Arabidopsis transcriptome responses to low water potential using high-throughput plate assays.

Soil-free assays that induce water stress are routinely used to investigate drought responses in the plant Arabidopsis thaliana. Due to their ease of use, the research community often relies on polyethylene glycol (PEG), mannitol, and salt (NaCl) treatments to reduce the water potential of agar media, and thus induce drought conditions in the laboratory. However, while these types of stress can create phenotypes that resemble those of water deficit experienced by soil-grown plants, it remains unclear how these treatments compare at the transcriptional level. Here, we demonstrate that these different methods of lowering water potential elicit both shared and distinct transcriptional responses in Arabidopsis shoot and root tissue. When we compared these transcriptional responses to those found in Arabidopsis roots subject to vermiculite drying, we discovered many genes induced by vermiculite drying were repressed by low water potential treatments on agar plates (and vice versa). Additionally, we also tested another method for lowering water potential of agar media. By increasing the nutrient content and tensile strength of agar, we show the 'hard agar' (HA) treatment can be leveraged as a high-throughput assay to investigate natural variation in Arabidopsis growth responses to low water potential.

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

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.

The biology of time: dynamic responses of cell types to developmental, circadian and environmental cues.

As sessile organisms, plants are finely tuned to respond dynamically to developmental, circadian and environmental cues. Genome-wide studies investigating these types of cues have uncovered the intrinsically different ways they can impact gene expression over time. Recent advances in single-cell sequencing and time-based bioinformatic algorithms are now beginning to reveal the dynamics of these time-based responses within individual cells and plant tissues. Here, we review what these techniques have revealed about the spatiotemporal nature of gene regulation, paying particular attention to the three distinct ways in which plant tissues are time sensitive. (i) First, we discuss how studying plant cell identity can reveal developmental trajectories hidden in pseudotime. (ii) Next, we present evidence that indicates that plant cell types keep their own local time through tissue-specific regulation of the circadian clock. (iii) Finally, we review what determines the speed of environmental signaling responses, and how they can be contingent on developmental and circadian time. By these means, this review sheds light on how these different scales of time-based responses can act with tissue and cell-type specificity to elicit changes in whole plant systems.

Effect of the spatial-temporal specific theca cell Cyp17 overexpression on the reproductive phenotype of the novel TC17 mouse.

BACKGROUND: In the ovarian follicle, the Theca Cells (TCs) have two main functions: preserving morphological integrity and, importantly, secreting steroid androgen hormones. TCs express the essential enzyme 17α-hydroxylase/17,20-desmolase (CYP17), which permits the conversion of pregnenolone and progesterone into androgens. Dysregulation of CYP17 enzyme activity due to an intrinsic ovarian defect is hypothesized to be a cause of hyperandrogenism in women. Androgen excess is observed in women with polycystic ovary syndrome (PCOS) resulting from excess endogenous androgen production, and in transgender males undergoing exogenous testosterone therapy after female sex assignment at birth. However, the molecular and morphological effects of Cyp17 overexpression and androgen excess on folliculogenesis is unknown. METHODS: In this work, seeking a comprehensive profiling of the local outcomes of the androgen excess in the ovary, we generated a transgenic mouse model (TC17) with doxycycline (Dox)-induced Cyp17 overexpression in a local and temporal manner. TC17 mice were obtained by a combination of the Tet-dependent expression system and the Cre/LoxP gene control system. RESULTS: Ovaries of Dox-treated TC17 mice overexpressed Cyp17 specifically in TCs, inducing high testosterone levels. Surprisingly, TC17 ovarian morphology resembled the human ovarian features of testosterone-treated transgender men (partially impaired folliculogenesis, hypertrophic or luteinized stromal cells, atretic follicles, and collapsed clusters). We additionally assessed TC17 fertility denoting a perturbation of the normal reproductive functions (e.g., low pregnancy rate and numbers of pups per litter). Finally, RNAseq analysis permitted us to identify dysregulated genes (Lhcgr, Fshr, Runx1) and pathways (Extra Cellular Matrix and Steroid Synthesis). CONCLUSIONS: Our novel mouse model is a versatile tool to provide innovative insights into study the effects of Cyp17 overexpression and hyperandrogenism in the ovary.

Time-Based Systems Biology Approaches to Capture and Model Dynamic Gene Regulatory Networks.

All aspects of transcription and its regulation involve dynamic events. However, capturing these dynamic events in gene regulatory networks (GRNs) offers both a promise and a challenge. The promise is that capturing and modeling the dynamic changes in GRNs will allow us to understand how organisms adapt to a changing environment. The ability to mount a rapid transcriptional response to environmental changes is especially important in nonmotile organisms such as plants. The challenge is to capture these dynamic, genome-wide events and model them in GRNs. In this review, we cover recent progress in capturing dynamic interactions of transcription factors with their targets-at both the local and genome-wide levels-and how they are used to learn how GRNs operate as a function of time. We also discuss recent advances that employ time-based machine learning approaches to forecast gene expression at future time points, a key goal of systems biology.

