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

Domain: biology
Published: 2026-03-31
Editorial outcome: major_revision

Abstract
Understanding how chromosome 21 gene dosage contributes to neurodevelopmental phenotypes in trisomy 21 (T21) remains a fundamental challenge. Here, we perform transcriptome-wide RNA-sequencing of fetal cortical and hippocampal tissues from T21 cases and euploid controls collected during mid-gestation, a critical window for human brain development. We identify widespread gene expression dysregulation with significant enrichment for chromosome 21 genes and perturbation of neurodevelopmental, synaptic, and immune-related pathways. Among the most strongly dysregulated genes is ADARB1, a chromosome 21-encoded RNA editing enzyme, whose overexpression associates with increased adenosine-to-inosine RNA editing, with consistent over-editing at functionally important recoding sites in glutamate and GABA receptor-related genes, including GRIK2, GRIA2, GRIA3, and GABRA3, across cortex and hippocampus. Meta-analyses across independent transcriptomic datasets validate robust chromosome 21 dosage effects, including ADARB1 overexpression and over-editing at 3′ UTRs and GRIA3. These findings implicate dysregulated RNA editing as a post-transcriptional mechanism contributing to fetal neuropathology in T21.

Introduction
Trisomy 21 (T21), the genetic hallmark of Down syndrome (DS), results in the triplication and overexpression of genes on chromosome 21, disrupting key neurodevelopmental processes1. Although the genetic cause of DS is well defined, the molecular mechanisms by which T21 disrupts fetal brain development remain in incompletely understood and mechanistically unresolved. Transcriptomic analyses across developmental stages and brain regions have revealed widespread dysregulation of gene expression, implicating both chromosome 21 genes and broader genome-wide effects2–4. In fetal brain from individuals with DS, studies have identified spatially and temporally specific transcriptional changes, including impaired oligodendrocyte differentiation and disrupted myelination3, suggesting a mechanistic link to abnormalities in white matter integrity and neural circuit formation. In postnatal and adult DS brains, transcriptional dysregulation persists, extending to pathways involved in oxidative stress responses, cytoskeletal organization, and immune signaling5–7.

Emerging evidence suggests that post-transcriptional regulation, particularly RNA editing, may play a critical role in the neurodevelopmental phenotypes of DS. ADARB1, a chromosome 21-encoded enzyme that catalyzes adenosine-to-inosine (A-to-I) editing, modulates RNA sequence diversity, neuronal excitability, and synaptic function during brain development8,9. A dosage-sensitive gene, ADARB1 is consistently overexpressed in DS cellular models and in vivo tissues4,10–13. However, the extent to which this alters RNA editing activity in the developing human brain remains unclear. By contrast, precise editing disruptions at recoding sites have been implicated in related neurodevelopmental and neuropsychiatric disorders, including Alzheimer’s disease14, autism spectrum disorder15, and schizophrenia16, where they impair synaptic signaling and circuit maturation. Whether similar editing-dependent mechanisms contribute to neurodevelopmental abnormalities in DS remains an open question.

In parallel with transcriptional and post-transcriptional disruptions, immune dysregulation has emerged as a central and persistent feature of DS. The triplication of interferon receptor genes (IFNAR1, IFNAR2, and IFNGR2) on chromosome 21 amplifies interferon signaling pathways, leading to chronic immune activation and sustained upregulation of interferon-stimulated genes17–20. While ADAR, particularly the interferon-inducible p150 isoform, is canonically upregulated by inflammatory signals21,22, our prior transcriptomic analyses of non-DS human brain tissues revealed that interferon activation is also associated with increased ADARB1 expression23, suggesting that ADARB1 may also be responsive to immune tone in vivo. This is particularly relevant in DS, where gene dosage imbalance uniquely co-occurs with heightened interferon activity, creating a biologically distinct environment that may alter RNA editing programs. The intersection of interferon signaling, gene triplication, and post-transcriptional control represents a largely unexplored axis of pathology with the potential to disrupt neurodevelopmental trajectories and impair synaptic function.

