A Cytoplasmic Index for Quantifying Immune-Related A-to-I RNA Editing

Abstract

Distinguishing self from non-self is a major challenge for the immune system. Endogenous cytoplasmic double-stranded RNA (dsRNA) can mimic viral RNA and activate immune sensors like MDA5. ADAR1-mediated A-to-I editing disrupts base-pairing to suppress immunogenicity of these endogenous structures. Global editing indices are widely used to probe this crucial ADAR1 function. However, they are dominated by nuclear pre-mRNA edits with limited immune relevance. Here we present the Cytoplasmic Editing Index (CEI) that quantifies editing specifically within dsRNA structures in mature cytoplasmic transcripts, which carry higher immunological risk. Analyzing over 25,000 RNA-seq samples, we demonstrate CEI captures ADARp150 activity and outperforms the global editing index in terms of sensitivity and signal-to-noise, enabling sharper tissue-specific profiling, enhanced detection power of infection-induced editing changes, and stronger association with cancer prognoses. An open-source, cloud-native pipeline delivers end-to-end, reproducible analysis at very low cost, supporting immediate, scalable adoption. Micro-abstractThe Cytoplasmic Editing Index (CEI) quantifies immune-relevant A-to-I RNA editing in inverted Alu clusters within 3'UTRs, capturing interferon-inducible ADAR1p150-dependent events. Analysis of >25,000 RNA-seq samples demonstrates CEI outperforms the global editing index in sensitivity and specificity, resolving tissue- and infection-linked editing patterns. An open-source, cloud-native pipeline enables scalable, low-cost deployment. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=82 SRC="FIGDIR/small/692070v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@159ae9org.highwire.dtl.DTLVardef@6d6119org.highwire.dtl.DTLVardef@101b4a1org.highwire.dtl.DTLVardef@f9d96b_HPS_FORMAT_FIGEXP M_FIG C_FIG