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. 2017 Jun 13;13(6):e1005602.
doi: 10.1371/journal.pcbi.1005602. eCollection 2017 Jun.

Histone Posttranslational Modifications Predict Specific Alternative Exon Subtypes in Mammalian Brain

Free PMC article

Histone Posttranslational Modifications Predict Specific Alternative Exon Subtypes in Mammalian Brain

Qiwen Hu et al. PLoS Comput Biol. .
Free PMC article


A compelling body of literature, based on next generation chromatin immunoprecipitation and RNA sequencing of reward brain regions indicates that the regulation of the epigenetic landscape likely underlies chronic drug abuse and addiction. It is now critical to develop highly innovative computational strategies to reveal the relevant regulatory transcriptional mechanisms that may underlie neuropsychiatric disease. We have analyzed chromatin regulation of alternative splicing, which is implicated in cocaine exposure in mice. Recent literature has described chromatin-regulated alternative splicing, suggesting a novel function for drug-induced neuroepigenetic remodeling. However, the extent of the genome-wide association between particular histone modifications and alternative splicing remains unexplored. To address this, we have developed novel computational approaches to model the association between alternative splicing and histone posttranslational modifications in the nucleus accumbens (NAc), a brain reward region. Using classical statistical methods and machine learning to combine ChIP-Seq and RNA-Seq data, we found that specific histone modifications are strongly associated with various aspects of differential splicing. H3K36me3 and H3K4me1 have the strongest association with splicing indicating they play a significant role in alternative splicing in brain reward tissue.

Conflict of interest statement

The authors have declared that no competing interests exist.


Fig 1
Fig 1
A. ChIP-Seq signal on flanking regions. ChIP-Seq signal is calculated as the number of ChIP-Seq reads (start position of reads) that aligned to each individual position of flanking regions. B: A schematic representation of different type of alternative splicing exons.
Fig 2
Fig 2
Distribution of ChIP-Seq signal on +/- 200 bp flanking regions of different exon types for four histone marks: (A) H3K36me3, (B) H3K27me3, (C) H3K9me2 and (D) H3K4me1.
Fig 3
Fig 3
The difference between alternatively spliced exon and constitutive exon in (A) Cocaine and (B) Saline treatments.
Fig 4
Fig 4. Importance score of variables from random forest model.
Fig 5
Fig 5. Schematic of exon complexity analysis.
Exon complexity is defined as the number of distinct locations that are connected to either end of an exon, as measured by spliced reads. An algorithm was developed to rigorously control for variables other than splicing complexity that may confound our findings, including number of exons, exon order, ChIP-signal window size and gene expression level. Analysis of Covariance was used to measure ChIP-Seq signal among different exon complexity levels.
Fig 6
Fig 6
The distributions of p-vaues across the parameter space, for the data and for permuted controls, for four histone marks: H3K36me3 (A), H3K9me2 (B), H3K27me3 (C) and H3K3me1 (D).
Fig 7
Fig 7. Contribution of histone modifications to the regulation of alternative splicing.
Unbiased global analysis reveals that the enrichment of specific histone marks varies with type of alternatively spliced exon. H3K36me3 and H3K4me1 show a much stronger association with alternative splicing than H3K27me3 and H3K9me2. H3K36me3 is maximally enriched at alternative PolyA exons, while at promoters it is depleted and H3K4me3 is maximally enriched.

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Grant support

Funding for this work was provided by The Perelman School of Medicine (EAH), The Charles E Kaufman Foundation (Grant KA2016_85225 to EAH), The Whitehall Foundation (Grant 2016-12-33 to EAH), and The National Center for Advancing Translational Sciences (5UL1TR000003, GRG). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.