Mechanistic insights into transcription factor cooperativity and its impact on protein-phenotype interactions

Ibarra, Ignacio L.; Hollmann, Nele Merret; Klaus, Bernd; Augsten, Sandra; Velten, Britta; Hennig, Janosch; Zaugg, Judith B.

doi:10.1038/s41467-019-13888-7

Cited by 75 publications

(63 citation statements)

References 68 publications

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“…The results of recent high-throughput binding assays revealed that transcriptional factor cooperativity is a common cellular phenomenon, 43 which has greatly enhanced our understanding of the interplay between transcription factors. As previously discussed, TEAD4 interacts with TCF4 to form a complex for cobind target genes.…”

Section: Cooperation With Transcription Factorsmentioning

confidence: 99%

<p>Structural and Functional Overview of TEAD4 in Cancer Biology</p>

Chen¹,

Huang²,

Zhu³

et al. 2020

OTT

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TEA domain transcription factor 4 (TEAD4) is an important member of the TEAD family. As a downstream effector of the Hippo pathway, TEAD4 has essential roles in cell proliferation, cell survival, tissue regeneration, and stem cell maintenance. TEAD4 contains a TEA DNA binding domain that binds the promoters of target genes and a Yesassociated protein/transcriptional co-activator with PDZ-binding motif (YAP/TAZ) binding domain that associates with transcriptional cofactors. TEAD4 coordinates with YAP, TAZ, VGLL, and other transcription factors to regulate different cellular processes in cancer via its transcriptional output. Moreover, TEAD4 undergoes post-translational modifications and subcellular translocations, and both processes have been shown to shed new insights on how TEAD transcriptional activity can be modified. In summary, TEAD4 has important roles in cancer, including epithelial-mesenchymal transition (EMT), metastasis, cancer stem cell dynamics, and chemotherapeutic drug resistance, suggesting that TEAD4 may be a promising prognostic biomarker in cancer.

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Section: Cooperation With Transcription Factorsmentioning

confidence: 99%

<p>Structural and Functional Overview of TEAD4 in Cancer Biology</p>

Chen¹,

Huang²,

Zhu³

et al. 2020

OTT

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“…4(c) and 4(f) show that the synthetic circuit is not needed if NK cells are stimulated for less than three hours due to the time delay in transcription and translation. Studies have demonstrated that modulating transcription 80,81 can enhance the rate of protein production. With this knowledge at hand, we alter key parameters that may increase RNA production.…”

Section: Resultsmentioning

confidence: 99%

An optimal control approach for enhancing natural killer cells' secretion of cytolytic molecules

Makaryan

Finley

2020

APL Bioengineering

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Natural killer (NK) cells are immune effector cells that can detect and lyse cancer cells. However, NK cell exhaustion, a phenotype characterized by reduced secretion of cytolytic models upon serial stimulation, limits the NK cell's ability to lyse cells. In this work, we investigated in silico strategies that counteract the NK cell's reduced secretion of cytolytic molecules. To accomplish this goal, we constructed a mathematical model that describes the dynamics of the cytolytic molecules granzyme B (GZMB) and perforin-1 (PRF1) and calibrated the model predictions to published experimental data using a Bayesian parameter estimation approach. We applied an information-theoretic approach to perform a global sensitivity analysis, from which we found that the suppression of phosphatase activity maximizes the secretion of GZMB and PRF1. However, simply reducing the phosphatase activity is shown to deplete the cell's intracellular pools of GZMB and PRF1. Thus, we added a synthetic Notch (synNotch) signaling circuit to our baseline model as a method for controlling the secretion of GZMB and PRF1 by inhibiting phosphatase activity and increasing production of GZMB and PRF1. We found that the optimal synNotch system depends on the frequency of NK cell stimulation. For only a few rounds of stimulation, the model predicts that inhibition of phosphatase activity leads to more secreted GZMB and PRF1; however, for many rounds of stimulation, the model reveals that increasing production of the cytolytic molecules is the optimal strategy. In total, we developed a mathematical framework that provides actionable insight into engineering robust NK cells for clinical applications.

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“…The previous model generates the heterodimeric motif based on separated predictive spacing length and orientation of the monomeric motif pair. Such a synthesis approach has some substantial limitations, since most of TF-TF pairs' cooperatively bound sits involves more than one orientation and various spacing preferences [14,16]. By contrast, DeepMotifSyn generates and scores heterodimeric motif candidates with all potential orientations and spacing preferences (see Figure 1).…”

Section: Discussionmentioning

confidence: 99%

“…ChIP-seq, ChIP-exo, and ChIP-nexus) have been developed to detect transcriptions factor binding sites (TFBSs) in various cell types and tissues, while some TFBSs do not have any strong-match motif [7,8,9,10,11]. Several studies have revealed that cooperative TF pairs can function as heterodimers for binding onto the composite DNA motif to regulate gene expression [12,13,14,15,16,17]. Therefore, characterizing TF interactions and the corresponding heterodimeric DNA motifs are essential for understanding the function of non-coding DNA in gene regulation.…”

Section: Introductionmentioning

confidence: 99%

DeepMotifSyn: a deep learning approach to synthesize heterodimeric DNA motifs

Lin

Huang

Chen

et al. 2021

Preprint

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The cooperativity of transcription factors (TFs) is a widespread phenomenon in the gene regulation system. However, the interaction patterns between TF binding motifs remain elusive. The recent high-throughput assays, CAP-SELEX, have identified over 600 composite DNA sites (i.e.~heterodimeric motifs) bound by cooperative TF pairs. However, there are over 25,000 inferentially effective heterodimeric TFs in human cell. It is not practically feasible to validate all heterodimeric motifs due to cost and labour. Therefore, it is highly demanding to develop a fast and accurate computational tool for heterodimeric motif synthesis. We introduce DeepMotifSyn, a deep-learning-based tool for synthesizing heterodimeric motifs from monomeric motif pairs. Specifically, DeepMotifSyn is composed of heterodimeric motif generator and evaluator. The generator is a U-Net-based neural network that can synthesize heterodimeric motifs from aligned motif pairs. The evaluator is a machine-learning-based model that can score the generated heterodimeric motif candidates based on the motif sequence features. Systematic evaluations on CAP-SELEX data illustrates that DeepMotifSyn significantly outperforms the current state-of-the-art predictors. In addition, DeepMotifSyn can synthesize multiple heterodimeric motifs with different orientation and spacing settings. Such a feature can address the shortcomings of previous models. We believe DeepMotifSyn is a more practical and reliable model than current predictors on heterodimeric motif synthesis.

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Mechanistic insights into transcription factor cooperativity and its impact on protein-phenotype interactions

Cited by 75 publications

References 68 publications

<p>Structural and Functional Overview of TEAD4 in Cancer Biology</p>

<p>Structural and Functional Overview of TEAD4 in Cancer Biology</p>

An optimal control approach for enhancing natural killer cells' secretion of cytolytic molecules

DeepMotifSyn: a deep learning approach to synthesize heterodimeric DNA motifs

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