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Posts Tagged ‘Macrophage’


Transcript Dynamics of Proinflammatory Genes

Author: Larry H Bernstein, MD, FCAP

Transcript Dynamics of Proinflammatory Genes Revealed by Sequence Analysis of Subcellular RNA Fractions

DM Bhatt, A Pandya-Jones, Ann-Jay Tong, I Barozzi, MM Lissner, et al.
Cell 2012;150: 279–290

In addition to documenting the subcellular locations of coding and noncoding transcripts, the results provide a high-resolution view of the relationship between
  • defined promoter and chromatin properties and
    • the temporal regulation of diverse classes of coexpressed genes.
The data also reveal a striking accumulation of full-length yet incompletely spliced transcripts in the chromatin fraction, suggesting that
  • splicing often occurs after transcription has been completed,
  • with transcripts retained on the chromatin until fully spliced.
Summary
Macrophages respond to inflammatory stimuli by modulating the expression of hundreds of genes in
  • a defined temporal cascade,
  • with diverse transcriptional and posttranscriptional mechanisms contributing to the regulatory network.
We examined proinflammatory gene regulation in activated macrophages by
  • performing RNA-seq with fractionated chromatin-associated, nucleoplasmic, and cytoplasmic transcripts.
This methodological approach allowed us
  • to separate the synthesis of nascent transcripts from transcript processing and
  • the accumulation of mature mRNAs.
In addition to documenting the subcellular locations of coding and noncoding transcripts,
the results provide a high-resolution view of the relationship between
  • defined promoter and chromatin properties and
  • the temporal regulation of diverse classes of coexpressed genes.
The data also reveal a striking accumulation of full-length yet incompletely spliced transcripts in the chromatin fraction, suggesting that
  • splicing often occurs after transcription has been completed, with transcripts retained on the chromatin until fully spliced.

Two independent experiments were performed with lipid A-stimulated bone marrow-derived macrophages. The two experiments made use of different macrophages prepared from different mice, several months apart.(A) Pearson pair-wise correlation values (R) derived from an analysis of greater than 500 lipid A-induced genes (>5-fold induced) are shown. Each time point from the first experiment, A, was compared to every other time point from the same experiment and from the second experiment, B.(B) Hierarchical clustering of the R-values from panel A was performed. This analysis reveals that, when only induced genes are considered, each time point from each experiment correlates more closely with the corresponding time point from the other experiment than with any of the other time points from either experiment.(C)

This analysis reveals that, when the transcript levels of expressed genes are compared,
  • each time point from a given experiment correlates with the same time point from the independent experiment.
The results reveal close correlations between all time-points from both experiments, presumably because genes that are consistently unexpressed (i.e., not counted in B) are contributing to the high degree of correlation. Nevertheless, the time points of each independent experiment still have the highest degree of correlation with each other.
Hierarchical clustering of the R values from panel D was performed. As with other clusterings, each sample clusters with its cognate time point in the independent experiment
Highlights
► Coding and noncoding transcripts exhibit characteristic subcellular distributions
► The most potently induced genes favor promoters with low CpG content
► Full-length, incompletely spliced transcripts accumulate on the chromatin
► Delayed transcript release may reflect a requirement for the completion of splicing
Eukaryotic transcription overview

Eukaryotic transcription overview (Photo credit: Allen Gathman)

English: Nucleosome structure.

English: Nucleosome structure. (Photo credit: Wikipedia)

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