Determinants of transcription factor regulatory range

Chen, Chen-Hao; Zheng, Rongbin; Fan, Jingyu; Brown, Myles; Liu, Jun S.; Meyer, Clifford A.; Liu, X. Shirley

doi:10.1101/582270

Cited by 13 publications

(13 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Precisely how heterochromatin and other repressive environments inhibit transcription is unclear; they might physically exclude TFs and transcription machinery, perhaps through the higher density of chromatin (Ou et al, 2017), but TFs that do enter the heterochromatic regions may be transiently trapped (Bancaud et al, 2009). These spatial components of TF activity expand the potential range of mechanisms by which chromatin state might regulate TF activity (Zheng et al, 2019).…”

Section: Biophysics Of Tf Activitymentioning

confidence: 99%

Mechanisms of Interplay between Transcription Factors and the 3D Genome

2019

View full text Add to dashboard Cite

Transcription factors (TFs) bind DNA in a sequence-specific manner and thereby serve as the protein anchors and determinants of 3D genome organization. Conversely, chromatin conformation shapes TF activity, for example by looping TF-bound enhancers to distally located target genes. Despite considerable effort, our understanding of the mechanistic relationship between TFs and 3D genome organization remains limited, in large part due to this interdependency. In this review, we summarize the evidence for the diverse mechanisms by which TFs and their activity shape the 3D genome, and vice versa. We further highlight outstanding questions and potential approaches for untangling the complex relationship between TF activity and the 3D genome.

show abstract

Section: Biophysics Of Tf Activitymentioning

confidence: 99%

Mechanisms of Interplay between Transcription Factors and the 3D Genome

2019

View full text Add to dashboard Cite

show abstract

“…Recently, another model proposed in ArchR (Granja et al) also uses the regulatory potential model [ 41 , 61 ] in combination with a gene body component to model the gene activity from scATAC-seq [ 62 ]. ArchR uses a gene boundary model to exclude interference from other genes, although in cases such as near-neighbor divergently transcribed gene pairs, this is likely to eliminate many real long-range cis -regulatory effects [ 63 , 64 ]. In terms of gene body accessibility, ArchR considers the whole gene body while MAESTRO uses only the exon regions.…”

Section: Discussionmentioning

confidence: 99%

Integrative analyses of single-cell transcriptome and regulome using MAESTRO

et al. 2020

Self Cite

View full text Add to dashboard Cite

We present Model-based AnalysEs of Transcriptome and RegulOme (MAESTRO), a comprehensive open-source computational workflow ( http://github.com/liulab-dfci/MAESTRO ) for the integrative analyses of single-cell RNA-seq (scRNA-seq) and ATAC-seq (scATAC-seq) data from multiple platforms. MAESTRO provides functions for pre-processing, alignment, quality control, expression and chromatin accessibility quantification, clustering, differential analysis, and annotation. By modeling gene regulatory potential from chromatin accessibilities at the single-cell level, MAESTRO outperforms the existing methods for integrating the cell clusters between scRNA-seq and scATAC-seq. Furthermore, MAESTRO supports automatic cell-type annotation using predefined cell type marker genes and identifies driver regulators from differential scRNA-seq genes and scATAC-seq peaks.

show abstract

“…A recent bioinformatic analysis suggests that there are two classes of TFs. The TFs with short-and long-range regulatory influence differ in their chromatin-binding preferences and auto-regulatory properties [106]. The regulatory range is further affected by the 3D structure of the chromatin.…”

Section: Conclusion-optimization Of Transcriptional Activation and Rmentioning

confidence: 99%

Tuning up Transcription Factors for Therapy

Becskei

2020

Molecules

View full text Add to dashboard Cite

The recent developments in the delivery and design of transcription factors put their therapeutic applications within reach, exemplified by cell replacement, cancer differentiation and T-cell based cancer therapies. The success of such applications depends on the efficacy and precision in the action of transcription factors. The biophysical and genetic characterization of the paradigmatic prokaryotic repressors, LacI and TetR and the designer transcription factors, transcription activator-like effector (TALE) and CRISPR-dCas9 revealed common principles behind their efficacy, which can aid the optimization of transcriptional activators and repressors. Further studies will be required to analyze the linkage between dissociation constants and enzymatic activity, the role of phase separation and squelching in activation and repression and the long-range interaction of transcription factors with epigenetic regulators in the context of the chromosomes. Understanding these mechanisms will help to tailor natural and synthetic transcription factors to the needs of specific applications.

show abstract

Determinants of transcription factor regulatory range

Cited by 13 publications

References 66 publications

Mechanisms of Interplay between Transcription Factors and the 3D Genome

Mechanisms of Interplay between Transcription Factors and the 3D Genome

Integrative analyses of single-cell transcriptome and regulome using MAESTRO

Tuning up Transcription Factors for Therapy

Contact Info

Product

Resources

About