Cellular and genetic drivers of RNA editing variation in the human brain

Posttranscriptional adenosine-to-inosine modifications amplify the functionality of RNA molecules in the brain, yet the cellular and genetic regulation of RNA editing is poorly described. We quantified base-specific RNA editing across three major cell populations from the human prefrontal cortex: glutamatergic neurons, medial ganglionic eminence GABAergic neurons, and oligodendrocytes. We found more selective editing and RNA hyper-editing in neurons relative to oligodendrocytes. The pattern of RNA editing was highly cell type-specific, with 189,229 cell type-associated sites. The cellular specificity for thousands of sites was confirmed by single nucleus RNA-sequencing. Importantly, cell type-associated sites were enriched in GTEx RNA-sequencing data, edited ∼twentyfold higher than all other sites, and variation in RNA editing was predominantly explained by neuronal proportions in bulk brain tissue. Finally, we discovered 661,791 cis-editing quantitative trait loci across thirteen brain regions, including hundreds with cell type-associated features. These data reveal an expansive repertoire of highly regulated RNA editing sites across human brain cell types and provide a resolved atlas linking cell types to editing variation and genetic regulatory effects.

This work entails four main levels of analysis:

1. Compute an Alu Editing Index (AEI) from a STAR mapped bam file (RNAEditingIndexer v1.0)
   
2. Quantifying RNA editing sites from STAR mapped bam files using de novo methods (reditools v2.0)
   
3. Quantifying RNA editing from STAR mapped bam files using a list of predefined list of sites (code provided below)
   
4. Quantifying RNA hyper-editing sites from STAR unmapped fastq files (method based on Porath et al., 2017)
   

1. 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.

AEI.sh

2. Quantify RNA editing sites from STAR mapped bam files using de novo methods:

We used already available software from the reditools v2.0 GitHub account to quantify de novo RNA editing sites based on a STAR mapped bam file. The method is describe in the original publication, BMC Bioinformatics (2020). Here, we provide an example of bash shell script that executes reditools 2.0 on one sample. Requirements and parameters are described in full in the bash script.

reditools_caller.sh

3. 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.

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

Helpful data files:

CNS_A2G_events.txt = A predefined list of 166,215 A-to-I RNA editing sites detected within each cell population.
CNS_A2G_15221edits.txt = A matrix of 15,221 RNA editing sites we detected across all three cell populations.

4. Quantifying RNA hyper-editing sites from STAR unmapped fastq files:

We used already available software from the RNA hyper-editing GitHub account to quantify RNA hyper-editing sites based on a STAR unmapped fastq files. The method is describe in the original publication, Genome Biology (2017). We provide a detailed example of how we execute this pipeline providing all code, with minor modifications, at Winston Cuddleston's GitHub account .

All data are available through an interactive Rshiny interface

An Rshiny app enabling users to download sites based on a gene of interest:
https://breenms.shinyapps.io/CNS_RNA_Editing
Notably, all GTEx RNA editing matrices are downloadable through this Rshiny app as they were too large to be hosted through github.

Supplemental Data Tables 1-11:

Table S1. Alu editing index and hyper-editing across purified cortical cell populations.
Table S2. Annotation of all 189,229 cell-specific RNA editing sites in the current study.
Table S3. RNA editing sites as a function of ADAR expression and RBPs.
Table S4. Differential RNA editing across cell types, plus pathway and disease enrichment.
Table S5. RNA recoding events across cell types.
Table S6. rhAmpSeq validation of recoding RNA-editing sites.
Table S7. The number of RNA editing events by gene length.
Table S8. Validation of RNA editing sites by snRNA-seq cellular pools.
Table S9. Features of RNA editing in GTEx brain regions.
Table S10. Total number of sites by genic region following sample thresholds per brain region.
Table S11. Max-edQTLs across bulk GTEx brain regions.