scholarly journals Permutational analysis of Saccharomyces cerevisiae regulatory elements

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Namrita Dhillon ◽  
Robert Shelansky ◽  
Brent Townshend ◽  
Miten Jain ◽  
Hinrich Boeger ◽  
...  
Keyword(s):  
Gene Expression ◽  
Saccharomyces Cerevisiae ◽  
Principal Component ◽  
Regulatory Elements ◽  
Untranslated Regions ◽  
Core Promoters ◽  
Regulatory Circuits ◽  
Multiple Levels ◽  
Functional Components ◽  
Gene Expression Levels

Abstract Gene expression in Saccharomyces cerevisiae is regulated at multiple levels. Genomic and epigenomic mapping of transcription factors and chromatin factors has led to the delineation of various modular regulatory elements—enhancers (upstream activating sequences), core promoters, 5′ untranslated regions (5′ UTRs) and transcription terminators/3′ untranslated regions (3′ UTRs). However, only a few of these elements have been tested in combinations with other elements and the functional interactions between the different modular regulatory elements remain under explored. We describe a simple and rapid approach to build a combinatorial library of regulatory elements and have used this library to study 26 different enhancers, core promoters, 5′ UTRs and transcription terminators/3′ UTRs to estimate the contribution of individual regulatory parts in gene expression. Our combinatorial analysis shows that while enhancers initiate gene expression, core promoters modulate the levels of enhancer-mediated expression and can positively or negatively affect expression from even the strongest enhancers. Principal component analysis (PCA) indicates that enhancer and promoter function can be explained by a single principal component while UTR function involves multiple functional components. The PCA also highlights outliers and suggest differences in mechanisms of regulation by individual elements. Our data also identify numerous regulatory cassettes composed of different individual regulatory elements that exhibit equivalent gene expression levels. These data thus provide a catalog of elements that could in future be used in the design of synthetic regulatory circuits.

scholarly journals Combinatorial analysis of Saccharomyces cerevisiae regulatory elements

2019 ◽  
Author(s):  
N. Dhillon ◽  
R. Shelansky ◽  
B. Townshend ◽  
M. Jain ◽  
H. Boeger ◽  
...  
Keyword(s):  
Gene Expression ◽  
Saccharomyces Cerevisiae ◽  
Specific Interaction ◽  
Principal Component ◽  
Regulatory Elements ◽  
Untranslated Regions ◽  
Core Promoters ◽  
Regulatory Circuits ◽  
Expression Cluster ◽  
Multiple Levels

AbstractGene expression in Saccharomyces cerevisiae is regulated at multiple levels. Genomic and epigenomic mapping of transcription factors and chromatin components has led to the definition and delineation of various regulatory elements. Enhancers, promoters, 5’ untranslated regions (5’UTR) and transcription terminators/3’ untranslated regions (3’UTR) have all been defined. However, the specific contributions of each of these features as part of a regulatory unit and the functional communications between these regulatory elements remains under explored.We built a combinatorial library of 26 different enhancers, core promoters, 5’UTRs and transcription terminators/3’UTRs. This library was analyzed with respect to gene expression in order to better understand the interactions between different regulatory elements. In the process we developed new methods to estimate the contribution of individual regulatory parts from just a few simple measurements. Our data show that different pairs of regulatory parts follow specific interaction rules affecting overall activity either positively or negatively. We find that while enhancers are the initiators of gene activity, core promoters modulate the levels of enhancer mediated expression. Cluster analysis based on expression show that TATA-box containing core promoters appear to increase enhancer-driven transcription to a greater extent than TATA-less promoters. Principal component analysis highlight outliers and suggest differences in mechanisms of regulation. These results provide a system to characterize regulatory elements and use these elements in the design of synthetic regulatory circuits.