Despite these insights, key gaps remain in our understanding of DS molecular pathology during early brain development. To date, only two transcriptomic studies have examined DS fetal brain tissue (using microarrays): one analyzing 15 DS and 15 control brains3, and another profiling 9 DS and 8 control brains4. While foundational, these studies, along with independent hiPSC-derived neuron models10–13, have been limited in both scale and resolution and were not designed to assess post-transcriptional regulation such as RNA editing. Additional efforts have focused primarily on postnatal or adult tissues using targeted PCR or, more recently, single-nucleus RNA sequencing (snRNA-seq)3,4,7,24. Although snRNA-seq provides cell-type-specific resolution, it is not well-suited for genome-wide, quantitative analysis of editing levels in the fetal brain. Furthermore, no study has systematically examined how chromosome 21 gene dosage, interferon signaling, and RNA editing intersect during early brain development. Addressing these gaps requires high-resolution transcriptomic profiling across key developmental windows and brain regions.

Here, we address these critical gaps by performing transcriptome-wide RNA sequencing of fetal prefrontal cortical and hippocampal tissues from 20 individuals with T21 and 27 euploid controls, spanning 13–22 weeks post-conception, a key window for human brain maturation (Fig. 1). By meta-analyzing these data with 9 independent transcriptomic datasets, we define robust and reproducible chromosome 21 dosage effects and reveal convergent transcriptional and post-transcriptional disruptions across diverse developmental stages and cellular contexts. Focusing on RNA editing, we demonstrate that ADARB1 is a dosage-sensitive regulator of A-to-I editing in the developing brain, driving premature and excessive recoding at key genes during neurodevelopment. Together, these findings establish RNA editing dysregulation as a conserved mechanism of T21 neuropathology and provide a framework for dissecting post-transcriptional contributions to altered brain development in DS.

Results
We generated deep bulk RNA-sequencing profiles (34.5M±5.4M reads/sample) from fetal prefrontal cortex (PFC) and hippocampal tissues of individuals with Trisomy 21 (T21; n=20) and age-, sex- and ancestry-matched euploid controls (n=27) (Supplemental Data 1). To assess global transcriptomic variation, we initially applied a unified normalization strategy across all samples, quantifying expression for 16,737 genes. Rigorous quality control analyses were then conducted to identify key sources of variability in the dataset (Figs. S1–S3). First, cell type deconvolution revealed similar estimated neuronal proportions in PFC (~62.2%) and hippocampus (~62.1%), with no significant differences between T21 and control groups (Fig. S1). Next, principal component analysis (PCA) highlighted neuronal content as the dominant axis of variation (PC1; 29.2% variance explained), while PC2 (17.8% variance) clearly separated PFC and hippocampal tissues (Fig. S2). To formally assess regional differences, we performed differential expression analysis between brain regions and identified 9163 differentially expressed genes at FDR<5%, accounting for ~54% of the transcriptome (Fig. S3). Based on these marked region-specific signatures, we conducted all downstream analyses independently for PFC and hippocampus using tailored, region-specific modeling strategies (see “Methods”).

Transcriptomic effects of T21 during early fetal brain development
To identify critical covariates for inclusion in our comparative analysis of T21 individuals and controls within each region, we used a linear mixed model to quantify the fraction of expression variance attributable to known clinical and technical factors, including neuronal cell type proportion, percentage of mRNA reads (defined as reads mapping to exons and untranslated regions), gestational age, RNA integrity number (RIN), T21 status, and sex, for each gene, separately within PFC and hippocampus (Fig. S4). Collectively, these variables accounted for ~57.1% of transcriptome-wide variation in the PFC and ~34.9% in the hippocampus. In the PFC, estimated neuronal cell type proportions exerted the largest effect, explaining a median 25.1% of the observed variation, followed by the percentage of mRNA reads (6.2%), gestational age (2.4%), and RIN (0.4%), with remaining factors contributing modestly. Expression variation attributable to T21 itself was detectable, particularly in a subset of genes located on chromosome 21, confirming the robustness of our approach. Similar patterns of variance decomposition were observed independently in the hippocampus (Fig. S4E–H).

We next performed differential expression analysis comparing individuals with T21 and controls, adjusting for the possible influence of estimated neuronal cell type proportions, RIN, gestational age, sex, and percentage of mRNA reads, within each region. This analysis identified 572 differentially expressed genes (DEGs) in the PFC and 519 in the hippocampus at transcriptome-wide significance (FDR<5%) (Fig. 2A, B, Fig. S5, Supplemental Data 2). While gene expression changes in T21 were positively correlated across regions (Spearman’s correlation, ρ=0.40), the DEG profiles were largely region-specific. Only 155 genes were consistently dysregulated in both the PFC and hippocampus, most of which were located on chromosome 21 (Fig. 2C, D). Among the DEGs, there was significant enrichment of upregulated chromosome 21 genes in individuals with T21 in both the PFC (n = 122, p=6.7×10−142, one-sided FET) and hippocampus (n = 118, p=8.6×10−137, one-sided FET) (Fig. 2C, E), reflecting the canonical gene dosage effects associated with T21.

