Trisomy 21 Drives ADARB1 Overexpression and Premature RNA Recoding in the Developing Fetal Brain

**Correspondence to: michael[dot] breen [at] mssm [dot] edu
A-to-I Editing and Rare Disorders Lab: https://labs.icahn.mssm.edu/breenlab/

Understanding how chromosome 21 gene dosage contributes to neurodevelopmental and systemic phenotypes in trisomy 21 (T21) remains a fundamental challenge. We performed transcriptome-wide RNA sequencing on fetal cortical and hippocampal tissues from 20 T21 cases and 27 euploid controls collected between 13–22 weeks post-conception, a critical period for human brain development. Differential expression analysis revealed 572 dysregulated genes in the prefrontal cortex and 519 in the hippocampus (FDR < 5%), with significant enrichment for chromosome 21 genes. Functional enrichment analyses highlighted disruptions in neurodevelopmental, synaptic, and immune-related pathways. Among the most strongly dysregulated genes was ADARB1, a chromosome 21-encoded RNA editing enzyme, whose overexpression in T21 fetal brain was associated with increased adenosine-to-inosine (A-to-I) editing, including recoding sites in GRIA2 (p.R764G), GRIA3 (p.R775G), and GRIK2 (p.Y571C, p.Q621R). A meta-analysis incorporating nine independent transcriptomic datasets spanning early embryonic and progenitor cell types validated robust chromosome 21 dosage effects, including consistent ADARB1 overexpression. Extending these findings, a meta-analysis of A-to-I editing across datasets revealed widespread over-editing at 3′UTRs and at GRIA3 (p.R775G), a site critical for AMPA receptor desensitization. Together, these results implicate dysregulated RNA editing driven by ADARB1 overexpression as a post-transcriptional mechanism contributing to fetal neuropathology in T21 and provide a framework for understanding the broader molecular consequences of chromosome 21 dosage sensitivity during brain development.

WHERE IS THE DATA? (two locations below)

1. All original RNA-sequencing data are publicly at the National Center for Biotechnology Information Gene Expression Omnibus under the following accession number: GSE301886.
   
2. Data can also be downloaded from our interactive RShiny App. It also enables gene-by-gene inspection of between-study effect sizes and heterogeneity, generating meta-analytic forest plots for any gene of interest across the ten harmonized Trisomy 21 RNA-sequencing datasets.
   
   

WHERE IS THE CODE? (it's all below)

Code falls into 2 core areas:

1. Gene-level analyses, including RNA-seq QC, mapping and counting via a detailed and highly cited NextFlow pipeline
   Note that normalization VOOM, differential expression testing limma, network analysis WGCNA, gene set preservation analysis, and cell type deconvolution bMIND are done following these GitHub and publications while following our methods section.

Additionally, gene-level meta-analysis was done using DEG ssummaries from 10 independent studies covering 17 unique comparisons and analyzed using R code.

2. RNA editing-level analyses, including computint an Alu Editing Index (AEI) from a STAR mapped bam file RNAEditingIndexer v1.0 and quantifying RNA editing sites from STAR mapped bam files using known editing sites as previously described. Further details provided below...
   
   

Compute AEI from a STAR mapped bam file:

We used already available software from the RNAEditingIndexer GitHub account to compute an AEI based on a mapped bam file. The method is describe in the original publication, Nat. Methods (2019). Here, we provide an example of bash shell script that executes the AEI on one sample. Requirements and parameters are described in full in the bash script.

An example for computing AEI on human samples:

RNAEditingIndex -d -f Aligned.sortedByCoord.out.bam -o .
--genes_expression ucscHg38GTExGeneExpression.bed.gz
--refseq ucscHg38RefSeqCurated.bed.gz
--snps ucscHg38CommonGenomicSNPs150.bed.gz
-gf ucscHg38Genome.fa
-rb ucscHg38Alu.bed.gz
--genome UserProvided  --paired_end --stranded

Quantify RNA editing from STAR mapped bam files using a list of predefined list of sites (based on a predefined list of sites):

It is often of interest to quantify RNA editing sites based on a user defined list of sites. Samtools mpileup has the functionality to execute this task. Here we provide two perl scripts that will achieve this task. The only requirement is installing a recent version of samtools. In the current study, we leveraged lists of known sites from these three resources: REDIportal, cellular and genetic drivers of RNA editing variability in the human brain, and an atlas of human recoding sites.

query_known_sites.pl= excute mpileup (samtools) to query a list of known editing sites.
parse_pileup_query.pl = a requirement for query_known_sites.pl

Usage: perl query_known_sites.pl [A predefined list of known editing sites] [STAR mapped bam file] [Output file name]

perl query_known_sites.pl CNS_A2G_events.txt SampleName.bam OutputFileName.txt