ConnecTF: A platform to integrate transcription factor–gene interactions and validate regulatory networks

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:10.1093/plphys/kiaa012

Cited by 37 publications

(33 citation statements)

References 66 publications

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“…Recently, a few studies have refined the prediction of TF-TFBS and TF-TG interactions in plants by integrating many types of omics data to gain high confidence in these interactions. However, this approach is constrained by the availability of omics data mostly only in well-studied plants ( Brooks et al, 2021 ; Puig et al, 2021 ). Once data is available, intensive validation with experiments in plants would leap forward the model capability.…”

Section: Discussionmentioning

confidence: 99%

Plant-DTI: Extending the landscape of TF protein and DNA interaction in plants by a machine learning-based approach

et al. 2022

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As a sessile organism, plants hold elaborate transcriptional regulatory systems that allow them to adapt to variable surrounding environments. Current understanding of plant regulatory mechanisms is greatly constrained by limited knowledge of transcription factor (TF)–DNA interactions. To mitigate this problem, a Plant-DTI predictor (Plant DBD-TFBS Interaction) was developed here as the first machine-learning model that covered the largest experimental datasets of 30 plant TF families, including 7 plant-specific DNA binding domain (DBD) types, and their transcription factor binding sites (TFBSs). Plant-DTI introduced a novel TFBS feature construction, called TFBS base-preference, which enhanced the specificity of TFBS to DBD types. The proposed model showed better predictive performance with the TFBS base-preference than the simple binary representation. Plant-DTI was validated with 22 independent ChIP-seq datasets. It accurately predicted the measured DBD-TFBS pairs along with their TFBS motifs, and effectively predicted interactions of other TFs containing similar DBD types. Comparing to the existing state-of-art methods, Plant-DTI prediction showed a figure of merit in sensitivity and specificity with respect to the position weight matrix (PWM) and TSPTFBS methods. Finally, the proposed Plant-DTI model helped to fill the knowledge gap in the regulatory mechanisms of the cassava sucrose synthase 1 gene (MeSUS1). Plant-DTI predicted MeERF72 as a regulator of MeSUS1 in consistence with the yeast one-hybrid (Y1H) experiment. Taken together, Plant-DTI would help facilitate the prediction of TF-TFBS and TF-target gene (TG) interactions, thereby accelerating the study of transcriptional regulatory systems in plant species.

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Section: Discussionmentioning

confidence: 99%

Plant-DTI: Extending the landscape of TF protein and DNA interaction in plants by a machine learning-based approach

et al. 2022

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“…The DNA sequence preference of TFs is largely conserved across phylogenetically-related species, leading to the advent of deep learning-based approaches for cross-species TFBS prediction [47,64,91]. However, recent attempts at mouse-to-human/human-to-mouse and maize- to-soybean cross-species predictions suffered from high false positive rates [47, 49].…”

Section: Discussionmentioning

confidence: 99%

“…Here, our CRNN models were capable of predicting TFBS for the selected TF families in soybean and Arabidopsis with ~90% accuracy. The exclusive use of DEG promoters for binding site prediction increased the likelihood that targets were biologically valid, as the correlation between stable TF binding and TF regulation is vastly inconsistent and oftentimes poor [72,90,91]. Moreover, CRNNs trained for one TF could find TFBS for other members of the same family.…”

