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Abstract Data from functional trait databases have been increasingly used to address questions related to plant diversity and trait-environment relationships. However, such databases provide intraspecific data that combine individual records obtained from distinct populations at different sites and, hence, environmental conditions. This prevents distinguishing sources of variation (e.g., genetic-based variation vs. phenotypic plasticity), a necessary condition to test for adaptive processes and other determinants of plant phenotypic diversity. Consequently, individual traits measured under common growing conditions and encompassing within-species variation across the occupied geographic range have the potential to leverage trait databases with valuable data for functional and evolutionary ecology. Here, we recorded 16 functional traits and leaf hyperspectral reflectance (NIRS) data for 721 widely distributed Arabidopsis thaliana natural accessions grown in a common garden experiment. These data records, together with meteorological variables obtained during the experiment, were assembled to create the AraDiv dataset. AraDiv is a comprehensive dataset of A. thaliana’s intraspecific variability that can be explored to address questions at the interface of genetics and ecology.

Abstract Background After uptake from soil into the root tissue, distribution and allocation of nitrate throughout the whole plant body, is a critical step of nitrogen use efficiency (NUE) and for modulation of plant growth in response to various environmental conditions. In legume plants nitrate distribution is also important for the regulation of the nodulation process that allows to fix atmospheric N (N2) through the symbiotic interaction with rhizobia (symbiotic nitrogen fixation, SNF). Results Here we report the functional characterization of the Lotus japonicus gene LjNPF2.9, which is expressed mainly in the root vascular structures, a key localization for the control of nitrate allocation throughout the plant body. LjNPF2.9 expression in Xenopus laevis oocytes induces 15NO3 accumulation indicating that it functions as a nitrate importer. The phenotypic characterization of three independent knock out mutants indicates an increased shoot biomass in the mutant backgrounds. This phenotype is associated to an increased/decreased nitrate content detected in the shoots/roots. Furthermore, our analysis indicates that the accumulation of nitrate in the shoot does not affect the nodulation and N-Fixation capacities of the knock out mutants. Conclusions This study shows that LjNPF2.9 plays a crucial role in the downward transport of nitrate to roots, occurring likely through a xylem-to-phloem loading-mediated activity. The increase of the shoot biomass and nitrate accumulation might represent a relevant phenotype in the perspective of an improved NUE and this is further reinforced in legume plants by the reported lack of effects on the SNF efficiency.

Few proteins have been characterized as abscisic acid transporters. Several of them are NRT1/PRT Family (NPF) transporters which have been characterized in yeast using reporter systems. Because several members of the NPF4 subfamily members were identified in yeast as ABA transporters, here, we screened for ABA transport activity the seven members of the NPF4 subfamily in Xenopus oocytes using cRNA injection and 3H-ABA accumulation. The ABA transport capacities of NPF4.2, NPF4.5, NPF4.6, and NPF4.7 were confirmed. The transport properties of NPF4.5 and NPF4.6 were studied in more detail. Both ABA transporter activities are pH-dependent and slightly pH-dependent apparent Km around 500 μM. There is no competitive inhibition of the ABA-analogs pyrabactin and quinabactin on ABA accumulation demonstrating a different selectivity compared to the ABA receptors. Functional expression of these ABA transporters in Xenopus oocyte is an opportunity to start structure–function studies and also to identify partner proteins of these hormone transporters.

Most plants use nitrate (NO3-) as their major nitrogen (N) source. The NO3- uptake capacity of a plant is determined by three interdependent factors that are sensitive to NO3- availability: (i) the functional properties of the transporters in the roots that contribute to the acquisition of NO3- from the external medium, (ii) the density of functional transporters at the plasma membrane of root cells, and (iii) the surface and architecture of the root system. The identification of factors that regulate the NO3--sensing systems is important for both fundamental and applied science, because these factors control the capacity of plants to use the available NO3-, a process known as the nitrate use efficiency. The molecular component of the transporters involved in uptake and sensing mechanism in Arabidopsis roots are presented and their relative contribution discussed.

