Microbial terroir: associations between soil microbiomes and the flavor chemistry of mustard (Brassica juncea).

Here, we characterized the independent role of soil microbiomes (bacterial and fungal communities) in determining the flavor chemistry of harvested mustard seed (Brassica juncea). Given the known impacts of soil microbial communities on various plant characteristics, we hypothesized that differences in rhizosphere microbiomes would result in differences in seed flavor chemistry (glucosinolate content). In a glasshouse study, we introduced distinct soil microbial communities to mustard plants growing in an otherwise consistent environment. At the end of the plant life cycle, we characterized the rhizosphere and root microbiomes and harvested produced mustard seeds for chemical characterization. Specifically, we measured the concentrations of glucosinolates, secondary metabolites known to create spicy and bitter flavors. We examined associations between rhizosphere microbial taxa or genes and seed flavor chemistry. We identified links between the rhizosphere microbial community composition and the concentration of the main glucosinolate, allyl, in seeds. We further identified specific rhizosphere taxa predictive of seed allyl concentration and identified bacterial functional genes, namely genes for sulfur metabolism, which could partly explain the observed associations. Together, this work offers insight into the potential influence of the belowground microbiome on the flavor of harvested crops.

Making watercress (Nasturtium officinale) cropping sustainable: genomic insights into enhanced phosphorus use efficiency in an aquatic crop.

Watercress (Nasturtium officinale) is a nutrient-dense salad crop with high antioxidant capacity and glucosinolate concentration and with the potential to contribute to nutrient security as a locally grown outdoor aquatic crop in northern temperate climates. However, phosphate-based fertilizers used to support plant growth contribute to the eutrophication of aquatic habitats, often pristine chalk streams, downstream of farms, increasing pressure to minimize fertilizer use and develop a more phosphorus-use efficient (PUE) crop. Here, we grew genetically distinct watercress lines selected from a bi-parental mapping population on a commercial watercress farm either without additional phosphorus (P-) or under a commercial phosphate-based fertilizer regime (P+), to decipher effects on morphology, nutritional profile, and the transcriptome. Watercress plants sustained shoot yield in P- conditions, through enhanced root biomass, but with shorter stems and smaller leaves. Glucosinolate concentration was not affected by P- conditions, but both antioxidant capacity and the concentration of sugars and starch in shoot tissue were enhanced. We identified two watercress breeding lines, with contrasting strategies for enhanced PUE: line 60, with highly plastic root systems and increased root growth in P-, and line 102, maintaining high yield irrespective of P supply, but less plastic. RNA-seq analysis revealed a suite of genes involved in cell membrane remodeling, root development, suberization, and phosphate transport as potential future breeding targets for enhanced PUE. We identified watercress gene targets for enhanced PUE for future biotechnological and breeding approaches enabling less fertilizer inputs and reduced environmental damage from watercress cultivation.

A Deep-Learning Algorithm to Predict Short-Term Progression to Geographic Atrophy on Spectral-Domain Optical Coherence Tomography.

