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Summary Root hair (RH) cells can elongate to several hundred times their initial size, and are an ideal model system for investigating cell size control. Their development is influenced by both endogenous and external signals, which are combined to form an integrative response. Surprisingly, a low‐temperature condition of 10°C causes increased RH growth inArabidopsisand in several monocots, even when the development of the rest of the plant is halted.Previously, we demonstrated a strong correlation between RH growth response and a significant decrease in nutrient availability in the growth medium under low‐temperature conditions. However, the molecular basis responsible for receiving and transmitting signals related to the availability of nutrients in the soil, and their relation to plant development, remain largely unknown.We have discovered two antagonic gene regulatory networks (GRNs) controlling RH early transcriptome responses to low temperature. One GNR enhances RH growth and it is commanded by the transcription factors (TFs)ROOT HAIR DEFECTIVE 6(RHD6),HAIR DEFECTIVE 6‐LIKE 2 and 4(RSL2‐RSL4) and a member of the homeodomain leucine zipper (HD‐Zip I) group I 16 (AtHB16). On the other hand, a second GRN was identified as a negative regulator of RH growth at low temperature and it is composed by the trihelix TFGT2‐LIKE1(GTL1) and the associated DF1, a previously unidentified MYB‐like TF (AT2G01060) and several members of HD‐Zip I group (AtHB3, AtHB13, AtHB20, AtHB23).Functional analysis of both GRNs highlights a complex regulation of RH growth response to low temperature, and more importantly, these discoveries enhance our comprehension of how plants synchronize RH growth in response to variations in temperature at the cellular level. 

Abstract Nitrate is a nutrient and signal that regulates gene expression. The nitrate response has been extensively characterized at the organism, organ, and cell‐type‐specific levels, but intracellular mRNA dynamics remain unexplored. To characterize nuclear and cytoplasmic transcriptome dynamics in response to nitrate, we performed a time‐course expression analysis after nitrate treatment in isolated nuclei, cytoplasm, and whole roots. We identified 402 differentially localized transcripts (DLTs) in response to nitrate treatment. Induced DLT genes showed rapid and transient recruitment of the RNA polymerase II, together with an increase in the mRNA turnover rates. DLTs code for genes involved in metabolic processes, localization, and response to stimulus indicating DLTs include genes with relevant functions for the nitrate response that have not been previously identified. Using single‐molecule RNA FISH, we observed early nuclear accumulation of theNITRATE REDUCTASE 1(NIA1) transcripts in their transcription sites. We found that transcription ofNIA1, a gene showing delayed cytoplasmic accumulation, is rapidly and transiently activated; however, its transcripts become unstable when they reach the cytoplasm. Our study reveals the dynamic localization of mRNAs between the nucleus and cytoplasm as an emerging feature in the temporal control of gene expression in response to nitrate treatment in Arabidopsis roots. 

SUMMARY Root systems are uniquely adapted to fluctuations in external nutrient availability. In response to suboptimal nitrogen conditions, plants adopt a root foraging strategy that favors a deeper and more branched root architecture, enabling them to explore and acquire soil resources. This response is gradually suppressed as nitrogen conditions improve. However, the root hairless mutantbuzzinBrachypodium distachyonshows a constitutive nitrogen‐foraging phenotype with increased root growth and root branching under nitrate‐rich conditions. To investigate how this unique root structure and root hair morphology in thebuzzmutant affects nitrate metabolism, we measured the expression of nitrate‐responsive genes, nitrate uptake and accumulation, nitrate reductase activity, and nitrogen use efficiency. We found that nitrate responses were upregulated by low nitrate conditions inbuzzrelative to wild type and correlated with increased expression of nitrate transport genes. In addition,buzzmutants showed increased nitrate uptake and a higher accumulation of nitrate in shoots. Thebuzzmutant also showed increased nitrate reductase activity in the shoots under low nitrate conditions. However, developmentally mature wild‐type andbuzzplants grown under low nitrate had similar nitrogen use efficiencies. These findings suggest thatBUZZinfluences nitrate signaling and that enhanced responsiveness to nitrate is required inbuzzseedlings to compensate for the lack of root hairs. These data question the importance of root hairs in enhancing nitrate uptake and expand our understanding of how root hairs in grasses affect physiological responses to low nitrate availability. 

Abstract A plant's response to external and internal nitrogen signals/status relies on sensing and signaling mechanisms that operate across spatial and temporal dimensions. From a comprehensive systems biology perspective, this involves integrating nitrogen responses in different cell types and over long distances to ensure organ coordination in real time and yield practical applications. In this prospective review, we focus on novel aspects of nitrogen (N) sensing/signaling uncovered using temporal and spatial systems biology approaches, largely in the model Arabidopsis. The temporal aspects span: transcriptional responses to N-dose mediated by Michaelis-Menten kinetics, the role of the master NLP7 transcription factor as a nitrate sensor, its nitrate-dependent TF nuclear retention, its “hit-and-run” mode of target gene regulation, and temporal transcriptional cascade identified by “network walking.” Spatial aspects of N-sensing/signaling have been uncovered in cell type-specific studies in roots and in root-to-shoot communication. We explore new approaches using single-cell sequencing data, trajectory inference, and pseudotime analysis as well as machine learning and artificial intelligence approaches. Finally, unveiling the mechanisms underlying the spatial dynamics of nitrogen sensing/signaling networks across species from model to crop could pave the way for translational studies to improve nitrogen-use efficiency in crops. Such outcomes could potentially reduce the detrimental effects of excessive fertilizer usage on groundwater pollution and greenhouse gas emissions. 

