ConnecTF: A platform to build gene networks by integrating transcription factor-target gene interactions

Brooks, Matthew D.; Juang, Che-Lun; Katari, Manpreet S.; Álvarez, José M.; Pasquino, Angelo; Shih, Hung-Jui; Huang, Ji; Shanks, Carly; Cirrone, Jacopo; Coruzzi, Gloria M.

doi:10.1101/2020.07.07.191627

Cited by 3 publications

(15 citation statements)

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“…org (Huynh-Thu et al, 2010;Juang et al, 2020) ( Figure S2a). We then annotated the network by time, node type, direction of expression at bolting and during DILS, and annotation in the LSD 3.0 (Data File S7 contains the fully annotated network).…”

Section: Bolting-associated Gene Regulatory Network (Grn)mentioning

confidence: 99%

Gene expression changes occurring at bolting time are associated with leaf senescence in Arabidopsis

Brusslan

2020

Plant Direct

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In plants, the vegetative to reproductive phase transition (termed bolting in Arabidopsis) generally precedes age‐dependent leaf senescence (LS). Many studies describe a temporal link between bolting time and LS, as plants that bolt early, senesce early, and plants that bolt late, senesce late. The molecular mechanisms underlying this relationship are unknown and are potentially agriculturally important, as they may allow for the development of crops that can overcome early LS caused by stress‐related early‐phase transition. We hypothesized that leaf gene expression changes occurring in synchrony with bolting were regulating LS. ARABIDOPSIS TRITHORAX (ATX) enzymes are general methyltransferases that regulate the adult vegetative to reproductive phase transition. We generated an atx1, atx3, and atx4 (atx1,3,4) triple T‐DNA insertion mutant that displays both early bolting and early LS. This mutant was used in an RNA‐seq time‐series experiment to identify gene expression changes in rosette leaves that are likely associated with bolting. By comparing the early bolting mutant to vegetative WT plants of the same age, we were able to generate a list of differentially expressed genes (DEGs) that change expression with bolting as the plants age. We trimmed the list by intersection with publicly available WT datasets, which removed genes from our DEG list that were atx1,3,4 specific. The resulting 398 bolting‐associated genes (BAGs) are differentially expressed in a mature rosette leaf at bolting. The BAG list contains many well‐characterized LS regulators (ORE1, WRKY45, NAP, WRKY28), and GO analysis revealed enrichment for LS and LS‐related processes. These bolting‐associated LS regulators may contribute to the temporal coupling of bolting time to LS.

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Section: Bolting-associated Gene Regulatory Network (Grn)mentioning

confidence: 99%

Gene expression changes occurring at bolting time are associated with leaf senescence in Arabidopsis

Brusslan

2020

Plant Direct

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“…GENIE3 analysis: TF!target gene predictions: The TF!target gene predictions between 90 TFs highly correlated with the NUE grain yield (NUEg) from WGCNA analysis (Step 2) and the total 10,815 N-and/or-W response genes from Swift et al, 2019 (Step 1) determined using the network inference program GENIE3 resulted in ((90 TFs*10,815 DE genes) -90 TFs) = 973,260 edges or TF!target gene predictions) Step 4. Network validation (AUPR) and "pruning": Validation data for 3 TFs in the GENIE3 network was located using rice.connectf.org (Brooks et al, 2020), which consisted of 9 RNA-seq/ChIP-seq in planta datasets. This rice validation data confirmed 5,683 predicted edges for the 3 TFs was used to calculate the area under the precision/recall curve (AUPR) using automated functions in ConnecTF (Brooks et al, 2020).…”

Section: Figurementioning

confidence: 99%

“…Network validation (AUPR) and "pruning": Validation data for 3 TFs in the GENIE3 network was located using rice.connectf.org (Brooks et al, 2020), which consisted of 9 RNA-seq/ChIP-seq in planta datasets. This rice validation data confirmed 5,683 predicted edges for the 3 TFs was used to calculate the area under the precision/recall curve (AUPR) using automated functions in ConnecTF (Brooks et al, 2020). This AUPR was then used to select a precision cut-off and "prune" the network for high-confidence edges of the GENIE3 gene regulatory network (GRN), again using automated functions in ConnecTF.…”

Section: Figurementioning

confidence: 99%

“…The 90 TFs for GENIE3 were selected from the two WGCNA modules (grey60 and skyblue) that are highly correlated with NUEg and are also N-and/or-W responsive (Figure 3D and Supplementary Data 4). The total unpruned network of 973,260 edges were uploaded to ConnecTF-Rice (rice.connectf.org) for network pruning and AUPR analysis (Brooks et al, 2020). This analysis is based on the in planta TF-target gene validation data…”

Section: Genie3 Analysis Of Grns and Validation Of Tf! Target Gene Pr...mentioning

confidence: 99%

“…The NUEg phenotype for 19 rice varieties measured under four N-by-W conditions. We used the Potential index (I PO ) (Ndiaye et al, 2019) for OsbZIP23, OsABF1, and OsNAC14 that is housed in the ConnecTF database (Brooks et al, 2020) (Figure 4 and Supplementary Figure 2). Gene Ontology (GO) biological process analysis was conducted using g:Profiler (https://biit.cs.…”

Section: Genie3 Analysis Of Grns and Validation Of Tf! Target Gene Pr...mentioning

confidence: 99%

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

Shanks

Huang²,

Cheng³

et al. 2022

Front. Plant Sci.

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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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Nitrogen sensing and regulatory networks: it's about time and space

Shanks,

Rothkegel,

Brooks

et al. 2024

The Plant Cell

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A plant's response to external and internal nitrogen signals/status relies on sensing and signaling mechanisms that operate across spatial and temporal dimensions. From a comprehensive systems biology perspective, this involves integrating nitrogen responses in different cell types and over long distances to ensure organ coordination in real time and yield practical applications. In this prospective review, we focus on novel aspects of nitrogen (N) sensing/signaling uncovered using temporal and spatial systems biology approaches, largely in the model Arabidopsis. The temporal aspects span: transcriptional responses to N-dose mediated by Michaelis-Menten kinetics, the role of the master NLP7 transcription factor as a nitrate sensor, its nitrate-dependent TF nuclear retention, its “hit-and-run” mode of target gene regulation, and temporal transcriptional cascade identified by “network walking.” Spatial aspects of N-sensing/signaling have been uncovered in cell type-specific studies in roots and in root-to-shoot communication. We explore new approaches using single-cell sequencing data, trajectory inference, and pseudotime analysis as well as machine learning and artificial intelligence approaches. Finally, unveiling the mechanisms underlying the spatial dynamics of nitrogen sensing/signaling networks across species from model to crop could pave the way for translational studies to improve nitrogen-use efficiency in crops. Such outcomes could potentially reduce the detrimental effects of excessive fertilizer usage on groundwater pollution and greenhouse gas emissions.

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ConnecTF: A platform to build gene networks by integrating transcription factor-target gene interactions

Cited by 3 publications

References 58 publications

Gene expression changes occurring at bolting time are associated with leaf senescence in Arabidopsis

Gene expression changes occurring at bolting time are associated with leaf senescence in Arabidopsis

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

Nitrogen sensing and regulatory networks: it's about time and space

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