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. 2021 Feb 25:14:624881.
doi: 10.3389/fnmol.2021.624881. eCollection 2021.

Identification of Natural Antisense Transcripts in Mouse Brain and Their Association With Autism Spectrum Disorder Risk Genes

Affiliations

Identification of Natural Antisense Transcripts in Mouse Brain and Their Association With Autism Spectrum Disorder Risk Genes

Baran Koç et al. Front Mol Neurosci. .

Abstract

Genome-wide sequencing technologies have greatly contributed to our understanding of the genetic basis of neurodevelopmental disorders such as autism spectrum disorder (ASD). Interestingly, a number of ASD-related genes express natural antisense transcripts (NATs). In some cases, these NATs have been shown to play a regulatory role in sense strand gene expression and thus contribute to brain function. However, a detailed study examining the transcriptional relationship between ASD-related genes and their NAT partners is lacking. We performed strand-specific, deep RNA sequencing to profile expression of sense and antisense reads with a focus on 100 ASD-related genes in medial prefrontal cortex (mPFC) and striatum across mouse post-natal development (P7, P14, and P56). Using de novo transcriptome assembly, we generated a comprehensive long non-coding RNA (lncRNA) transcriptome. We conducted BLAST analyses to compare the resultant transcripts with the human genome and identified transcripts with high sequence similarity and coverage. We assembled 32861 de novo antisense transcripts mapped to 12182 genes, of which 1018 are annotated by Ensembl as lncRNA. We validated the expression of a subset of selected ASD-related transcripts by PCR, including Syngap1 and Cntnap2. Our analyses revealed that more than 70% (72/100) of the examined ASD-related genes have one or more expressed antisense transcripts, suggesting more ASD-related genes than previously thought could be subject to NAT-mediated regulation in mice. We found that expression levels of antisense contigs were mostly positively correlated with their cognate coding sense strand RNA transcripts across developmental age. A small fraction of the examined transcripts showed brain region specific enrichment, indicating possible circuit-specific roles. Our BLAST analyses identified 110 of 271 ASD-related de novo transcripts with >90% identity to the human genome at >90% coverage. These findings, which include an assembled de novo antisense transcriptome, contribute to the understanding of NAT regulation of ASD-related genes in mice and can guide NAT-mediated gene regulation strategies in preclinical investigations toward the ultimate goal of developing novel therapeutic targets for ASD.

Keywords: ASD; antisense transcriptome; autism; development; lncRNA; mPFC; natural antisense transcripts; striatum.

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Conflict of interest statement

BK, RS, NG, TB, and BJH were full time employees of F. Hoffmann-La Roche Ltd. of Basel, Switzerland during the course of studies. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Methodology and key statistics for the current study. (A) Summary statistics for the main analyses conducted in the study. (B) A schematic explaining the methodology to extract antisense reads for a selected gene (Gene X). Only antisense reads in the overlap free zone were used in analyses. (C) Number of differentially expressed genes across developmental age and brain regions. (D) Histogram of number of antisense contigs per gene.
FIGURE 2
FIGURE 2
Differential expression between mPFC and striatum and over-representation analysis. (A) Hierarchically clustered heatmap of all genes differentially expressed in mPFC versus striatum. (B) Dotplot of top enriched Reactome pathway annotations related to the central nervous system among genes differentially expressed in mPFC versus striatum. (C) Hierarchically clustered heatmap of ASD-related genes differentially expressed in mPFC versus striatum.
FIGURE 3
FIGURE 3
Differential expression across development and over-representation analysis. (A) Hierarchically clustered heatmap of all genes differentially expressed across developmental ages. (B) Dotplot of top enriched Gene Ontology annotations related to the central nervous system among genes differentially expressed across developmental ages in mPFC. (C) Hierarchically clustered heatmap of ASD-related genes differentially expressed across developmental ages.
FIGURE 4
FIGURE 4
Principal component analysis (PCA) and clustering of the assembled contigs. (A) Plot of the first two principal components for the normalized expression values of all antisense contigs. (B) Hierarchically clustered heatmap of contigs mapped to ASD-related genes expressed across developmental ages and between mPFC and striatum.
FIGURE 5
FIGURE 5
Tissue enrichment analysis of genes with at least one cognate antisense contig. (A) Violin plot of tissue specificity score distribution for all genes with at least one cognate antisense contig. (B) Box plot of tissue specificity score distribution for ASD-related genes with at least one cognate antisense contig.
FIGURE 6
FIGURE 6
Correlation analysis of gene-antisense contig pairs using linear model. (A) Scatterplot showing the relationships between linear model slopes for all gene-antisense contig pairs. (B) Scatterplot showing the relationships between linear model slopes for ASD-related gene-antisense contig pairs.

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