Summary Drought and the availability of nitrate, the predominant source of nitrogen (N) in agriculture, are major factors limiting plant growth and crop productivity. The dissection of the transcriptional networks' components integrating droght stress and nitrate responses provides valuable insights into how plants effectively balance stress response with growth programs. Recent evidence inArabidopsis thalianaindicates that transcription factors (TFs) involved in abscisic acid (ABA) signaling affect N metabolism and nitrate responses, and reciprocally, components of nitrate signaling might affect ABA and drought gene responses. Advances in understanding regulatory circuits of nitrate and drought crosstalk in plant tissues empower targeted genetic modifications to enhance plant development and stress resistance, critical traits for optimizing crop yield and promoting sustainable agriculture. 

Drought is a significant environmental stressor that severely impairs plant growth and agricultural productivity. Unraveling the molecular mechanisms underlying plant responses to drought is crucial for developing crops with enhanced resilience. In this study, we investigated the transcriptomic responses of cultivated tomato (Solanum lycopersicum) and its drought-tolerant wild relative,Solanum pennellii, to identify “stress-ready” gene expression patterns associated with pre-adaptation to arid environments. Through RNA-seq analysis, we identified orthologous genes between the two species and compared their transcriptomic profiles under both control and drought conditions. Approximately 43% of the orthologous genes exhibited species-specific expression patterns, while nearly 20% were classified as stress-ready. These stress-ready genes were significantly enriched for functions related to nucleosome assembly, RNA metabolism, and transcriptional regulation. Furthermore, transcription factor binding motif analysis revealed a marked enrichment of ERF family motifs, emphasizing their role in both stress-ready and species-specific responses. Our findings indicate that regulatory mechanisms, particularly those mediated by ERF transcription factors, are pivotal to the drought resilience ofS. pennellii, providing a foundation for future crop improvement strategies. 

Database and data structure research can improve machine learning performance in many ways. One way is to design better algorithms on data structures. This paper combines the use of incremental computation as well as sequential and probabilistic filtering to enable “forgetful” tree-based learning algorithms to cope with streaming data that suffers from concept drift. (Concept drift occurs when the functional mapping from input to classification changes over time). The forgetful algorithms described in this paper achieve high performance while maintaining high quality predictions on streaming data. Specifically, the algorithms are up to 24 times faster than state-of-the-art incremental algorithms with, at most, a 2% loss of accuracy, or are at least twice faster without any loss of accuracy. This makes such structures suitable for high volume streaming applications. 

Nitrogen (N) and Water (W) - two resources critical for crop productivity – are becoming increasingly limited in soils globally. To address this issue, we aim to uncover the gene regulatory networks (GRNs) that regulate nitrogen use efficiency (NUE) - as a function of water availability - in Oryza sativa, a staple for 3.5 billion people. In this study, we infer and validate GRNs that correlate with rice NUE phenotypes affected by N-by-W availability in the field. We did this by exploiting RNA-seq and crop phenotype data from 19 rice varieties grown in a 2x2 N-by-W matrix in the field. First, to identify gene-to-NUE field phenotypes, we analyzed these datasets using weighted gene co-expression network analysis (WGCNA). This identified two network modules ("skyblue" & "grey60") highly correlated with NUE grain yield (NUEg). Next, we focused on 90 TFs contained in these two NUEg modules and predicted their genome-wide targets using the N-and/or-W response datasets using a random forest network inference approach (GENIE3). Next, to validate the GENIE3 TF→target gene predictions, we performed Precision/Recall Analysis (AUPR) using nine datasets for three TFs validated in planta . This analysis sets a precision threshold of 0.31, used to "prune" the GENIE3 network for high-confidence TF→target gene edges, comprising 88 TFs and 5,716 N-and/or-W response genes. Next, we ranked these 88 TFs based on their significant influence on NUEg target genes responsive to N and/or W signaling. This resulted in a list of 18 prioritized TFs that regulate 551 NUEg target genes responsive to N and/or W signals. We validated the direct regulated targets of two of these candidate NUEg TFs in a plant cell-based TF assay called TARGET, for which we also had in planta data for comparison. Gene ontology analysis revealed that 6/18 NUEg TFs - OsbZIP23 (LOC_Os02g52780), Oshox22 (LOC_Os04g45810), LOB39 (LOC_Os03g41330), Oshox13 (LOC_Os03g08960), LOC_Os11g38870, and LOC_Os06g14670 - regulate genes annotated for N and/or W signaling. Our results show that OsbZIP23 and Oshox22, known regulators of drought tolerance, also coordinate W-responses with NUEg. This validated network can aid in developing/breeding rice with improved yield on marginal, low N-input, drought-prone soils. 

Nitrate is a nutrient and a potent signal that impacts global gene expression in plants. However, the regulatory factors controlling temporal and cell type–specific nitrate responses remain largely unknown. We assayed nitrate-responsive transcriptome changes in five major root cell types of the Arabidopsis thaliana root as a function of time. We found that gene-expression response to nitrate is dynamic and highly localized and predicted cell type–specific transcription factor (TF)–target interactions. Among cell types, the endodermis stands out as having the largest and most connected nitrate-regulatory gene network. ABF2 and ABF3 are major hubs for transcriptional responses in the endodermis cell layer. We experimentally validated TF–target interactions for ABF2 and ABF3 by chromatin immunoprecipitation followed by sequencing and a cell-based system to detect TF regulation genome-wide. Validated targets of ABF2 and ABF3 account for more than 50% of the nitrate-responsive transcriptome in the endodermis. Moreover, ABF2 and ABF3 are involved in nitrate-induced lateral root growth. Our approach offers an unprecedented spatiotemporal resolution of the root response to nitrate and identifies important components of cell-specific gene regulatory networks.