Chromatin profiling of cortical neurons identifies individual epigenetic signatures in schizophrenia

Both heritability and environment contribute to risk for schizophrenia. However, the molecular mechanisms of interactions between genetic and non-genetic factors remain unclear. Epigenetic regulation of neuronal genome may be a presumable mechanism in pathogenesis of schizophrenia. Here, we performed analysis of open chromatin landscape of gene promoters in prefrontal cortical (PFC) neurons from schizophrenic patients. We cataloged cell-type-based epigenetic signals of transcriptional start sites (TSS) marked by histone H3-K4 trimethylation (H3K4me3) across the genome in PFC from multiple schizophrenia subjects and age-matched control individuals. One of the top-ranked chromatin alterations was found in the major histocompatibility (MHC) locus on chromosome 6 highlighting the overlap between genetic and epigenetic risk factors in schizophrenia. The chromosome conformation capture (3C) analysis in human brain cells revealed the architecture of multipoint chromatin interactions between the schizophrenia-associated genetic and epigenetic polymorphic sites and distantly located HLA-DRB5 and BTNL2 genes. In addition, schizophrenia-specific chromatin modifications in neurons were particularly prominent for non-coding RNA genes, including an uncharacterized LINC01115 gene and recently identified BNRNA_052780. Notably, protein-coding genes with altered epigenetic state in schizophrenia are enriched for oxidative stress and cell motility pathways. Our results imply the rare individual epigenetic alterations in brain neurons are involved in the pathogenesis of schizophrenia

Spatial Proximity and Similarity of the Epigenetic State of Genome Domains

Recent studies demonstrate that the organization of the chromatin within the nuclear space might play a crucial role in the regulation of gene expression. The ongoing progress in determination of the 3D structure of the nuclear chromatin allows one to study correlations between spatial proximity of genome domains and their epigenetic state. We combined the data on three-dimensional architecture of the whole human genome with results of high-throughput studies of the chromatin functional state and observed that fragments of different chromosomes that are spatially close tend to have similar patterns of histone modifications, methylation state, DNAse sensitivity, expression level, and chromatin states in general. Moreover, clustering of genome regions by spatial proximity produced compact clusters characterized by the high level of histone modifications and DNAse sensitivity and low methylation level, and loose clusters with the opposite characteristics. We also associated the spatial proximity data with previously detected chimeric transcripts and the results of RNA-seq experiments and observed that the frequency of formation of chimeric transcripts from fragments of two different chromosomes is higher among spatially proximal genome domains. A fair fraction of these chimeric transcripts seems to arise post-transcriptionally via trans-splicing

Correlation of the spatial proximity values with the Gene Ontology semantic similarity of the genes located in the interacting genome fragments.

<p>(A) Molecular Function. (B) Biological Process. (C) Cellular Component. Black squares show average GO similarity values, dashed lines, standard deviations.</p

Histogram of the number of pairs of interacting genome fragments originating at different chromosomes in the human genome-wide spatial proximity matrix

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Sequence identity levels in 29 considered intervals of spatial proximity values between 1-Mb fragments of different chromosomes in the genome-wide correlation matrix

<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033947#pone.0033947-LiebermanAiden1" target="_blank">[<b>4</b>]</a><b> (see </b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033947#s4" target="_blank"><b>Methods</b></a><b> for the details).</b> Negative spatial proximity values correspond to fragments distant from each other, positive values correspond to proximal fragments. The whisker boxes show quartiles, median (the line in the box), min and max values (the lines outside the box).</p

Correlation between chimeric RNA production and spatial proximity values for (A) the K562 cell line, the GM12878 cell line, and the brain tissue sample (red, orange and green triangles, respectively); the genomic rearrangement dataset (shown in blue); the shuffled control K562 dataset (red whisker boxes); the shuffled control GM12878 dataset (orange whisker boxes); the shuffled control brain dataset (green whisker boxes) and (B) three ChimerDB datasets: mRNA, EST and SRA-derived (red, blue and green dots, respectively).

<p>Correlation between chimeric RNA production and spatial proximity values for (A) the K562 cell line, the GM12878 cell line, and the brain tissue sample (red, orange and green triangles, respectively); the genomic rearrangement dataset (shown in blue); the shuffled control K562 dataset (red whisker boxes); the shuffled control GM12878 dataset (orange whisker boxes); the shuffled control brain dataset (green whisker boxes) and (B) three ChimerDB datasets: mRNA, EST and SRA-derived (red, blue and green dots, respectively).</p

Correlations of the spatial proximity values with expression (A), histone modifications (B), DNA methylation (C), and DNAse sensitivity (D) differences.

<p>The whisker boxes (A,C,D) are as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033947#pone-0033947-g001" target="_blank">Fig. 1</a>. Symbols in B show the medians for different histone modifications; the whisker boxes for all modifications are given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033947#pone.0033947.s001" target="_blank">Fig. S1</a>.</p

The root mean squared error (RMSE) of the spatial proximity prediction with standard deviations (SD) (all values were estimated on the testing set for each split and averaged) of regression models, which used one through 24 features, selected by two algorithms: Successive (successive selection based on individual accuracy) and Greedy (greedy forward feature selection), and the RMSE with SD of an algorithm, which always uses the training set mean as the predicted value.

<p>Additional information about used features can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033947#pone.0033947.s026" target="_blank">Table S1</a>.</p