scholarly journals Separate Polycomb Response Elements control chromatin state and activation of the vestigial gene

2018 ◽  
Author(s):  
Kami Ahmad ◽  
Amy E Spens
Keyword(s):  
Dna Sequences ◽  
Target Genes ◽  
Chromatin Modification ◽  
Chromatin Domains ◽  
Response Elements ◽  
Regulatory Dna Sequences ◽  
Polycomb Response Elements ◽  
Regulatory Dna ◽  
Inactive Genes ◽  
Endogenous Locus

Patterned expression of many developmental genes is specified by transcription factor gene expression, but is thought to be refined by chromatin-mediated repression. Regulatory DNA sequences called Polycomb Response Elements (PREs) are required to repress some developmental target genes, and are widespread in genomes, suggesting that they broadly affect developmental programs. While PREs in transgenes can nucleate trimethylation on lysine 27 of the histone H3 tail (H3K27me3), none have been demonstrated to be necessary at endogenous chromatin domains. This failure is thought to be due to the fact that most endogenous H3K27me3 domains contain many PREs, and individual PREs may be redundant. In contrast to these ideas, we show here that PREs near the wing selector gene vestigial have distinctive roles at their endogenous locus, even though both PREs are repressors in transgenes. First, a PRE near the promoter is required for vestigial activation and not for repression. Second, only the distal PRE contributes to H3K27me3, but even removal of both PREs does not eliminate H3K27me3 across the vestigial domain. Thus, endogenous chromatin domains appear to be intrinsically marked by H3K27me3, and PREs appear required to enhance this chromatin modification to high levels at inactive genes.

Variation in structure and expression of ribosomal DNA loci in wheat

Genome ◽  
1989 ◽  
Vol 31 (2) ◽  
pp. 963-968 ◽  
Author(s):  
R. B. Flavell
Keyword(s):  
Ribosomal Rna ◽  
Dna Sequences ◽  
Rrna Genes ◽  
Rrna Gene ◽  
Gene Methylation ◽  
Regulatory Dna Sequences ◽  
Regulatory Dna ◽  
Inactive Genes ◽  
Rna Genes ◽  
Active Genes

Ribosomal RNA genes are organised in tandem arrays at complex loci called nucleolus organisers. The structure of a locus and of a wheat rRNA gene is described in detail. Active or potentially active genes are in the nucleolus during interphase, while inactive genes are excluded from the nucleolus. Genetic variation exists within a species for the number of the rRNA genes at a locus and also for the structure of the intergenic, regulatory DNA. This variation can affect the activity of a locus, relative to that of another in the same cell and the proportion of the rRNA genes at a locus included in the nucleolus. The active loci are enriched with genes that are not methylated at specific CpG residues in the intergenic regulatory DNA and that are in a chromatin conformation accessible to DNase I. A model is presented that attempts to relate the structural variation between genes to the differential expression and nucleolar organisation of the rRNA genes at different loci. The model is based on the affinity of proteins, in limiting concentration, to specific regulatory DNA sequences. These sequences are subject to change as a result of mechanisms that can spread mutations through a locus. The resulting variation, which may not be eliminated by selection unless it is very deleterious and accounts for a large part of the total rDNA, may be one reason why plants maintain a large excess of ribosomal RNA genes.Key words: nucleolus, ribosomal RNA gene, methylation.

scholarly journals The HMGB Protein KlIxr1, a DNA Binding Regulator of Kluyveromyces lactis Gene Expression Involved in Oxidative Metabolism, Growth, and dNTP Synthesis

Biomolecules ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1392
Author(s):  
Agustín Rico-Díaz ◽  
Aída Barreiro-Alonso ◽  
Cora Rey-Souto ◽  
Manuel Becerra ◽  
Mónica Lamas-Maceiras ◽  
...  
Keyword(s):  
Dna Sequences ◽  
Target Genes ◽  
Sequence Similarity ◽  
Kluyveromyces Lactis ◽  
High Mobility ◽  
Amino Acid Sequence Similarity ◽  
Yeast Saccharomyces Cerevisiae ◽  
Regulatory Dna Sequences ◽  
Important Regulator ◽  
Regulatory Dna

In the traditional fermentative model yeast Saccharomyces cerevisiae, ScIxr1 is an HMGB (High Mobility Group box B) protein that has been considered as an important regulator of gene transcription in response to external changes like oxygen, carbon source, or nutrient availability. Kluyveromyces lactis is also a useful eukaryotic model, more similar to many human cells due to its respiratory metabolism. We cloned and functionally characterized by different methodologies KlIXR1, which encodes a protein with only 34.4% amino acid sequence similarity to ScIxr1. Our data indicate that both proteins share common functions, including their involvement in the response to hypoxia or oxidative stress induced by hydrogen peroxide or metal treatments, as well as in the control of key regulators for maintenance of the dNTP (deoxyribonucleotide triphosphate) pool and ribosome synthesis. KlIxr1 is able to bind specific regulatory DNA sequences in the promoter of its target genes, which are well conserved between S. cerevisiae and K. lactis. Oppositely, we found important differences between ScIrx1 and KlIxr1 affecting cellular responses to cisplatin or cycloheximide in these yeasts, which could be dependent on specific and non-conserved domains present in these two proteins.

scholarly journals Quantifying the regulatory role of individual transcription factors in Escherichia coli

2021 ◽  
Author(s):  
Sunil Guharajan ◽  
Shivani Chhabra ◽  
Vinuselvi Parisutham ◽  
Robert C Brewster
Keyword(s):  
Transcription Factors ◽  
Dna Sequences ◽  
Transcription Initiation ◽  
Target Genes ◽  
Regulatory Role ◽  
E Coli ◽  
Regulatory Dna Sequences ◽  
The Individual ◽  
Regulatory Dna