Phosphorus (P) is an essential macronutrient for plants to complete their life cycle. P taken up from the soil by the roots is transported to the rest of the plant and ultimately stored in seeds. This stored P is used during germination to sustain the nutritional demands of the growing seedling in the absence of a developed root system. Nevertheless, P deficiency, an increasing global issue, greatly decreases the vigour of afflicted seeds. To combat P deficiency, current crop production methods rely on heavy P fertilizer application, an unsustainable practice in light of a speculated decrease in worldwide P stocks. Therefore, the overall goal in optimizing P usage for agricultural purposes is both to decrease our dependency on P fertilizers and enhance the P-use efficiency in plants. Achieving this goal requires a robust understanding of how plants regulate inorganic phosphate (Pi) transport, during vegetative growth as well as the reproductive stages of development. In this short review, we present the current knowledge on Pi transport in the model plant Arabidopsis thaliana and apply the information towards the economically important cereal crop wheat. We highlight the importance of developing our knowledge on the regulation of these plants’ P transport systems and P accumulation in seeds due to its involvement in maintaining their vigour and nutritional quality. We additionally discuss further discoveries in the subjects this review discusses substantiate this importance in their practical applications for practical food security and geopolitical applications.

Zinc (Zn) is essential for the optimal growth of plants but is toxic if present in excess, so Zn homeostasis needs to be finely tuned. Understanding Zn homeostasis mechanisms in plants will help in the development of innovative approaches for the phytoremediation of Zn-contaminated sites. In this study, Zn tolerance quantitative trait loci (QTL) were identified by analyzing differences in the Bay-0 and Shahdara accessions of Arabidopsis thaliana. Fine-scale mapping showed that a variant of the Fe homeostasis-related FERRIC REDUCTASE DEFECTIVE3 (FRD3) gene, which encodes a multidrug and toxin efflux (MATE) transporter, is responsible for reduced Zn tolerance in A. thaliana. Allelic variation in FRD3 revealed which amino acids are necessary for FRD3 function. In addition, the results of allele-specific expression assays in F1 individuals provide evidence for the existence of at least one putative metal-responsive cis-regulatory element. Our results suggest that FRD3 works as a multimer and is involved in loading Zn into xylem. Cross-homeostasis between Fe and Zn therefore appears to be important for Zn tolerance in A. thaliana with FRD3 acting as an essential regulator.

Sonic hedgehog (Shh) signaling plays a crucial role in growth and patterning during embryonic development, and also in stem cell maintenance and tissue regeneration in adults. Aberrant Shh pathway activation is involved in the development of many tumors, and one of the most affected Shh signaling steps found in these tumors is the regulation of the signaling receptor Smoothened by the Shh receptor Patched. In the present work, we investigated Patched activity and the mechanism by which Patched inhibits Smoothened.Using the well-known Shh-responding cell line of mouse fibroblasts NIH 3T3, we first observed that enhancement of the intracellular cholesterol concentration induces Smoothened enrichment in the plasma membrane, which is a crucial step for the signaling activation. We found that binding of Shh protein to its receptor Patched, which involves Patched internalization, increases the intracellular concentration of cholesterol and decreases the efflux of a fluorescent cholesterol derivative (BODIPY-cholesterol) from these cells. Treatment of fibroblasts with cyclopamine, an antagonist of Shh signaling, inhibits Patched expression and reduces BODIPY-cholesterol efflux, while treatment with the Shh pathway agonist SAG enhances Patched protein expression and BODIPY-cholesterol efflux. We also show that over-expression of human Patched in the yeast S. cerevisiae results in a significant boost of BODIPY-cholesterol efflux. Furthermore, we demonstrate that purified Patched binds to cholesterol, and that the interaction of Shh with Patched inhibits the binding of Patched to cholesterol.Our results suggest that Patched may contribute to cholesterol efflux from cells, and to modulation of the intracellular cholesterol concentration. This activity is likely responsible for the inhibition of the enrichment of Smoothened in the plasma membrane, which is an important step in Shh pathway activation.

The trait-based approach in plant ecology aims at understanding and classifying the diversity of ecological strategies by comparing plant morphology and physiology across organisms. The major drawback of the approach is that the time and financial cost of measuring the traits on many individuals and environments can be prohibitive. We show that combining near-infrared spectroscopy (NIRS) with deep learning resolves this limitation by quickly, non-destructively, and accurately measuring a suite of traits, including plant morphology, chemistry, and metabolism. Such an approach also allows to position plants within the well-known CSR triangle that depicts the diversity of plant ecological strategies. The processing of NIRS through deep learning identifies the effect of growth conditions on trait values, an issue that plagues traditional statistical approaches. Together, the coupling of NIRS and deep learning is a promising high-throughput approach to capture a range of ecological information on plant diversity and functioning and can accelerate the creation of extensive trait databases.