite variation. However, promiscuity only leads to metabolic diversity when alternative substrates are available; thus, enzyme cellular and subcellular localization directly influence chemical phenotypes. We review a variety of mechanisms that modulate substrate availability for promiscuous plant enzymes. We focus on examples where evolution led to modification of the ‘cellular context’ through changes in cell‐type expression, subcellular relocalization, pathway sequestration, and cellular mixing via tissue damage. These varied mechanisms contributed to the emergence of structurally diverse plant specialized metabolites and inform future metabolic engineering approaches. 

Plant specialized metabolism (SM) enzymes produce lineage-specific metabolites with important ecological, evolutionary, and biotechnological implications. UsingArabidopsis thalianaas a model, we identified distinguishing characteristics of SM and GM (general metabolism, traditionally referred to as primary metabolism) genes through a detailed study of features including duplication pattern, sequence conservation, transcription, protein domain content, and gene network properties. Analysis of multiple sets of benchmark genes revealed that SM genes tend to be tandemly duplicated, coexpressed with their paralogs, narrowly expressed at lower levels, less conserved, and less well connected in gene networks relative to GM genes. Although the values of each of these features significantly differed between SM and GM genes, any single feature was ineffective at predicting SM from GM genes. Using machine learning methods to integrate all features, a prediction model was established with a true positive rate of 87% and a true negative rate of 71%. In addition, 86% of known SM genes not used to create the machine learning model were predicted. We also demonstrated that the model could be further improved when we distinguished between SM, GM, and junction genes responsible for reactions shared by SM and GM pathways, indicating that topological considerations may further improve the SM prediction model. Application of the prediction model led to the identification of 1,220A. thalianagenes with previously unknown functions, each assigned a confidence measure called an SM score, providing a global estimate of SM gene content in a plant genome.