Cross-Species Network Analysis Uncovers Conserved Nitrogen-Regulated Network Modules in Rice

In this study, we used a cross-species network approach to uncover nitrogen-regulated network modules conserved across a model and a crop species. By translating gene “network knowledge” from the data-rich model Arabidopsis (Arabidopsis thaliana) to a crop (Oryza sativa), we identified evolutionarily conserved N-regulatory modules as targets for translational studies to improve N-use efficiency in transgenic plants. To uncover such conserved N-regulatory network modules, we first generated a N-regulatory network based solely on rice (O. sativa) transcriptome and gene interaction data. Next, we enhanced the “network knowledge” in the rice N-regulatory network using transcriptome and gene interaction data from Arabidopsis and new data from Arabidopsis and rice plants exposed to the same N-treatment conditions. This cross-species network analysis uncovered a set of N-regulated transcription factors (TFs) predicted to target the same genes and network modules in both species. Supernode analysis of the TFs and their targets in these conserved network modules uncovered genes directly related to nitrogen use (e.g. N-assimilation) and to other shared biological processes indirectly related to nitrogen. This cross-species network approach was validated with members of two TF families in the supernode network, bZIP-TGA and HRS1/HHO family, have recently been experimentally validated to mediate the N-response in Arabidopsis.Fil: Obertello, Mariana. University of New York; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular ; ArgentinaFil: Shrivastava, Stuti. University of New York; Estados UnidosFil: Katari, Manpreet S.. University of New York; Estados UnidosFil: Coruzzi, Gloria M.. University of New York; Estados Unido

Mesotherapy versus Systemic Therapy in the Treatment of Acute Low Back Pain: A Randomized Trial

Pharmacological therapy of back pain with analgesics and anti-inflammatory drugs is frequently associated with adverse effects, particularly in the elderly. Aim of this study was to compare mesotherapic versus conventional systemic administration of nonsteroidal anti-inflammatory drugs (NSAIDs) and corticosteroids in patients with acute low back pain. Eighty-four patients were randomized to receive anti-inflammatory therapy according to the following protocols: (a) mesotherapy group received the 1st and 4th day 2% lidocaine (1 mL) + ketoprofen 160 mg (1 mL) + methylprednisolone 40 mg (1 mL), then on 7th, 10th, and 13th day, 2% lidocaine (1 mL) + ketoprofen 160 mg (1 mL) + methylprednisolone 20 mg (1 mL) (b) conventional therapy group received ketoprofen 80 mg × 2/die and esomeprazole 20 mg/die orally for 12 days, methylprednisolone 40 mg/die intramuscularly for 4 days, followed by methylprednisolone 20 mg/die for 3 days, and thereafter, methylprednisolone 20 mg/die at alternate days. Pain intensity and functional disability were assessed at baseline (T0), at the end of treatment (T1), and 6 months thereafter (T2) by using visual analogic scale (VAS) and Roland-Morris disability questionnaire (RMDQ). In both groups, VAS and RMDQ values were significantly reduced at the end of drug treatment and after 6 months, in comparison with baseline. No significant differences were found between the two groups. This suggests that mesotherapy may be a valid alternative to conventional therapy in the treatment of acute low back pain with corticosteroids and NSAIDs

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

Multiscale Sample Entropy of Cardiovascular Signals: Does the Choice between Fixed- or Varying-Tolerance among Scales Influence Its Evaluation and Interpretation?

Multiscale entropy (MSE) quantifies the cardiovascular complexity evaluating Sample Entropy (SampEn) on coarse-grained series at increasing scales τ. Two approaches exist, one using a fixed tolerance r at all scales (MSEFT), the other a varying tolerance r(τ) adjusted following the standard-deviation changes after coarse graining (MSEVT). The aim of this study is to clarify how the choice between MSEFT and MSEVT influences quantification and interpretation of cardiovascular MSE, and whether it affects some signals more than others. To achieve this aim, we considered 2-h long beat-by-beat recordings of inter-beat intervals and of systolic and diastolic blood pressures in male (N = 42) and female (N = 42) healthy volunteers. We compared MSE estimated with fixed and varying tolerances, and evaluated whether the choice between MSEFT and MSEVT estimators influence quantification and interpretation of sex-related differences. We found substantial discrepancies between MSEFT and MSEVT results, related to the degree of correlation among samples and more important for heart rate than for blood pressure; moreover the choice between MSEFT and MSEVT may influence the interpretation of gender differences for MSE of heart rate. We conclude that studies on cardiovascular complexity should carefully choose between fixed- or varying-tolerance estimators, particularly when evaluating MSE of heart rate