Section: Discussionmentioning

confidence: 99%

Gene regulatory network inference in soybean upon infection byPhytophthora sojae

Hale

Ratnayake

Flory

et al. 2022

Preprint

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Phytophthora sojaeis a soil-borne oomycete and the causal agent of Phytophthora root and stem rot (PRR) in soybean (Glycine max[L.] Merrill). Yield losses attributed toP. sojaeare devastating in disease-conducive environments, with global estimates surpassing 1.1 million tonnes annually. Historically, management of PRR has entailed host genetic resistance (both vertical and horizontal) complemented by disease-suppressive cultural practices (e.g., oomicide application). However, the vast expansion of complex and/or diverseP. sojaepathotypes necessitates developing novel technologies to attenuate PRR in field environments. Therefore, the objective of the present study was to couple high-throughput sequencing data and deep learning to elucidate molecular features in soybean following infection byP. sojae. In doing so, we generated transcriptomes to identify differentially expressed genes (DEGs) during compatible and incompatible interactions withP. sojaeand a mock inoculation. The expression data were then used to select two defense-related transcription factors (TFs) belonging to WRKY and RAV families. DNA Affinity Purification and sequencing (DAP-seq) data were obtained for each TF, providing putative DNA binding sites in the soybean genome. These bound sites were used to train Deep Neural Networks with convolutional and recurrent layers to predict new target sites of WRKY and RAV family members in the DEG set. Moreover, we leveraged publicly available Arabidopsis (Arabidopsis thaliana) DAP-seq data for five TF families enriched in our transcriptome analysis to train similar models. These Arabidopsis data-based models were used for cross-species TF binding site prediction on soybean. Finally, we created a gene regulatory network depicting TF-target gene interactions that orchestrate an immune response againstP. sojae. Information herein provides novel insight into molecular plant-pathogen interaction and may prove useful in developing soybean cultivars with more durable resistance toP. sojae.Author SummaryGlobal food security is threatened continually by plant pathogens. One approach to circumvent these disease-causing agents entails understanding how hosts balance primary growth and defense upon pathogen perception. Molecular signatures of perception-rendered defense may be leveraged subsequently to develop resistant/tolerant crop plants. Additionally, evidence suggests that the plant immune system is characterized by tuning primary and secondary metabolic activity via transcription factor-mediated transcriptional reprogramming. Therefore, we investigated transcription factor-target gene interactions in soybean upon infection by compatible and incompatible races ofPhytophthora sojae. Through transcriptome analysis, we found that the interactions elicited vast, overlapping transcriptional responses and identified overrepresented, defense-related transcription factor families. We then generated/acquired DNA-protein interactome data for the most represented transcription factor families in the transcriptome analysis and trained deep learning-based models to predict novel transcription factor targets. Transcription factor/target gene metrics were used to construct a gene regulatory network with prioritized components. We identified hub transcription factors belonging to WRKY and ERF families, the majority of which function in response to various biotic and abiotic stressors. These findings propose novel regulators in the soybean defense response toPhytophthora sojaeand provide an avenue for the investigation of transcription factor-target gene interactions in plants.

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“…A network analysis was conducted using the ConnecTF platform [64], available at https://connectf.org (accessed on 23 November 2022). The list of differentially expressed gene candidates for harvest date was used as "Target Gene List" and "Filter TFs" to identify transcription factor candidates in the ConnecTF database for network construction.…”

Section: Sequencing Data Analysis and Network Constructionmentioning

confidence: 99%

Transcriptome and Gene Regulatory Network Analyses Reveal New Transcription Factors in Mature Fruit Associated with Harvest Date in Prunus persica

Núñez-Lillo

Pérez-Reyes

Riveros

et al. 2022

Plants

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Harvest date is a critical parameter for producers and consumers regarding agro-industrial performance. It involves a pleiotropic effect controlling the development of other fruit quality traits through finely controlling regulatory mechanisms. Fruit ripening is a process in which various signals and biological events co-occur and are regulated by hormone signaling that produces the accumulation/degradation of multiple compounds. However, the regulatory mechanisms that control the hormone signaling involved in fruit development and ripening are still unclear. To investigate the issue, we used individuals with early, middle and late harvest dates from a peach segregating population to identify regulatory candidate genes controlling fruit quality traits at the harvest stage and validate them in contrasting peach varieties for this trait. We identified 467 and 654 differentially expressed genes for early and late harvest through a transcriptomic approach. In addition, using the Arabidopsis DAP-seq database and network analysis, six transcription factors were selected. Our results suggest significant hormonal balance and cell wall composition/structure differences between early and late harvest samples. Thus, we propose that higher expression levels of the transcription factors HB7, ERF017 and WRKY70 in early harvest individuals would induce the expression of genes associated with the jasmonic acid pathway, photosynthesis and gibberellins inhibition. While on the other hand, the high expression levels of LHY, CDF3 and NAC083 in late harvest individuals would promote the induction of genes associated with abscisic acid biosynthesis, auxins and cell wall remodeling.

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ConnecTF: A platform to integrate transcription factor–gene interactions and validate regulatory networks

Cited by 37 publications

References 66 publications

Plant-DTI: Extending the landscape of TF protein and DNA interaction in plants by a machine learning-based approach

Plant-DTI: Extending the landscape of TF protein and DNA interaction in plants by a machine learning-based approach

Gene regulatory network inference in soybean upon infection byPhytophthora sojae

Transcriptome and Gene Regulatory Network Analyses Reveal New Transcription Factors in Mature Fruit Associated with Harvest Date in Prunus persica

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