A system biology approach highlights a hormonal enhancer effect on regulation of genes in a nitrate responsive "biomodule"

<p>Abstract</p> <p>Background</p> <p>Nitrate-induced reprogramming of the transcriptome has recently been shown to be highly context dependent. Herein, a systems biology approach was developed to identify the components and role of cross-talk between nitrate and hormone signals, likely to be involved in the conditional response of NO₃^-signaling.</p> <p>Results</p> <p>Biclustering was used to identify a set of genes that are N-responsive across a range of Nitrogen (N)-treatment backgrounds (i.e. nitrogen treatments under different growth conditions) using a meta-dataset of 76 Affymetrix ATH1 chips from 5 different laboratories. Twenty-one biclusters were found to be N-responsive across subsets of this meta-dataset. <it>N-bicluster 9 </it>(126 genes) was selected for further analysis, as it was shown to be reproducibly responsive to NO₃^-as a signal, across a wide-variety of background conditions and datasets. <it>N-bicluster 9 </it>genes were then used as "seed" to identify putative cross-talk mechanisms between nitrate and hormone signaling. For this, the 126 nitrate-regulated genes in <it>N-bicluster 9 </it>were biclustered over a meta-dataset of 278 ATH1 chips spanning a variety of hormone treatments. This analysis divided the bicluster 9 genes into two classes: i) genes controlled by NO₃^-only <it>vs</it>. ii) genes controlled by <it>both </it>NO₃^-and hormones. The genes in the latter group showed a NO₃^-response that is significantly enhanced, compared to the former. <it>In silico </it>analysis identified two Cis-Regulatory Elements candidates (CRE) (E2F, HSE) potentially involved the interplay between NO₃^-and hormonal signals.</p> <p>Conclusion</p> <p>This systems analysis enabled us to derive a hypothesis in which hormone signals are proposed to enhance the nitrate response, providing a potential mechanistic explanation for the link between nitrate signaling and the control of plant development.</p

Predictive network modeling of the high-resolution dynamic plant transcriptome in response to nitrate

International audienceABSTRACT: BACKGROUND: Nitrate, acting as both a nitrogen source and a signaling molecule, controls many aspects of plant development. However, gene networks involved in plant adaptation to fluctuating nitrate environments have not yet been identified. RESULTS: Here we use time-series transcriptome data to decipher gene relationships and consequently to build core regulatory networks involved in Arabidopsis root adaptation to nitrate provision. The experimental approach has been to monitor genome-wide responses to nitrate at 3, 6, 9, 12, 15 and 20 minutes, using Affymetrix ATH1 gene chips. This high-resolution time course analysis demonstrated that the previously known primary nitrate response is actually preceded by a very fast gene expression modulation, involving genes and functions needed to prepare plants to use or reduce nitrate. A state-space model inferred from this microarray time-series data successfully predicts gene behavior in unlearnt conditions. CONCLUSIONS: The experiments and methods allow us to propose a temporal working model for nitrate-driven gene networks. This network model is tested both in silico and experimentally. For example, the over-expression of a predicted gene hub encoding a transcription factor induced early in the cascade indeed leads to the modification of the kinetic nitrate response of sentinel genes such as NIR, NIA2, and NRT1.1, and several other transcription factors. The potential nitrate /hormone connections implicated by this time-series data is also evaluated

Modeling the global effect of the basic-leucine zipper transcription factor 1 (bZIP1) on nitrogen and light regulation in Arabidopsis

Background: Nitrogen and light are two major regulators of plant metabolism and development. While genes involved in the control of each of these signals have begun to be identified, regulators that integrate gene responses to nitrogen and light signals have yet to be determined. Here, we evaluate the role of bZIP1, a transcription factor involved in light and nitrogen sensing, by exposing wild-type (WT) and bZIP1 T-DNA null mutant plants to a combinatorial space of nitrogen (N) and light (L) treatment conditions and performing transcriptome analysis. We use ANOVA analysis combined with clustering and Boolean modeling, to evaluate the role of bZIP1 in mediating L and N signaling genome-wide. Results: This transcriptome analysis demonstrates that a mutation in the bZIP1 gene can alter the L and/or N-regulation of several gene clusters. More surprisingly, the bZIP1 mutation can also trigger N and/or L regulation of genes that are not normally controlled by these signals in WT plants. This analysis also reveals that bZIP1 can, to a large extent, invert gene regulation (e. g., several genes induced by N in WT plants are repressed by N in the bZIP1 mutant). Conclusion: These findings demonstrate that the bZIP1 mutation triggers a genome-wide de-regulation in response to L and/or N signals that range from i) a reduction of the L signal effect, to ii) unlocking gene regulation in response to L and N combinations. This systems biology approach demonstrates that bZIP1 tunes L and N signaling relationships genome-wide, and can suppress regulatory mechanisms hypothesized to be needed at different developmental stages and/or environmental conditions

Transient genome-wide interactions of the master transcription factor NLP7 initiate a rapid nitrogen-response cascade

