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1. The regulatory landscape of Arabidopsis thaliana roots at single-cell resolution

   https://doi.org/10.1038/s41467-021-23675-y  

   Dorrity, Michael W.; Alexandre, Cristina M.; Hamm, Morgan O.; Vigil, Anna-Lena; Fields, Stanley; Queitsch, Christine; Cuperus, Josh T. (June 2021, Nature Communications)

   Abstract The scarcity of accessible sites that are dynamic or cell type-specific in plants may be due in part to tissue heterogeneity in bulk studies. To assess the effects of tissue heterogeneity, we apply single-cell ATAC-seq toArabidopsis thalianaroots and identify thousands of differentially accessible sites, sufficient to resolve all major cell types of the root. We find that the entirety of a cell’s regulatory landscape and its transcriptome independently capture cell type identity. We leverage this shared information on cell identity to integrate accessibility and transcriptome data to characterize developmental progression, endoreduplication and cell division. We further use the combined data to characterize cell type-specific motif enrichments of transcription factor families and link the expression of family members to changing accessibility at specific loci, resolving direct and indirect effects that shape expression. Our approach provides an analytical framework to infer the gene regulatory networks that execute plant development. 

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2. A new tool for discovering transcriptional regulators of co-expressed genes predicts gene regulatory networks that mediate ethylene-controlled root development

   https://doi.org/10.1093/insilicoplants/diaa006  

   Harkey, Alexandria F; Sims, Kira N; Muday, Gloria K (January 2020, in silico Plants)

   Marshall-Colon, Amy (Ed.)

   Abstract Gene regulatory networks (GRNs) are defined by a cascade of transcriptional events by which signals, such as hormones or environmental cues, change development. To understand these networks, it is necessary to link specific transcription factors (TFs) to the downstream gene targets whose expression they regulate. Although multiple methods provide information on the targets of a single TF, moving from groups of co-expressed genes to the TF that controls them is more difficult. To facilitate this bottom-up approach, we have developed a web application named TF DEACoN. This application uses a publicly available Arabidopsis thaliana DNA Affinity Purification (DAP-Seq) data set to search for TFs that show enriched binding to groups of co-regulated genes. We used TF DEACoN to examine groups of transcripts regulated by treatment with the ethylene precursor 1-aminocyclopropane-1-carboxylic acid (ACC), using a transcriptional data set performed with high temporal resolution. We demonstrate the utility of this application when co-regulated genes are divided by timing of response or cell-type-specific information, which provides more information on TF/target relationships than when less defined and larger groups of co-regulated genes are used. This approach predicted TFs that may participate in ethylene-modulated root development including the TF NAM (NO APICAL MERISTEM). We used a genetic approach to show that a mutation in NAM reduces the negative regulation of lateral root development by ACC. The combination of filtering and TF DEACoN used here can be applied to any group of co-regulated genes to predict GRNs that control coordinated transcriptional responses. 

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3. Nutrient dose-responsive transcriptome changes driven by Michaelis–Menten kinetics underlie plant growth rates

   https://doi.org/10.1073/pnas.1918619117  

   Swift, Joseph; Alvarez, Jose M.; Araus, Viviana; Gutiérrez, Rodrigo A.; Coruzzi, Gloria M. (June 2020, Proceedings of the National Academy of Sciences)

   null (Ed.)

   An increase in nutrient dose leads to proportional increases in crop biomass and agricultural yield. However, the molecular underpinnings of this nutrient dose–response are largely unknown. To investigate, we assayed changes in the Arabidopsis root transcriptome to different doses of nitrogen (N)—a key plant nutrient—as a function of time. By these means, we found that rate changes of genome-wide transcript levels in response to N-dose could be explained by a simple kinetic principle: the Michaelis–Menten (MM) model. Fitting the MM model allowed us to estimate the maximum rate of transcript change ( V max ), as well as the N-dose at which one-half of V max was achieved ( K m ) for 1,153 N-dose–responsive genes. Since transcription factors (TFs) can act in part as the catalytic agents that determine the rates of transcript change, we investigated their role in regulating N-dose–responsive MM-modeled genes. We found that altering the abundance of TGA1, an early N-responsive TF, perturbed the maximum rates of N-dose transcriptomic responses ( V max ), K m , as well as the rate of N-dose–responsive plant growth. We experimentally validated that MM-modeled N-dose–responsive genes included both direct and indirect TGA1 targets, using a root cell TF assay to detect TF binding and/or TF regulation genome-wide. Taken together, our results support a molecular mechanism of transcriptional control that allows an increase in N-dose to lead to a proportional change in the rate of genome-wide expression and plant growth. 

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4. Cell‐type‐aware regulatory landscapes governing monoterpene indole alkaloid biosynthesis in the medicinal plant Catharanthus roseus

   https://doi.org/10.1111/nph.20208  

   Li, Chenxin; Colinas, Maite; Wood, Joshua_C; Vaillancourt, Brieanne; Hamilton, John_P; Jones, Sophia_L; Caputi, Lorenzo; O'Connor, Sarah_E; Buell, C_Robin (October 2024, New Phytologist)

   Summary In plants, the biosynthetic pathways of some specialized metabolites are partitioned into specialized or rare cell types, as exemplified by the monoterpenoid indole alkaloid (MIA) pathway ofCatharanthus roseus(Madagascar Periwinkle), the source of the anticancer compounds vinblastine and vincristine. In the leaf, theC. roseusMIA biosynthetic pathway is partitioned into three cell types with the final known steps of the pathway expressed in the rare cell type termed idioblast. How cell‐type specificity of MIA biosynthesis is achieved is poorly understood.We generated single‐cell multi‐omics data fromC. roseusleaves. Integrating gene expression and chromatin accessibility profiles across single cells, as well as transcription factor (TF)‐binding site profiles, we constructed a cell‐type‐aware gene regulatory network for MIA biosynthesis.We showcased cell‐type‐specific TFs as well as cell‐type‐specificcis‐regulatory elements. Using motif enrichment analysis, co‐expression across cell types, and functional validation approaches, we discovered a novel idioblast‐specific TF (Idioblast MYB1,CrIDM1) that activates expression of late‐stage MIA biosynthetic genes in the idioblast.These analyses not only led to the discovery of the first documented cell‐type‐specific TF that regulates the expression of two idioblast‐specific biosynthetic genes within an idioblast metabolic regulon but also provides insights into cell‐type‐specific metabolic regulation. 

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5. Validation of a high-confidence regulatory network for gene-to-NUE phenotype in field-grown rice

   https://doi.org/10.3389/fpls.2022.1006044  

   Shanks, Carly M.; Huang, Ji; Cheng, Chia-Yi; Shih, Hung-Jui S.; Brooks, Matthew D.; Alvarez, José M.; Araus, Viviana; Swift, Joseph; Henry, Amelia; Coruzzi, Gloria M. (November 2022, Frontiers in Plant Science)

   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. 

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