Temporal transcriptional logic of dynamic regulatory networks underlying nitrogen signaling and use in plants

Varala, Kranthi; Marshall‐Colón, Amy; Cirrone, Jacopo; Brooks, Matthew D.; Pasquino, Angelo; Léran, Sophie; Mittal, Shipra; Rock, Tara M.; Edwards, Molly B.; Kim, Grace J.; Ruffel, Sandrine; McCombie, W. Richard; Shasha, Dennis; Coruzzi, Gloria M.

doi:10.1073/pnas.1721487115

Cited by 157 publications

(232 citation statements)

References 49 publications

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“…Recent work uncovered a temporal transcriptional logic underlying nitrogen (N) signaling in Arabidopsis [43]; we see similar logic based on developmental timing for abiotic stress responses. Consider the example of heat stress: the ME_tan module was correlated with V3 heat stress (r 2 =0.89, p <4e-32), the ME_yellow module correlated with V4 heat stresses (r 2 =0.96, p <1e-49), and the ME_darkturquoise (r 2 =0.43, p <2e-05) and ME_pink (r 2 =0.49, p <1e-06) modules were associated with heat in the V6 stage.…”

Section: Resultsmentioning

confidence: 69%

Transposable elements contribute to regulatory hub stress-related long noncoding RNAs in Maize

Zhou

et al. 2019

Preprint

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Several studies have mined short-read RNA sequencing datasets to identify lncRNAs, and others have focused on the function of individual lncRNA in abiotic stress response. However, our understanding of the complement, function and origin of long-non-coding RNA (lncRNAs) response to abiotic stress, especially transposon derived lncRNA (TE-lncRNA), is still in its infancy. To discover and study lncRNAs in maize (Zea mays ssp. mays), we utilized a dataset of 127 RNA sequencing samples that included PacBio fl-cDNA and total RNA-Seq datasets. Overall, we identified 23,309 candidate lncRNAs, 60% of which were identified in polyadenylated (polyA+) samples. The majority (65%) of the 23,309 lncRNAs had sequence similarity to transposable elements (TEs). Most had similarity to long-terminal-repeat retrotransposons from the Copia and Gypsy superfamilies, representing the high proportion of these elements in the genome, but class II, DNA transposons were enriched for lncRNAs relative to their genomic representation by 2-fold. By assessing the fraction of lncRNAs that respond to abiotic stresses like heat, cold, salt and drought, we identified 1,077 differentially expressed lncRNA transcripts. Their expression was correlated (r2=0.48) with their nearest gene, suggesting that lncRNAs are subject to some of the cis regulatory features as neighboring genes. By inferring co-expression networks across our large dataset, we found that 39 lncRNAs act as major hubs in co-expression networks, of which 18 appeared to be derived from TEs. These results suggest that lncRNAs, especially TE-lncRNAs, may play key regulatory roles in moderating abiotic responses.

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Section: Resultsmentioning

confidence: 69%

Transposable elements contribute to regulatory hub stress-related long noncoding RNAs in Maize

Zhou

et al. 2019

Preprint

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“…We compare the OutPredict method to the state-of-the-art forecasting algorithms, such as Dynamic Genie3 9 , that support forecasting and non-linear relationships, but currently lack the ability to incorporate priors. Other time-based machine learning methods such as Inferelator 6 and Dynamic Factor Graph 10 , which we used in our previous studies 11,12 are based on regularized linear regression. We also compare OutPredict with a neural net-based method built to predict gene expression time series 13 .…”

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confidence: 99%

OutPredict: multiple datasets can improve prediction of expression and inference of causality

Cirrone

Brooks

Bonneau

et al. 2020

Sci Rep

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the ability to accurately predict the causal relationships from transcription factors to genes would greatly enhance our understanding of transcriptional dynamics. this could lead to applications in which one or more transcription factors could be manipulated to effect a change in genes leading to the enhancement of some desired trait. Here we present a method called outpredict that constructs a model for each gene based on time series (and other) data and that predicts gene's expression in a previously unseen subsequent time point. the model also infers causal relationships based on the most important transcription factors for each gene model, some of which have been validated from previous physical experiments. The method benefits from known network edges and steady-state data to enhance predictive accuracy. our results across B. subtilis, Arabidopsis, e.coli, Drosophila and the DREAM4 simulated in silico dataset show improved predictive accuracy ranging from 40% to 60% over other state-of-the-art methods. We find that gene expression models can benefit from the addition of steady-state data to predict expression values of time series. Finally, we validate, based on limited available data, that the influential edges we infer correspond to known relationships significantly more than expected by chance or by state-of-the-art methods.

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“…Moreover, most biological processes are dynamic, and by collecting transcriptome data of high-resolution time series the regulatory networks and their sub-modules underlying gene expression programs can be more accurately deciphered. Several of these large-scale transcriptome studies have generated key biological and regulatory insights, including successful inference of GRN models for a wide-range of plant responses, including for example, development, hormone signalling, and abiotic and biotic stresses (Brady et al, 2007; Krouk et al, 2010; Breeze et al, 2011; Bechtold et al, 2016; Song et al, 2016; Hickman et al, 2017; Hillmer et al, 2017; Mine et al, 2018; Varala et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Transcriptional Dynamics of the Salicylic Acid Response and its Interplay with the Jasmonic Acid Pathway

Hickman

Mendes

Verk

et al. 2019

Preprint

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The phytohormone salicylic acid (SA) is a central regulator of plant immunity.Antagonistic and synergistic actions between SA and other defense-associated hormones like jasmonic acid (JA) play key roles in determining the outcome of the plant immune response. To obtain a deeper understanding of SA-mediated transcriptional reprogramming and SA/JA crosstalk, we generated a high-resolution time series of gene expression from Arabidopsis leaves treated with SA alone and a combination of SA and methyl JA (MeJA), sampled at 14 time points over a 16-h period. We found that approximately one-third of the Arabidopsis genome was differentially expressed in response to SA, and temporal changes in gene expression could be partitioned into 45 distinct clusters of process-specific coregulated genes, linked to specific cis-regulatory elements and binding of transcription factors (TFs).Integration of our expression data with information on TF-DNA binding allowed us to generate a dynamic gene regulatory network model of the SA response, recovering known regulators and identifying novel ones. We found that 12% of SA-responsive genes and 69% of the MeJA-responsive genes exhibited antagonistic or synergistic expression levels in the combination treatment. Multi-condition co-clustering of the single-and combined-hormone expression profiles predicted underlying regulatory mechanisms in signal integration. Finally, we identified the TFs ANAC061 and ANAC090 as negative regulators of SA pathway genes and defense against biotrophic pathogens. Collectively, our data provide an unprecedented level of detail about transcriptional changes during the SA response and SA/JA crosstalk, serving as a valuable resource for systems-level network studies and functional plant defense studies.

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Temporal transcriptional logic of dynamic regulatory networks underlying nitrogen signaling and use in plants

Cited by 157 publications

References 49 publications

Transposable elements contribute to regulatory hub stress-related long noncoding RNAs in Maize

Transposable elements contribute to regulatory hub stress-related long noncoding RNAs in Maize

OutPredict: multiple datasets can improve prediction of expression and inference of causality

Transcriptional Dynamics of the Salicylic Acid Response and its Interplay with the Jasmonic Acid Pathway

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