Sodium intake and hypertension

The close relationship between hypertension and dietary sodium intake is widely recognized and supported by several studies. A reduction in dietary sodium not only decreases the blood pressure and the incidence of hypertension, but is also associated with a reduction in morbidity and mortality from cardiovascular diseases. Prolonged modest reduction in salt intake induces a relevant fall in blood pressure in both hypertensive and normotensive individuals, irrespective of sex and ethnic group, with larger falls in systolic blood pressure for larger reductions in dietary salt. The high sodium intake and the increase in blood pressure levels are related to water retention, increase in systemic peripheral resistance, alterations in the endothelial function, changes in the structure and function of large elastic arteries, modification in sympathetic activity, and in the autonomic neuronal modulation of the cardiovascular system. In this review, we have focused on the effects of sodium intake on vascular hemodynamics and their implication in the pathogenesis of hypertensio

Genome-wide patterns of carbon and nitrogen regulation of gene expression validate the combined carbon and nitrogen (CN)-signaling hypothesis in plants

BACKGROUND: Carbon and nitrogen are two signals that influence plant growth and development. It is known that carbon- and nitrogen-signaling pathways influence one another to affect gene expression, but little is known about which genes are regulated by interactions between carbon and nitrogen signaling or the mechanisms by which the different pathways interact. RESULTS: Microarray analysis was used to study global changes in mRNA levels due to carbon and nitrogen in Arabidopsis thaliana. An informatic analysis using InterAct Class enabled us to classify genes on the basis of their responses to carbon or nitrogen treatments. This analysis provides in vivo evidence supporting the hypothesis that plants have a carbon/nitrogen (CN)-sensing/regulatory mechanism, as we have identified over 300 genes whose response to combined CN treatment is different from that expected from expression values due to carbon and nitrogen treatments separately. Metabolism, energy and protein synthesis were found to be significantly affected by interactions between carbon and nitrogen signaling. Identified putative cis-acting regulatory elements involved in mediating CN-responsive gene expression suggest multiple mechanisms for CN responsiveness. One mechanism invokes the existence of a single CN-responsive cis element, while another invokes the existence of cis elements that promote nitrogen-responsive gene expression only when present in combination with a carbon-responsive cis element. CONCLUSION: This study has allowed us to identify genes and processes regulated by interactions between carbon and nitrogen signaling and take a first step in uncovering how carbon- and nitrogen-signaling pathways interact to regulate transcription

In Silico Evaluation of Predicted Regulatory Interactions in Arabidopsis thaliana

<p>Abstract</p> <p>Background</p> <p>Prediction of transcriptional regulatory mechanisms in <it>Arabidopsis </it>has become increasingly critical with the explosion of genomic data now available for both gene expression and gene sequence composition. We have shown in previous work <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>, that a combination of correlation measurements and <it>cis</it>-regulatory element (CRE) detection methods are effective in predicting targets for candidate transcription factors for specific case studies which were validated. However, to date there has been no quantitative assessment as to which correlation measures or CRE detection methods used alone or in combination are most effective in predicting TF→target relationships on a genome-wide scale.</p> <p>Results</p> <p>We tested several widely used methods, based on correlation (Pearson and Spearman Rank correlation) and <it>cis-</it>regulatory element (CRE) detection (≥1 CRE or CRE over-representation), to determine which of these methods individually or in combination is the most effective by various measures for making regulatory predictions. To predict the regulatory targets of a transcription factor (TF) of interest, we applied these methods to microarray expression data for genes that were regulated over treatment and control conditions in wild type (WT) plants. Because the chosen data sets included identical experimental conditions used on TF over-expressor or T-DNA knockout plants, we were able to test the TF→target predictions made using microarray data from WT plants, with microarray data from mutant/transgenic plants. For each method, or combination of methods, we computed sensitivity, specificity, positive and negative predictive value and the F-measure of balance between sensitivity and positive predictive value (precision). This analysis revealed that the ≥1 CRE and Spearman correlation (used alone or in combination) were the most balanced CRE detection and correlation methods, respectively with regard to their power to accurately predict regulatory-target interactions.</p> <p>Conclusion</p> <p>These findings provide an approach and guidance for researchers interested in predicting transcriptional regulatory mechanisms using microarray data that they generate (or microarray data that is publically available) combined with CRE detection in promoter sequence data.</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