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Related Concept Videos

Histone Modification02:32

Histone Modification

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The histone proteins have a flexible N-terminal tail extending out from the nucleosome. These histone tails are often subjected to post-translational modifications such as acetylation, methylation, phosphorylation, and ubiquitination. Particular combinations of these modifications form “histone codes” that influence the chromatin folding and tissue-specific gene expression.
Acetylation
The enzyme histone acetyltransferase adds acetyl group to the histones. Another enzyme, histone...
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Histone Modification02:32

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Spreading of Chromatin Modifications02:25

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The histone proteins in the nucleosomes are post-translationally modified (PTM) to increase or decrease access to DNA. The commonly observed PTMs are methylation, acetylation, phosphorylation, and ubiquitination of lysine amino acids in the histone H3 tail region. These histone modifications have specific meaning for the cell. Hence, they are called "histone code". The protein complex involved in histone modification is termed as "reader-writer" complex.
Writers
The writer...
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Co-activators and Co-repressors02:04

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Gene transcription is regulated by the synergistic action of several proteins that form a complex at a gene regulatory site. This is observed in eukaryotes, where the regulation of gene expression is a complex process. Regulatory proteins in eukaryotes can broadly be classified into two types – regulators that bind directly to specific DNA sequences and co-regulators that associate with regulatory proteins but cannot directly bind to the DNA. These co-regulators are further divided into...
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The Nucleosome Core Particle01:12

The Nucleosome Core Particle

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Nucleosomes are the DNA-histone complex, where the DNA strand is wound around the histone core. The histone core is an octamer containing two copies of H2A, H2B, H3, and H4 histone proteins.
Nucleosomes, paradoxically, perform two opposite functions simultaneously. On the one hand, their primary aim is to protect the delicate DNA strands from physical damage and help achieve a higher compaction ratio. On the other hand, they must allow polymerase enzymes to access histone-bound DNA during...
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Related Experiment Video

Updated: Apr 19, 2026

Complete Workflow for Analysis of Histone Post-translational Modifications Using Bottom-up Mass Spectrometry: From Histone Extraction to Data Analysis
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Predicting expression: the complementary power of histone modification and transcription factor binding data.

David M Budden1, Daniel G Hurley2, Joseph Cursons2

  • 1Systems Biology Laboratory, Melbourne School of Engineering, The University of Melbourne, 3010 Parkville, Australia ; NICTA Victoria Research Laboratory, The University of Melbourne, 3010 Parkville, Australia.

Epigenetics & Chromatin
|December 10, 2014
PubMed
Summary

Statistical redundancy between transcription factors (TFs) and histone modifications (HMs) in gene expression is explained by HM distribution and TF binding, not functional overlap. This offers regulatory robustness.

Keywords:
Gene expressionHistone modificationsPredictive modellingTranscription factorsTranscriptional regulation

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Transcription factors (TFs) and histone modifications (HMs) are key regulators of gene expression.
  • Existing models integrating omics data reveal unexpected statistical redundancy between TFs and HMs in explaining mRNA levels.

Purpose of the Study:

  • To investigate the reasons behind the statistical redundancy observed between TFs and HMs in gene expression regulation.
  • To explore how this redundancy varies across different biological processes and gene sets.

Main Methods:

  • Construction of predictive gene expression models using RNA-sequencing, TF and HM ChIP-sequencing, and DNase I hypersensitivity data.
  • Analysis of statistical redundancy within and between TFs and HMs across ontology-classified biological processes.
  • Assessment of the predictive capacity of TFs and HMs in relation to gene function, such as housekeeping genes.

Main Results:

  • Genome-wide statistical redundancy was confirmed for both TFs and HMs.
  • Significant variation in the predictive power of TFs and HMs was observed across different biological processes.
  • The predictive power of HMs was inversely proportional to their enrichment in housekeeping genes.

Conclusions:

  • The observed statistical redundancy is attributed to the heterogeneous distribution of HMs across chromatin domains, not functional redundancy.
  • Statistical redundancy between TFs is explained by nucleosome-mediated cooperative binding.
  • These mechanisms may contribute to cellular regulatory robustness by filtering noise and enabling multi-pathway control.