Transcription factors (TFs) modulate gene expression by binding to regulatory DNA sequences surrounding target genes. To isolate the fundamental regulatory interactions of E. coli TFs, we measure regulation of TFs acting on synthetic target genes that are designed to isolate the individual TF regulatory effect. This data is interpreted through a thermodynamic model that decouples the role of TF copy number and TF binding affinity from the interactions of the TF on RNA polymerase through two distinct mechanisms: (de)stabilization of the polymerase and (de)acceleration of transcription initiation. We find the contribution of each mechanism towards the observed regulation depends on TF identity and binding location; for the set of TFs studied here, regulation immediately downstream of the promoter is not sensitive to TF identity, however these same TFs regulate through distinct mechanisms at an upstream binding site. Furthermore, depending on binding location, these two mechanisms of regulation can act coherently, to reinforce the observed regulatory role (activation or repression), or incoherently, where the TF regulates two distinct steps with opposing effect.

Role of Transcriptional Read-Through in PRE Activity in Drosophila melanogaster

Acta Naturae ◽  
2016 ◽  
Vol 8 (2) ◽  
pp. 79-86 ◽  
Author(s):  
P. V. Elizar’ev ◽  
D. V. Lomaev ◽  
D. A. Chetverina ◽  
P. G. Georgiev ◽  
M. M. Erokhin
Keyword(s):  
Dna Sequences ◽  
Cell Types ◽  
Transcription Terminator ◽  
Response Elements ◽  
Polycomb Response Elements ◽  
The Individual ◽  
Multicellular Organisms ◽  
Different Cell Types ◽  
Main Factor

Maintenance of the individual patterns of gene expression in different cell types is required for the differentiation and development of multicellular organisms. Expression of many genes is controlled by Polycomb (PcG) and Trithorax (TrxG) group proteins that act through association with chromatin. PcG/TrxG are assembled on the DNA sequences termed PREs (Polycomb Response Elements), the activity of which can be modulated and switched from repression to activation. In this study, we analyzed the influence of transcriptional read-through on PRE activity switch mediated by the yeast activator GAL4. We show that a transcription terminator inserted between the promoter and PRE doesnt prevent switching of PRE activity from repression to activation. We demonstrate that, independently of PRE orientation, high levels of transcription fail to dislodge PcG/TrxG proteins from PRE in the absence of a terminator. Thus, transcription is not the main factor required for PRE activity switch.

scholarly journals Deciphering regulatory DNA sequences and noncoding genetic variants using neural network models of massively parallel reporter assays

PLoS ONE ◽  
2019 ◽  
Vol 14 (6) ◽  
pp. e0218073 ◽  
Author(s):  
Rajiv Movva ◽  
Peyton Greenside ◽  
Georgi K. Marinov ◽  
Surag Nair ◽  
Avanti Shrikumar ◽  
...  
Keyword(s):  
Neural Network ◽  
Dna Sequences ◽  
Genetic Variants ◽  
Network Models ◽  
Massively Parallel ◽  
Neural Network Models ◽  
Regulatory Dna Sequences ◽  
Reporter Assays ◽  
Regulatory Dna

Functional Comparison of the Upstream Regulatory DNA Sequences of Four Human Epidermal Keratin Genes

1991 ◽  
Vol 96 (2) ◽  
pp. 162-167 ◽  
Author(s):  
Chuan-Kui Jiang ◽  
Howard S Epstein ◽  
Marjana Tomic ◽  
Irwin M Freedberg ◽  
Miroslav Blumenberg
Keyword(s):  
Dna Sequences ◽  
Regulatory Dna Sequences ◽  
Regulatory Dna

scholarly journals Deciphering regulatory DNA sequences and noncoding genetic variants using neural network models of massively parallel reporter assays

2018 ◽  
Author(s):  
Rajiv Movva ◽  
Peyton Greenside ◽  
Georgi K. Marinov ◽  
Surag Nair ◽  
Avanti Shrikumar ◽  
...  
Keyword(s):  
Neural Network ◽  
Dna Sequences ◽  
Genetic Variants ◽  
Massively Parallel ◽  
Regulatory Sequence ◽  
Regulatory Activity ◽  
Neural Network Models ◽  
Regulatory Dna Sequences ◽  
Reporter Assays ◽  
Regulatory Dna

AbstractThe relationship between noncoding DNA sequence and gene expression is not well-understood. Massively parallel reporter assays (MPRAs), which quantify the regulatory activity of large libraries of DNA sequences in parallel, are a powerful approach to characterize this relationship. We present MPRA-DragoNN, a convolutional neural network (CNN)-based framework to predict and interpret the regulatory activity of DNA sequences as measured by MPRAs. While our method is generally applicable to a variety of MPRA designs, here we trained our model on the Sharpr-MPRA dataset that measures the activity of ~500,000 constructs tiling 15,720 regulatory regions in human K562 and HepG2 cell lines. MPRA-DragoNN predictions were moderately correlated (Spearman ρ = 0.28) with measured activity and were within range of replicate concordance of the assay. State-of-the-art model interpretation methods revealed high-resolution predictive regulatory sequence features that overlapped transcription factor (TF) binding motifs. We used the model to investigate the cell type and chromatin state preferences of predictive TF motifs. We explored the ability of our model to predict the allelic effects of regulatory variants in an independent MPRA experiment and fine map putative functional SNPs in loci associated with lipid traits. Our results suggest that interpretable deep learning models trained on MPRA data have the potential to reveal meaningful patterns in regulatory DNA sequences and prioritize regulatory genetic variants, especially as larger, higher-quality datasets are produced.

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