Importance: The identification of patients at risk of progressing from intermediate age-related macular degeneration (iAMD) to geographic atrophy (GA) is essential for clinical trials aimed at preventing disease progression. DeepGAze is a fully automated and accurate convolutional neural network-based deep learning algorithm for predicting progression from iAMD to GA within 1 year from spectral-domain optical coherence tomography (SD-OCT) scans. Objective: To develop a deep-learning algorithm based on volumetric SD-OCT scans to predict the progression from iAMD to GA during the year following the scan. Design, Setting, and Participants: This retrospective cohort study included participants with iAMD at baseline and who either progressed or did not progress to GA within the subsequent 13 months. Participants were included from centers in 4 US states. Data set 1 included patients from the Age-Related Eye Disease Study 2 AREDS2 (Ancillary Spectral-Domain Optical Coherence Tomography) A2A study (July 2008 to August 2015). Data sets 2 and 3 included patients with imaging taken in routine clinical care at a tertiary referral center and associated satellites between January 2013 and January 2023. The stored imaging data were retrieved for the purpose of this study from July 1, 2022, to February 1, 2023. Data were analyzed from May 2021 to July 2023. Exposure: A position-aware convolutional neural network with proactive pseudointervention was trained and cross-validated on Bioptigen SD-OCT volumes (data set 1) and validated on 2 external data sets comprising Heidelberg Spectralis SD-OCT scans (data sets 2 and 3). Main Outcomes and Measures: Prediction of progression to GA within 13 months was evaluated with area under the receiver-operator characteristic curves (AUROC) as well as area under the precision-recall curve (AUPRC), sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. Results: The study included a total of 417 patients: 316 in data set 1 (mean [SD] age, 74 [8]; 185 [59%] female), 53 in data set 2, (mean [SD] age, 83 [8]; 32 [60%] female), and 48 in data set 3 (mean [SD] age, 81 [8]; 32 [67%] female). The AUROC for prediction of progression from iAMD to GA within 1 year was 0.94 (95% CI, 0.92-0.95; AUPRC, 0.90 [95% CI, 0.85-0.95]; sensitivity, 0.88 [95% CI, 0.84-0.92]; specificity, 0.90 [95% CI, 0.87-0.92]) for data set 1. The addition of expert-annotated SD-OCT features to the model resulted in no improvement compared to the fully autonomous model (AUROC, 0.95; 95% CI, 0.92-0.95; P = .19). On an independent validation data set (data set 2), the model predicted progression to GA with an AUROC of 0.94 (95% CI, 0.91-0.96; AUPRC, 0.92 [0.89-0.94]; sensitivity, 0.91 [95% CI, 0.74-0.98]; specificity, 0.80 [95% CI, 0.63-0.91]). At a high-specificity operating point, simulated clinical trial recruitment was enriched for patients progressing to GA within 1 year by 8.3- to 20.7-fold (data sets 2 and 3). Conclusions and Relevance: The fully automated, position-aware deep-learning algorithm assessed in this study successfully predicted progression from iAMD to GA over a clinically meaningful time frame. The ability to predict imminent GA progression could facilitate clinical trials aimed at preventing the condition and could guide clinical decision-making regarding screening frequency or treatment initiation.

The rhizosphere microbiome and host plant glucosinolates exhibit feedback cycles in Brassica rapa.

The rhizosphere microbiome influences many aspects of plant fitness, including production of secondary compounds and defence against insect herbivores. Plants also modulate the composition of the microbial community in the rhizosphere via secretion of root exudates. We tested both the effect of the rhizosphere microbiome on plant traits, and host plant effects on rhizosphere microbes using recombinant inbred lines (RILs) of Brassica rapa that differ in production of glucosinolates (GLS), secondary metabolites that contribute to defence against insect herbivores. First, we investigated the effect of genetic variation in GLS production on the composition of the rhizosphere microbiome. Using a Bayesian Dirichlet-multinomial regression model (DMBVS), we identified both negative and positive associations between bacteria from six genera and the concentration of five GLS compounds produced in plant roots. Additionally, we tested the effects of microbial inoculation (an intact vs. disrupted soil microbiome) on GLS production and insect damage in these RILs. We found a significant microbial treatment × genotype interaction, in which total GLS was higher in the intact relative to the disrupted microbiome treatment in some RILs. However, despite differences in GLS production between microbial treatments, we observed no difference in insect damage between treatments. Together, these results provide evidence for a full feedback cycle of plant-microbe interactions mediated by GLS; that is, GLS compounds produced by the host plant "feed-down" to influence rhizosphere microbial community and rhizosphere microbes "feed-up" to influence GLS production.

Novel method for the quantification of rosette area from images of Arabidopsis seedlings grown on agar plates.

