scholarly journals Systematic dissection of transcriptional regulatory networks by genome-scale and single-cell CRISPR screens

2021 ◽  
Vol 7 (27) ◽  
pp. eabf5733
Author(s):  
Rui Lopes ◽  
Kathleen Sprouffske ◽  
Caibin Sheng ◽  
Esther C. H. Uijttewaal ◽  
Adriana Emma Wesdorp ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regulatory Networks ◽  
Essential Role ◽  
Regulatory Elements ◽  
Transcriptional Regulatory Networks ◽  
Transcriptional Regulatory Elements ◽  
Transcriptional Regulatory ◽  
Functional Relevance ◽  
Genome Scale ◽  
Transcription Program

Millions of putative transcriptional regulatory elements (TREs) have been cataloged in the human genome, yet their functional relevance in specific pathophysiological settings remains to be determined. This is critical to understand how oncogenic transcription factors (TFs) engage specific TREs to impose transcriptional programs underlying malignant phenotypes. Here, we combine cutting edge CRISPR screens and epigenomic profiling to functionally survey ≈15,000 TREs engaged by estrogen receptor (ER). We show that ER exerts its oncogenic role in breast cancer by engaging TREs enriched in GATA3, TFAP2C, and H3K27Ac signal. These TREs control critical downstream TFs, among which TFAP2C plays an essential role in ER-driven cell proliferation. Together, our work reveals novel insights into a critical oncogenic transcription program and provides a framework to map regulatory networks, enabling to dissect the function of the noncoding genome of cancer cells.

scholarly journals ATRT-07. DEFINING LOST AND GAINED TRANSCRIPTIONAL REGULATORY NETWORKS IN ATYPICAL TERATOID RHABDOID TUMOR

2021 ◽  
Vol 23 (Supplement_1) ◽  
pp. i2-i2
Author(s):  
Cody Nesvick ◽  
Liang Zhang ◽  
Alex Wixom ◽  
Feda Hamdan ◽  
Steven Johnsen ◽  
...  
Keyword(s):  
Cell Lines ◽  
Regulatory Networks ◽  
Regulatory Elements ◽  
Rhabdoid Tumor ◽  
Allelic Loss ◽  
Global Changes ◽  
Atypical Teratoid Rhabdoid Tumor ◽  
Transcriptional Regulatory Networks ◽  
Transcriptional Regulatory ◽  
Atypical Teratoid

Abstract Atypical teratoid rhabdoid tumor (ATRT) is a central nervous system cancer of infancy and early childhood that may occur anywhere along the neuraxis and is associated with a high rate of mortality. While contemporary multimodal therapeutic approaches have significantly improved overall survival, targeted therapy remains elusive, and treatment is often associated with significant morbidity. ATRT is unique in its genomic stability, with the only recurrent genetic abnormality being bi-allelic loss of the SMARCB1 gene, which encodes a core subunit of the BAF chromatin remodeling complex. The epigenetic mechanisms by which SMARCB1 loss leads to tumorigenesis are not yet well-defined and addressing this gap in understanding is necessary for creating efficacious, targeted therapeutics. To better understand the epigenetic features gained and lost in ATRT, we re-expressed SMARCB1 in a library of patient-derived and established ATRT cell lines of multiple molecular subtypes. SMARCB1 restoration significantly reduced or eliminated the proliferative and clonogenic capacity of each cell line. We performed assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-Seq) and RNA sequencing (RNA-Seq) to define putative transcriptional regulatory networks that are gained and lost in ATRT. SMARCB1 restoration was associated with global changes in chromatin openness consistent with the creation of new regulatory elements throughout the genome, and these were associated with induction of a diverse developmental transcriptional signature. Motif enrichment analysis of regions with increased accessibility defined a small but consistent number of centrally enriched transcription factor motifs across cell lines indicative of putative pioneer factors whose functions may be lost in ATRT. Pertinent chromatin immunoprecipitation with sequencing (ChIP-Seq) data will be discussed in the context of lost and gained transcriptional regulatory networks in ATRT and normal cellular development.

Breast Cancer Transcriptional Regulatory Network Reprogramming by using the CRISPR/Cas9 system: An Oncogenetics Perspective

2021 ◽  
Vol 21 ◽  
Author(s):  
Desh Deepak Singh ◽  
Ravi Verma ◽  
Subhash K. Tripathi ◽  
Rajnish Sahu ◽  
Poonam Trivedi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Network ◽  
Treatment Options ◽  
Diagnostic Tools ◽  
Transcriptional Regulatory Networks ◽  
Development Of Resistance ◽  
Transcriptional Regulatory ◽  
Network Reprogramming