Gene-regulatory elements may change the amount, timing, or location of gene expression, cis-Regulation therapy platforms might become a gene therapy to treat many genetic diseases

2021 ◽  
Author(s):  
Moataz Dowaidar
Keyword(s):  
Gene Expression ◽  
Genetic Diseases ◽  
Regulatory Elements ◽  
Tissue Type ◽  
Gene Promoters ◽  
Expression Levels ◽  
Cis Regulation ◽  
Gene Regulatory Elements ◽  
Gene Regulatory ◽  
Gene Expression Levels

Changes in gene expression levels above or below a particular threshold may have a dramatic impact on phenotypes, leading to a wide spectrum of human illnesses. Gene-regulatory elements, also known as cis-regulatory elements (CREs), may change the amount, timing, or location (cell/tissue type) of gene expression, whereas mutations in a gene's coding sequence may result in lower or higher gene expression levels resulting in protein loss or gain. Loss-of-function mutations in both genes produce recessive human illness, while haploinsufficient mutations in 65 genes are also known to be deleterious due to function gain, according to the ClinVar1 and ClinGen3 databases. CREs are promoters living near to a gene's transcription start site and switching it on at predefined times, places, and levels. Other distal CREs, like enhancers and silencers, are temporal and tissue-specific control promoters. Enhancers activate promoters, commonly referred to as "promoters," whereas silencers turn them off. Insulators also restrict promiscuous interactions between enhancers and gene promoters. Systematic genomic approaches can help understand the cis-regulatory circuitry of gene expression by highly detecting and functionally defining these CREs. This includes the new use of CRISPR–CRISPR-associated protein 9 (CRISPR–Cas9) and other editing approaches to discover CREs. Cis-Regulation therapy (CRT) provides many promises to heal human ailments. CRT may be used to upregulate or downregulate disease-causing genes due to lower or higher levels of expression, and it may also be used to precisely adjust the expression of genes that assist in alleviating disease features. CRT may employ proteins that generate epigenetic modifications like methylation, histone modification, or gene expression regulation looping. Weighing CRT's advantages and downsides against alternative treatment methods is crucial. CRT platforms might become a practical technique to treat many genetic diseases that now lack treatment alternatives if academics, patient communities, clinicians, regulators and industry work together.

scholarly journals Enhancer variants associated with Alzheimer’s disease affect gene expression via chromatin looping

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Masataka Kikuchi ◽  
Norikazu Hara ◽  
Mai Hasegawa ◽  
Akinori Miyashita ◽  
Ryozo Kuwano ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Regulatory Elements ◽  
Genome Wide Association Studies ◽  
Expression Levels ◽  
Chromatin Loops ◽  
Coding Regions ◽  
Affect Gene Expression ◽  
Gene Expression Levels

Abstract Background Genome-wide association studies (GWASs) have identified single-nucleotide polymorphisms (SNPs) that may be genetic factors underlying Alzheimer’s disease (AD). However, how these AD-associated SNPs (AD SNPs) contribute to the pathogenesis of this disease is poorly understood because most of them are located in non-coding regions, such as introns and intergenic regions. Previous studies reported that some disease-associated SNPs affect regulatory elements including enhancers. We hypothesized that non-coding AD SNPs are located in enhancers and affect gene expression levels via chromatin loops. Methods To characterize AD SNPs within non-coding regions, we extracted 406 AD SNPs with GWAS p-values of less than 1.00 × 10− 6 from the GWAS catalog database. Of these, we selected 392 SNPs within non-coding regions. Next, we checked whether those non-coding AD SNPs were located in enhancers that typically regulate gene expression levels using publicly available data for enhancers that were predicted in 127 human tissues or cell types. We sought expression quantitative trait locus (eQTL) genes affected by non-coding AD SNPs within enhancers because enhancers are regulatory elements that influence the gene expression levels. To elucidate how the non-coding AD SNPs within enhancers affect the gene expression levels, we identified chromatin-chromatin interactions by Hi-C experiments. Results We report the following findings: (1) nearly 30% of non-coding AD SNPs are located in enhancers; (2) eQTL genes affected by non-coding AD SNPs within enhancers are associated with amyloid beta clearance, synaptic transmission, and immune responses; (3) 95% of the AD SNPs located in enhancers co-localize with their eQTL genes in topologically associating domains suggesting that regulation may occur through chromatin higher-order structures; (4) rs1476679 spatially contacts the promoters of eQTL genes via CTCF-CTCF interactions; (5) the effect of other AD SNPs such as rs7364180 is likely to be, at least in part, indirect through regulation of transcription factors that in turn regulate AD associated genes. Conclusion Our results suggest that non-coding AD SNPs may affect the function of enhancers thereby influencing the expression levels of surrounding or distant genes via chromatin loops. This result may explain how some non-coding AD SNPs contribute to AD pathogenesis.