Premise: The agar-based culture of Arabidopsis seedlings is widely used for quantifying root traits. Shoot traits are generally overlooked in these studies, probably because the rosettes are often askew. A technique to assess the shoot surface area of seedlings grown inside agar culture dishes would facilitate simultaneous root and shoot phenotyping. Methods: We developed an image processing workflow in Python that estimates rosette area of Arabidopsis seedlings on agar culture dishes. We validated this method by comparing its output with other metrics of seedling growth. As part of a larger study on genetic variation in plant responses to nitrogen form and concentration, we measured the rosette areas from more than 2000 plate images. Results: The rosette area measured from plate images was strongly correlated with the rosette area measured from directly overhead and moderately correlated with seedling mass. Rosette area in the large image set was significantly influenced by genotype and nitrogen treatment. The broad-sense heritability of leaf area measured using this method was 0.28. Discussion: These results indicated that this approach for estimating rosette area produces accurate shoot phenotype data. It can be used with image sets for which other methods of leaf area quantification prove unsuitable.

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.

Integrated omics reveal novel functions and underlying mechanisms of the receptor kinase FERONIA in Arabidopsis thaliana.

The receptor kinase FERONIA (FER) is a versatile regulator of plant growth and development, biotic and abiotic stress responses, and reproduction. To gain new insights into the molecular interplay of these processes and to identify new FER functions, we carried out quantitative transcriptome, proteome, and phosphoproteome profiling of Arabidopsis (Arabidopsis thaliana) wild-type and fer-4 loss-of-function mutant plants. Gene ontology terms for phytohormone signaling, abiotic stress, and biotic stress were significantly enriched among differentially expressed transcripts, differentially abundant proteins, and/or misphosphorylated proteins, in agreement with the known roles for FER in these processes. Analysis of multiomics data and subsequent experimental evidence revealed previously unknown functions for FER in endoplasmic reticulum (ER) body formation and glucosinolate biosynthesis. FER functions through the transcription factor NAI1 to mediate ER body formation. FER also negatively regulates indole glucosinolate biosynthesis, partially through NAI1. Furthermore, we found that a group of abscisic acid (ABA)-induced transcription factors is hypophosphorylated in the fer-4 mutant and demonstrated that FER acts through the transcription factor ABA INSENSITIVE5 (ABI5) to negatively regulate the ABA response during cotyledon greening. Our integrated omics study, therefore, reveals novel functions for FER and provides new insights into the underlying mechanisms of FER function.

Genetic variation, environment and demography intersect to shape Arabidopsis defense metabolite variation across Europe.

Plants produce diverse metabolites to cope with the challenges presented by complex and ever-changing environments. These challenges drive the diversification of specialized metabolites within and between plant species. However, we are just beginning to understand how frequently new alleles arise controlling specialized metabolite diversity and how the geographic distribution of these alleles may be structured by ecological and demographic pressures. Here, we measure the variation in specialized metabolites across a population of 797 natural Arabidopsis thaliana accessions. We show that a combination of geography, environmental parameters, demography and different genetic processes all combine to influence the specific chemotypes and their distribution. This showed that causal loci in specialized metabolism contain frequent independently generated alleles with patterns suggesting potential within-species convergence. This provides a new perspective about the complexity of the selective forces and mechanisms that shape the generation and distribution of allelic variation that may influence local adaptation.

Since plants cannot move, they have evolved chemical defenses to help them respond to changes in their surroundings. For example, where animals run from predators, plants may produce toxins to put predators off. This approach is why plants are such a rich source of drugs, poisons, dyes and other useful substances. The chemicals plants produce are known as specialized metabolites, and they can change a lot between, and even within, plant species. The variety of specialized metabolites is a result of genetic changes and evolution over millions of years. Evolution is a slow process, yet plants are able to rapidly develop new specialized metabolites to protect them from new threats. Even different populations of the same species produce many distinct metabolites that help them survive in their surroundings. However, the factors that lead plants to produce new metabolites are not well understood, and it is not known how this affects genetic variation. To gain a better understanding of this process, Katz et al. studied 797 European variants of a common weed species called Arabidopsis thaliana, which is widely studied. The investigation found that many factors affect the range of specialized metabolites in each variant. These included local geography and environment, as well as genetics and population history (demography). Katz et al. revealed a pattern of relationships between the variants that could mirror their evolutionary history as the species spread and adapted to new locations. These results highlight the complex network of factors that affect plant evolution. Rapid diversification is key to plant survival in new and changing environments and has resulted in a wide range of specialized metabolites. As such they are of interest both for studying plant evolution and for understanding their ecology. Expanding similar work to more populations and other species will broaden the scope of our ability to understand how plants adapt to their surroundings.