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

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 (Vmax), as well as the N-dose at which one-half of Vmax was achieved (Km) 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 (Vmax), Km, 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.

A balancing act: how plants integrate nitrogen and water signals.

Nitrogen (N) and water (W) are crucial inputs for plant survival as well as costly resources for agriculture. Given their importance, the molecular mechanisms that plants rely on to signal changes in either N or W status have been under intense scrutiny. However, how plants sense and respond to the combination of N and W signals at the molecular level has received scant attention. The purpose of this review is to shed light on what is currently known about how plant responses to N are impacted by W status. We review classic studies which detail how N and W combinations have both synergistic and antagonistic effects on key plant traits, such as root architecture and stomatal aperture. Recent molecular studies of N and W interactions show that mutations in genes involved in N metabolism affect drought responses, and vice versa. Specifically, perturbing key N signaling genes may lead to changes in drought-responsive gene expression programs, which is supported by a meta-analysis we conduct on available transcriptomic data. Additionally, we cite studies that show how combinatorial transcriptional responses to N and W status might drive crop phenotypes. Through these insights, we suggest research strategies that could help to develop crops adapted to marginal soils depleted in both N and W, an important task in the face of climate change.

Network Walking charts transcriptional dynamics of nitrogen signaling by integrating validated and predicted genome-wide interactions.

Charting a temporal path in gene networks requires linking early transcription factor (TF)-triggered events to downstream effects. We scale-up a cell-based TF-perturbation assay to identify direct regulated targets of 33 nitrogen (N)-early response TFs encompassing 88% of N-responsive Arabidopsis genes. We uncover a duality where each TF is an inducer and repressor, and in vitro cis-motifs are typically specific to regulation directionality. Validated TF-targets (71,836) are used to refine precision of a time-inferred root network, connecting 145 N-responsive TFs and 311 targets. These data are used to chart network paths from direct TF1-regulated targets identified in cells to indirect targets responding only in planta via Network Walking. We uncover network paths from TGA1 and CRF4 to direct TF2 targets, which in turn regulate 76% and 87% of TF1 indirect targets in planta, respectively. These results have implications for N-use and the approach can reveal temporal networks for any biological system.

Water impacts nutrient dose responses genome-wide to affect crop production.

Changes in nutrient dose have dramatic effects on gene expression and development. One outstanding question is whether organisms respond to changes in absolute nutrient amount (moles) vs. its concentration in water (molarity). This question is particularly relevant to plants, as soil drying can alter nutrient concentration, without changing its absolute amount. To compare the effects of amount vs. concentration, we expose rice to a factorial matrix varying the dose of nitrogen (N) and water (W) over a range of combinations, and quantify transcriptome and phenotype responses. Using linear models, we identify distinct dose responses to either N-moles, W-volume, N-molarity (N/W), or their synergistic interaction (N×W). Importantly, genes whose expression patterns are best explained by N-dose and W interactions (N/W or N×W) in seedlings are associated with crop outcomes in replicated field trials. Such N-by-W responsive genes may assist future efforts to develop crops resilient to increasingly arid, low nutrient soils.

A matter of time - How transient transcription factor interactions create dynamic gene regulatory networks.

Dynamic reprogramming of transcriptional networks enables cells to adapt to a changing environment. Thus, it is crucial not only to understand what gene targets are regulated by a transcription factor (TF) but also when. This review explores the way TFs function with respect to time, paying particular attention to discoveries made in plants - where coordinated, genome-wide responses to environmental change is crucial to the survival of these sessile organisms. We investigate the molecular mechanisms that mediate transient TF-DNA binding, and assess how these rapid and dynamic interactions translate to long-term temporal regulation of genomes. We also discuss how current molecular techniques can catch, and sometimes miss, transient TF-target interactions that underlie dynamic cellular responses. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.

Identification of aggregation inhibitors of the human antibody light chain repertoire by phage display.

Protein aggregation hinders the development of biologics and underpins the molecular basis of many human diseases. Considerable variation of aggregation propensity exists not only between different proteins, but also within a single homologous family, which complicates analyses. A classic example is observed among human antibody light chains, which aggregate in a clonally specific manner, driven by sequence diversity within their variable domains. Here, we utilise a library versus library strategy, based on phage display and a chemical library of FDA approved drugs, to overcome this limitation. Our approach allowed the identification of small molecule drugs that inhibit the aggregation of the human light chain repertoire. It also provides a general template for the small molecule targeting of diverse protein families.