scholarly journals ImmuneRegulation: a web-based tool for identifying human immune regulatory elements

2019 ◽  
Vol 47 (W1) ◽  
pp. W142-W150 ◽  
Author(s):  
Selim Kalayci ◽  
Myvizhi Esai Selvan ◽  
Irene Ramos ◽  
Chris Cotsapas ◽  
Eva Harris ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Types ◽  
Regulatory Elements ◽  
Specific Gene ◽  
Web Based ◽  
Transcriptome Regulation ◽  
Rich Data ◽  
User Friendly ◽  
Gene Expression Levels

Abstract Humans vary considerably both in their baseline and activated immune phenotypes. We developed a user-friendly open-access web portal, ImmuneRegulation, that enables users to interactively explore immune regulatory elements that drive cell-type or cohort-specific gene expression levels. ImmuneRegulation currently provides the largest centrally integrated resource on human transcriptome regulation across whole blood and blood cell types, including (i) ∼43,000 genotyped individuals with associated gene expression data from ∼51,000 experiments, yielding genetic variant-gene expression associations on ∼220 million eQTLs; (ii) 14 million transcription factor (TF)-binding region hits extracted from 1945 ChIP-seq studies; and (iii) the latest GWAS catalog with 67,230 published variant-trait associations. Users can interactively explore associations between queried gene(s) and their regulators (cis-eQTLs, trans-eQTLs or TFs) across multiple cohorts and studies. These regulators may explain genotype-dependent gene expression variations and be critical in selecting the ideal cohorts or cell types for follow-up studies or in developing predictive models. Overall, ImmuneRegulation significantly lowers the barriers between complex immune regulation data and researchers who want rapid, intuitive and high-quality access to the effects of regulatory elements on gene expression in multiple studies to empower investigators in translating these rich data into biological insights and clinical applications, and is freely available at https://immuneregulation.mssm.edu.

scholarly journals Role of Non-Coding Regulatory Elements in the Control of GR-Dependent Gene Expression

2021 ◽  
Vol 22 (8) ◽  
pp. 4258
Author(s):  
Malgorzata Borczyk ◽  
Mateusz Zieba ◽  
Michał Korostyński ◽  
Marcin Piechota
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Regulatory Elements ◽  
Transcription Activator ◽  
Long Distance ◽  
Core Promoters ◽  
Transcriptional Changes ◽  
Gene Expression Dynamics ◽  
Upregulated Genes

The glucocorticoid receptor (GR, also known as NR3C1) coordinates molecular responses to stress. It is a potent transcription activator and repressor that influences hundreds of genes. Enhancers are non-coding DNA regions outside of the core promoters that increase transcriptional activity via long-distance interactions. Active GR binds to pre-existing enhancer sites and recruits further factors, including EP300, a known transcriptional coactivator. However, it is not known how the timing of GR-binding-induced enhancer remodeling relates to transcriptional changes. Here we analyze data from the ENCODE project that provides ChIP-Seq and RNA-Seq data at distinct time points after dexamethasone exposure of human A549 epithelial-like cell line. This study aimed to investigate the temporal interplay between GR binding, enhancer remodeling, and gene expression. By investigating a single distal GR-binding site for each differentially upregulated gene, we show that transcriptional changes follow GR binding, and that the largest enhancer remodeling coincides in time with the highest gene expression changes. A detailed analysis of the time course showed that for upregulated genes, enhancer activation persists after gene expression changes settle. Moreover, genes with the largest change in EP300 binding showed the highest expression dynamics before the peak of EP300 recruitment. Overall, our results show that enhancer remodeling may not directly be driving gene expression dynamics but rather be a consequence of expression activation.