Dynamic reprogramming of gene regulatory networks (GRNs) enables organisms to rapidly respond to environmental perturbation. However, the underlying transient interactions between transcription factors (TFs) and genome-wide targets typically elude biochemical detection. Here, we capture both stable and transient TF-target interactions genome-wide within minutes after controlled TF nuclear import using time-series chromatin immunoprecipitation (ChIP-seq) and/or DNA adenine methyltransferase identification (DamID-seq). The transient TF-target interactions captured uncover the early mode-of-action of NIN-LIKE PROTEIN 7 (NLP7), a master regulator of the nitrogen signaling pathway in plants. These transient NLP7 targets captured in root cells using temporal TF perturbation account for 50% of NLP7-regulated genes not detectably bound by NLP7 in planta. Rapid and transient NLP7 binding activates early nitrogen response TFs, which we validate to amplify the NLP7-initiated transcriptional cascade. Our approaches to capture transient TF-target interactions genome-wide can be applied to validate dynamic GRN models for any pathway or organism of interest. Conventional methods cannot reveal transient transcription factors (TFs) and targets interactions. Here, Alvarez et al. capture both stable and transient TF-target interactions by time-series ChIP-seq and/or DamID-seq in a cell-based TF perturbation system and show NLP7 as a master TF to initiate a rapid nitrogen-response cascade

Identification of molecular integrators shows that nitrogen activelycontrolsthephosphatestarvationresponseinplants

Nitrogen (N) and phosphorus (P) are key macronutrients sustaining plant growth and crop yield and ensuring food security worldwide. Understanding how plants perceive and interpret the combinatorial nature of these signals thus has important agricultural implications within the context of (1) increased food demand, (2) limited P supply, and (3) environmental pollution due to N fertilizer usage. Here, we report the discovery of an active control of P starvation response (PSR) by a combination of local and long-distance N signaling pathways in plants. We show that, in Arabidopsis (Arabidopsis thaliana), the nitrate transceptor CHLORINA1/NITRATE TRANSPORTER1.1 (CHL1/NRT1.1) is a component of this signaling crosstalk. We also demonstrate that this crosstalk is dependent on the control of the accumulation and turnover by N of the transcription factor PHOSPHATE STARVATION RESPONSE1 (PHR1), a master regulator of P sensing and signaling. We further show an important role of PHOSPHATE2 (PHO2) as an integrator of the N availability into the PSR since the effect of N on PSR is strongly affected in pho2 mutants. We finally show that PHO2 and NRT1.1 influence each other’s transcript levels. These observations are summarized in a model representing a framework with several entry points where N signal influence PSR. Finally, we demonstrate that this phenomenon is conserved in rice (Oryza sativa) and wheat (Triticum aestivum), opening biotechnological perspectives in crop plants.This work was supported in the Honude group (Biochemistry & Plant Molecular Physiology) by Agence Nationale de la Recherche (IMANA ANR-14-CE19-0008 with a doctoral fellowship to A.S.), by the Centre National de la Recherche Scientifique (CNRS LIA-CoopNet to G.K.), and by the National Science Foundation (NSF IOS 1339362-NutriNet). Research in V.R.’s laboratory was funded by the Ministry of Economy and Competitiveness and AEI/FEDER/European (grants BIO2013-46539-R and BIO2016-80551-R)

A map of cell type-specific auxin responses

In plants, changes in local auxin concentrations can trigger a range of developmental processes as distinct tissues respond differently to the same auxin stimulus. However, little is known about how auxin is interpreted by individual cell types. We performed a transcriptomic analysis of responses to auxin within four distinct tissues of the Arabidopsis thaliana root and demonstrate that different cell types show competence for discrete responses. The majority of auxin-responsive genes displayed a spatial bias in their induction or repression. The novel data set was used to examine how auxin influences tissue-specific transcriptional regulation of cell-identity markers. Additionally, the data were used in combination with spatial expression maps of the root to plot a transcriptomic auxin-response gradient across the apical and basal meristem. The readout revealed a strong correlation for thousands of genes between the relative response to auxin and expression along the longitudinal axis of the root. This data set and comparative analysis provide a transcriptome-level spatial breakdown of the response to auxin within an organ where this hormone mediates many aspects of development

A Systems Approach Uncovers Restrictions for Signal Interactions Regulating Genome-wide Responses to Nutritional Cues in Arabidopsis

As sessile organisms, plants must cope with multiple and combined variations of signals in their environment. However, very few reports have studied the genome-wide effects of systematic signal combinations on gene expression. Here, we evaluate a high level of signal integration, by modeling genome-wide expression patterns under a factorial combination of carbon (C), light (L), and nitrogen (N) as binary factors in two organs (O), roots and leaves. Signal management is different between C, N, and L and in shoots and roots. For example, L is the major factor controlling gene expression in leaves. However, in roots there is no obvious prominent signal, and signal interaction is stronger. The major signal interaction events detected genome wide in Arabidopsis roots are deciphered and summarized in a comprehensive conceptual model. Surprisingly, global analysis of gene expression in response to C, N, L, and O revealed that the number of genes controlled by a signal is proportional to the magnitude of the gene expression changes elicited by the signal. These results uncovered a strong constraining structure in plant cell signaling pathways, which prompted us to propose the existence of a “code” of signal integration

The next generation of training for arabidopsis researchers: Bioinformatics and Quantitative Biology

It has been more than 50 years since Arabidopsis (Arabidopsis thaliana) was first introduced as a model organism to understand basic processes in plant biology. A well-organized scientific community has used this small reference plant species to make numerous fundamental plant biology discoveries (Provart et al., 2016). Due to an extremely well-annotated genome and advances in high-throughput sequencing, our understanding of this organism and other plant species has become even more intricate and complex. Computational resources, including CyVerse,3 Araport,4 The Arabidopsis Information Resource (TAIR),5 and BAR,6 have further facilitated novel findings with just the click of a mouse. As we move toward understanding biological systems, Arabidopsis researchers will need to use more quantitative and computational approaches to extract novel biological findings from these data. Here, we discuss guidelines, skill sets, and core competencies that should be considered when developing curricula or training undergraduate or graduate students, postdoctoral researchers, and faculty. A selected case study provides more specificity as to the concrete issues plant biologists face and how best to address such challenges