mGWAS Uncovers Gln-Glucosinolate Seed-Specific Interaction and its Role in Metabolic Homeostasis.

Gln is a key player in plant metabolism. It is one of the major free amino acids that is transported into the developing seed and is central for nitrogen metabolism. However, Gln natural variation and its regulation and interaction with other metabolic processes in seeds remain poorly understood. To investigate the latter, we performed a metabolic genome-wide association study (mGWAS) of Gln-related traits measured from the dry seeds of the Arabidopsis (Arabidopsis thaliana) diversity panel using all potential ratios between Gln and the other members of the Glu family as traits. This semicombinatorial approach yielded multiple candidate genes that, upon further analysis, revealed an unexpected association between the aliphatic glucosinolates (GLS) and the Gln-related traits. This finding was confirmed by an independent quantitative trait loci mapping and statistical analysis of the relationships between the Gln-related traits and the presence of specific GLS in seeds. Moreover, an analysis of Arabidopsis mutants lacking GLS showed an extensive seed-specific impact on Gln levels and composition that manifested early in seed development. The elimination of GLS in seeds was associated with a large effect on seed nitrogen and sulfur homeostasis, which conceivably led to the Gln response. This finding indicates that both Gln and GLS play key roles in shaping the seed metabolic homeostasis. It also implies that select secondary metabolites might have key functions in primary seed metabolism. Finally, our study shows that an mGWAS performed on dry seeds can uncover key metabolic interactions that occur early in seed development.

Diverse Allyl Glucosinolate Catabolites Independently Influence Root Growth and Development.

Glucosinolates (GSLs) are sulfur-containing defense metabolites produced in the Brassicales, including the model plant Arabidopsis (Arabidopsis thaliana). Previous work suggests that specific GSLs may function as signals to provide direct feedback regulation within the plant to calibrate defense and growth. These GSLs include allyl-GSL, a defense metabolite that is one of the most widespread GSLs in Brassicaceae and has also been associated with growth inhibition. Here we show that at least three separate potential catabolic products of allyl-GSL or closely related compounds affect growth and development by altering different mechanisms influencing plant development. Two of the catabolites, raphanusamic acid and 3-butenoic acid, differentially affect processes downstream of the auxin signaling cascade. Another catabolite, acrylic acid, affects meristem development by influencing the progression of the cell cycle. These independent signaling events propagated by the different catabolites enable the plant to execute a specific response that is optimal to any given environment.

Auxin-sensitive Aux/IAA proteins mediate drought tolerance in Arabidopsis by regulating glucosinolate levels.

A detailed understanding of abiotic stress tolerance in plants is essential to provide food security in the face of increasingly harsh climatic conditions. Glucosinolates (GLSs) are secondary metabolites found in the Brassicaceae that protect plants from herbivory and pathogen attack. Here we report that in Arabidopsis, aliphatic GLS levels are regulated by the auxin-sensitive Aux/IAA repressors IAA5, IAA6, and IAA19. These proteins act in a transcriptional cascade that maintains expression of GLS levels when plants are exposed to drought conditions. Loss of IAA5/6/19 results in reduced GLS levels and decreased drought tolerance. Further, we show that this phenotype is associated with a defect in stomatal regulation. Application of GLS to the iaa5,6,19 mutants restores stomatal regulation and normal drought tolerance. GLS action is dependent on the receptor kinase GHR1, suggesting that GLS may signal via reactive oxygen species. These results provide a novel connection between auxin signaling, GLS levels and drought response.