scholarly journals Pervasive and Dynamic Transcription Initiation in Saccharomyces cerevisiae

2018 ◽  
Author(s):  
Zhaolian Lu ◽  
Zhenguo Lin
Keyword(s):  
Gene Expression ◽  
Saccharomyces Cerevisiae ◽  
Transcription Initiation ◽  
Core Promoter ◽  
Model Organism ◽  
Start Codon ◽  
Yeast Genome ◽  
Core Promoters ◽  
Nucleotide Resolution ◽  
Yeast Genes

AbstractTranscription initiation is finely regulated to ensure the proper expression and function of these genes. The regulated transcription initiation in response to various environmental cues in the model organism Saccharomyces cerevisiae has not been systematically investigated. In this study, we generated quantitative maps of transcription start site (TSS) at a single-nucleotide resolution for S. cerevisiae grown in nine different conditions using no-amplification non-tagging Cap analysis of gene expression (nAnT-iCAGE) sequencing. Based on 337 million uniquely mapped CAGE tags, we mapped ~1 million well-supported TSSs, suggesting highly pervasive transcription initiation in the compact genome of yeast. The comprehensive TSS maps allowed us to identify core promoters for ~96% verified protein-coding genes and to revise the predicted translation start codon for 183 genes. We found that 56% of yeast genes have at least two core promoters and alternative usage of different core promoters in a gene is widespread in response to changing environments. More importantly, most core promoter shifts are coupled with differential gene expression, indicating that core promoter shift might play an important role in controlling transcriptional activity of yeast genes. Based on their dynamic activities, we divided yeast core promoters as constitutive core promoters (55%) and inducible core promoters (45%). The two classes of core promoters exhibit distinctive patterns in transcriptional abundance, chromatin structure, promoter shape, and sequence context. In summary, the quantitative TSS maps generated by this study improved the annotation of yeast genome, and revealed a highly pervasive and dynamic nature of transcription initiation in yeast.

scholarly journals No Need to Stick Together to Be Connected: Multiple Types of Enhancers’ Networking

Cancers ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 5201
Author(s):  
Emanuele Vitale ◽  
Mila Gugnoni ◽  
Alessia Ciarrocchi
Keyword(s):  
Gene Expression ◽  
Gene Expression Regulation ◽  
Building Blocks ◽  
Regulatory Elements ◽  
Transcriptional Level ◽  
Independent Manner ◽  
Control Of Gene Expression ◽  
Cis Acting ◽  
Regulatory Circuits ◽  
Dna Elements

The control of gene expression at a transcriptional level requires a widespread landscape of regulatory elements. Central to these regulatory circuits are enhancers (ENHs), which are defined as cis-acting DNA elements able to increase the transcription of a target gene in a distance- and orientation-independent manner. ENHs are not independent functional elements but work in a complex and dynamic cooperative network, constituting the building blocks of multimodular domains of gene expression regulation. The information from each of these elements converges on the target promoter, contributing to improving the precision and sharpness of gene modulation. ENHs’ interplay varies in its nature and extent, ranging from an additive to redundant effect depending on contexts. Moving from super-enhancers that drive the high expression levels of identity genes, to shadow-enhancers, whose redundant functions contribute to buffering the variation in gene expression, this review aims to describe the different modalities of ENHs’ interaction and their role in the regulation of complex biological processes like cancer development.

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