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Recent technical and theoretical advances have generated an explosion in the identification of specialized metabolite pathways. In comparison, our understanding of how these pathways are regulated is relatively lagging. This and the relatively young age of specialized metabolite pathways has partly contributed to a default and common paradigm whereby specialized metabolite regulation is theorized as relatively simple with a few key transcription factors and the compounds are non-regulatory end-products. In contrast, studies into model specialized metabolites, such as glucosinolates, are beginning to identify a new understanding whereby specialized metabolites are highly integrated into the plants’ core metabolic, physiological, and developmental pathways. This model includes a greatly extended compendium of transcription factors controlling the pathway, key transcription factors that co-evolve with the pathway and simultaneously control core metabolic and developmental components, and finally the compounds themselves evolve regulatory connections to integrate into the plants signaling machinery. In this review, these concepts are illustrated using studies in the glucosinolate pathway within the Brassicales. This suggests that the broader community needs to reconsider how they do or do not integrate specialized metabolism into the regulatory network of their study species.

Leaf-level hyperspectral reflectance has become an effective tool for high-throughput phenotyping of plant leaf traits due to its rapid, low-cost, multi-sensing, and non-destructive nature. However, collecting samples for model calibration can still be expensive, and models show poor transferability among different datasets. This study had three specific objectives: first, to assemble a large library of leaf hyperspectral data (n=2460) from maize and sorghum; second, to evaluate two machine-learning approaches to estimate nine leaf properties (chlorophyll, thickness, water content, nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur); and third, to investigate the usefulness of this spectral library for predicting external datasets (n=445) including soybean and camelina using extra-weighted spiking. Internal cross-validation showed satisfactory performance of the spectral library to estimate all nine traits (mean R2=0.688), with partial least-squares regression outperforming deep neural network models. Models calibrated solely using the spectral library showed degraded performance on external datasets (mean R2=0.159 for camelina, 0.337 for soybean). Models improved significantly when a small portion of external samples (n=20) was added to the library via extra-weighted spiking (mean R2=0.574 for camelina, 0.536 for soybean). The leaf-level spectral library greatly benefits plant physiological and biochemical phenotyping, whilst extra-weight spiking improves model transferability and extends its utility.

The relationship between root, stem, and leaf hydraulic status and stomatal conductance during drought (field capacities: 100–25%) and drought recovery was studied in Helianthus annuus and five tree species (Populus×canadensis, Acer saccharum, A. saccharinum, Picea glauca, and Tsuga canadensis). Measurements of stomatal conductance (gs), organ water potential, and vessel embolism were performed and the following was observed: (i) cavitation only occurred in the petioles and not the roots or stems of tree species regardless of drought stress; (ii) in contrast, all H. annuus organs exhibited cavitation to an increasing degree from root to petiole; and (iii) all species initiated stomatal closure before cavitation events occurred or the expected turgor loss point was reached. After rewatering: (i) cavitated vessels in petioles of Acer species recovered whereas those of P. ×canadensis did not and leaves were shed; (ii) in H. annuus, cavitated xylem vessels were refilled in roots and petioles, but not in stems; and (iii) despite refilled embolisms in petioles of some species during drought recovery, gs never returned to pre-drought conditions. Conclusions are drawn with respect to the hydraulic segmentation hypothesis for above- and below-ground organs, and the timeline of embolism occurrence and repair is discussed.

Sorghum [Sorghum bicolor (L.) Moench] is the fifth most important cereal crop globally by harvested area and production. Its drought and heat tolerance allow high yields with minimal input. It is a promising biomass crop for the production of biofuels and bioproducts. In addition, as an annual diploid with a relatively small genome compared with other C4 grasses, and excellent germplasm diversity, sorghum is an excellent research species for other C4 crops such as maize. As a result, an increasing number of researchers are looking to test the transferability of findings from other organisms such as Arabidopsis thaliana and Brachypodium distachyon to sorghum, as well as to engineer new biomass sorghum varieties. Here, we provide an overview of sorghum as a multipurpose feedstock crop which can support the growing bioeconomy, and as a monocot research model system. We review what makes sorghum such a successful crop and identify some key traits for future improvement. We assess recent progress in sorghum transformation and highlight how transformation limitations still restrict its widespread adoption. Finally, we summarize available sorghum genetic, genomic, and bioinformatics resources. This review is intended for researchers new to sorghum research, as well as those wishing to include non-food and forage applications in their research.