: Breast cancer (BC) is the second most commonly diagnosed cancer in the world. BC develops due to dysregulation of transcriptional profiles, substantial interpatient variations, genetic mutations, and dysregulation of signaling pathways in breast cells. These events are regulated by many genes such as BRCA1/2, PTEN, TP53, mTOR, TERT, AKT, PI3K and others genes. Treatment options for BC remain a hurdle, which warrants a comprehensive understanding that establishes an interlinking connection between these genes in BC tumorigenesis. Consequently, there is an increasing demand for alternative treatment approaches and the design of more effective treatments. In this regard, it is crucial to build the corresponding transcriptional regulatory networks governing BC by using advanced genetic tools and techniques. In the past, several molecular editing technologies have been used to edit genes with several limitations. Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR Associated Protein 9 (CRISPR/Cas9) recently received a profound attention due to its potential in biomedical and therapeutic applications. Here, we review the role of various molecular signalling pathways dysregulated in BC development such as PTEN/PI3K/AKT/mTOR as well as BRCA1/BRCA2/TP53/TERT and their interplay between the related gene networks in BC initiation, progression and development of resistance against available targeted therapeutic agents. Use of CRISPR/Cas9 gene-editing technology to generate BC gene-specific transgenic cell lines and animal models to decipher their role and interactions with other gene products has been employed successfully. Moreover, the significance of using CRISPR/Cas9 technology to develop early BC diagnostic tools and treatments is discussed here.

scholarly journals Analysis of next- and third-generation RNA-Seq data reveals the structures of alternative transcription units in bacterial genomes

2021 ◽  
Author(s):  
Qi Wang ◽  
Zhaoqian Liu ◽  
Bo Yan ◽  
Wen-Chi Chou ◽  
Laurence Ettwiller ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Programming Model ◽  
Third Generation ◽  
Rna Seq ◽  
Bacterial Genomes ◽  
Transcriptional Regulatory Networks ◽  
Computational Framework ◽  
Transcriptional Regulatory ◽  
Alternative Transcription ◽  
Genome Scale

ABSTRACTAlternative transcription units (ATUs) are dynamically encoded under different conditions or environmental stimuli in bacterial genomes, and genome-scale identification of ATUs is essential for studying the emergence of human diseases caused by bacterial organisms. However, it is unrealistic to identify all ATUs using experimental techniques, due to the complexity and dynamic nature of ATUs. Here we present the first-of-its-kind computational framework, named SeqATU, for genome-scale ATU prediction based on next-generation RNA-Seq data. The framework utilizes a convex quadratic programming model to seek an optimum expression combination of all of the to-be-identified ATUs. The predicted ATUs in E. coli reached a precision of 0.77/0.74 and a recall of 0.75/0.76 in the two RNA-Sequencing datasets compared with the benchmarked ATUs from third-generation RNA-Seq data. We believe that the ATUs identified by SeqATU can provide fundamental knowledge to guide the reconstruction of transcriptional regulatory networks in bacterial genomes.

scholarly journals A review of methods for the reconstruction and analysis of integrated genome-scale models of metabolism and regulation

2020 ◽  
Vol 48 (5) ◽  
pp. 1889-1903
Author(s):  
Fernando Cruz ◽  
José P. Faria ◽  
Miguel Rocha ◽  
Isabel Rocha ◽  
Oscar Dias
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Networks ◽  
Flux Balance ◽  
Transcriptional Regulatory ◽  
Scale Models ◽  
Current Survey ◽  
Balance Analysis ◽  
Or Gene ◽  
Genome Scale

The current survey aims to describe the main methodologies for extending the reconstruction and analysis of genome-scale metabolic models and phenotype simulation with Flux Balance Analysis mathematical frameworks, via the integration of Transcriptional Regulatory Networks and/or gene expression data. Although the surveyed methods are aimed at improving phenotype simulations obtained from these models, the perspective of reconstructing integrated genome-scale models of metabolism and gene expression for diverse prokaryotes is still an open challenge.

Computational approaches to study transcriptional regulation

2008 ◽  
Vol 36 (4) ◽  
pp. 758-765 ◽  
Author(s):  
M. Madan Babu
Keyword(s):  
Transcriptional Regulation ◽  
Regulatory Networks ◽  
Regulatory Elements ◽  
Model Organisms ◽  
Emerging Pathogens ◽  
Transcriptional Regulatory Networks ◽  
Regulatory Interactions ◽  
Regulatory Circuits ◽  
Transcriptional Regulatory ◽  
The Individual

In recent years, a number of technical and experimental advances have allowed us to obtain an unprecedented amount of information about living systems on a genomic scale. Although the complete genomes of many organisms are available due to the progress made in sequencing technology, the challenge to understand how the individual genes are regulated within the cell remains. Here, I provide an overview of current computational methods to investigate transcriptional regulation. I will first discuss how representing protein–DNA interactions as a network provides us with a conceptual framework to understand the organization of regulatory interactions in an organism. I will then describe methods to predict transcription factors and cis-regulatory elements using information such as sequence, structure and evolutionary conservation. Finally, I will discuss approaches to infer genome-scale transcriptional regulatory networks using experimentally characterized interactions from model organisms and by reverse-engineering regulatory interactions that makes use of gene expression data and genomewide location data. The methods summarized here can be exploited to discover previously uncharacterized transcriptional pathways in organisms whose genome sequence is known. In addition, such a framework and approach can be invaluable to investigate transcriptional regulation in complex microbial communities such as the human gut flora or populations of emerging pathogens. Apart from these medical applications, the concepts and methods discussed can be used to understand the combinatorial logic of transcriptional regulation and can be exploited in biotechnological applications, such as in synthetic biology experiments aimed at engineering regulatory circuits for various purposes.

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