Indole-3-carbinol: a plant hormone combatting cancer.

A diet rich in cruciferous vegetables such as cauliflower, broccoli, and cabbage has long been considered healthy, and various epidemiological studies suggest that the consumption of cruciferous vegetables contributes to a cancer-protecting diet. While these vegetables contain a vast array of phytochemicals, the mechanism by which these vegetables counteract cancer is still largely unresolved. Numerous in situ studies have implicated indole-3-carbinol, a breakdown product of the glucosinolate indole-3-ylmethylglucosinolate, as one of the phytochemicals with anti-cancer properties. Indole-3-carbinol influences a range of cellular processes, but the mechanisms by which it acts on cancer cells are slowly being revealed. Recent studies on the role of indole-3-carbinol in Arabidopsis opens the door for cross-kingdom comparisons that can help in understanding the roles of this important phytohormone in both plant biology and combatting cancer.

Wounding of Arabidopsis leaves induces indole-3-carbinol-dependent autophagy in roots of Arabidopsis thaliana.

In cruciferous plants insect attack or physical damage induce the synthesis of the glucosinolate breakdown product indole-3-carbinol, which plays a key role in the defense against attackers. Indole-3-carbinol also affects plant growth and development, acting as an auxin antagonist by binding to the TIR1 auxin receptor. Other potential functions of indole-3-carbinol and the underlying mechanisms in plant biology are unknown. Here we show that an indole-3-carbinol-dependent signal induces specific autophagy in root cells. Leaf treatment with exogenous indole-3-carbinol or leaf-wounding induced autophagy and inhibited auxin response in the root. This induction is lost in glucosinolate-defective mutants, indicating that the effect of indole-3-carbinol is transported in the plants. Thus, indole-3-carbinol is not only a defensive metabolite that repels insects, but is also involved in long-distance communication regulating growth and development in plants.

The effect of indole-3-carbinol on PIN1 and PIN2 in Arabidopsis roots.

The phytochemical indole-3-carbinol is produced in Cruciferous plants upon tissue rapture and deters herbivores. We recently showed that indole-3-carbinol modulates auxin signaling in root tips. Here we present transcript profiling experiments which further reveal the influence of indole-3-carbinol on auxin signaling in root tips, and also show that I3C affects auxin transporters. Brief treatment with indole-3-carbinol led to a reduction in the amount of PIN1 and to mislocalization of PIN2.

The glucosinolate breakdown product indole-3-carbinol acts as an auxin antagonist in roots of Arabidopsis thaliana.

The glucosinolate breakdown product indole-3-carbinol functions in cruciferous vegetables as a protective agent against foraging insects. While the toxic and deterrent effects of glucosinolate breakdown on herbivores and pathogens have been studied extensively, the secondary responses that are induced in the plant by indole-3-carbinol remain relatively uninvestigated. Here we examined the hypothesis that indole-3-carbinol plays a role in influencing plant growth and development by manipulating auxin signaling. We show that indole-3-carbinol rapidly and reversibly inhibits root elongation in a dose-dependent manner, and that this inhibition is accompanied by a loss of auxin activity in the root meristem. A direct interaction between indole-3-carbinol and the auxin perception machinery was suggested, as application of indole-3-carbinol rescues auxin-induced root phenotypes. In vitro and yeast-based protein interaction studies showed that indole-3-carbinol perturbs the auxin-dependent interaction of Transport Inhibitor Response (TIR1) with auxin/3-indoleacetic acid (Aux/IAAs) proteins, further supporting the possibility that indole-3-carbinol acts as an auxin antagonist. The results indicate that chemicals whose production is induced by herbivory, such as indole-3-carbinol, function not only to repel herbivores, but also as signaling molecules that directly compete with auxin to fine tune plant growth and development.