Despite the strong enrichment for upregulated chromosome 21 genes, we did not observe a universal dosage effect across the entire chromosome. In the PFC, 40 genes on chromosome 21 showed no significant expression changes between T21 cases and controls, with a similar pattern observed for 50 genes in the hippocampus (Fig. 2F). These genes were distributed relatively evenly across the chromosome. Notably, chromosome 21 genes that were significantly upregulated in T21 cases were more highly expressed in T21 and control samples compared to non-significant genes (PFC: fold-change=2.29, p=1.1×10−¹⁰; hippocampus: fold-change=2.31, p=1.3×10−¹¹; Wilcoxon rank-sum test) (Fig. S6). This suggests that gene dosage sensitivity in the T21 brain preferentially affects genes that are already more actively transcribed, while lower-expressed genes may evade dysregulation, potentially due to tighter transcriptional control. Among the dysregulated chromosome 21 genes, we highlight increased expression of key interferon signaling components, including interferon gamma receptor 2 (IFNGR2) and interferon alpha/beta receptor subunits 1 and 2 (IFNAR1, IFNAR2), in both the PFC (FDR p=6.7×10−⁴) and hippocampus (FDR p=3.7×10−²). These genes, which canonically mediate interferon signaling in immune contexts, have also been implicated in brain development and glial function, raising the possibility that their overexpression contributes to immune-related transcriptional changes in the T21 brain. Additionally, we observed upregulation of several collagen genes critical for brain extracellular matrix integrity (COL6A1, COL6A2, COL18A1) in both regions (PFC FDR p=1.4×10−²; hippocampus FDR p=2.5×10−²), as well as increased expression of genes involved in ligase activity (ATP5PF, ATP5PO, GART, HLCS, DIP2A) (PFC FDR p=2.0×10−²; hippocampus FDR p=3.7×10−²).

Developmental imbalance of fetal gene expression patterns in individuals with T21
A substantial proportion of dysregulated T21 genes reside outside chromosome 21 and likely represent secondary, downstream effects. To assess whether non-chromosome 21 DEGs reflect broader disruptions in developmental timing, we leveraged transcriptomic data from the BrainSpan atlas to define normative gene expression trajectories in the PFC and hippocampus across prenatal and postnatal stages (Supplemental Data 3). For each gene, we computed a t-statistic capturing its expression bias toward prenatal or postnatal timepoints in typical development (see “Methods”). We then tested whether T21-overexpressed and T21-underexpressed genes were disproportionately drawn from genes with strong developmental timing biases, by comparing their BrainSpan-derived t-statistic distributions within each brain region. This analysis revealed a striking asymmetry: genes downregulated in T21 were significantly enriched for prenatally expressed genes (PFC: p=0.02; hippocampus: p=2.3×10−⁵; Mann–Whitney U), whereas upregulated genes were enriched for postnatally expressed genes (PFC: p=0.01; hippocampus: p=7.6×10−⁷; Mann–Whitney U) (Fig. 2G, H). These findings suggest that T21 disrupts the temporal coordination of gene expression, prematurely activating postnatal programs while attenuating prenatal ones during early brain development.

Systems-level dysregulation of gene expression in T21
To further characterize the biological processes underlying this temporal dysregulation, we performed functional enrichment analysis of differentially expressed genes located outside chromosome 21 (Supplemental Data 4). Using a competitive gene set ranking framework, we identified significant enrichment of downregulated genes involved in mitochondrial translation and mRNA binding in both the PFC and hippocampus (Fig. 3A, B), pointing to impaired metabolic function and post-transcriptional regulation during early neurodevelopment. This was accompanied by reduced expression of genes critical for translational initiation and elongation, ribosomal RNA processing, and nonsense-mediated mRNA decay, suggesting broader suppression of protein synthesis and RNA quality control pathways in the T21 fetal brain. In contrast, over-expressed genes were enriched for sodium channel activity across both regions and extracellular matrix (ECM) organization specifically in the hippocampus, including DCN (Decorin) and LTBP4, which regulate TGF-β signaling in the ECM microenvironment. Together, these results suggest that T21-associated gene dysregulation involves both suppression of early translational and post-transcriptional machinery and premature activation of pathways associated with neuronal excitability and structural maturation.

While differential expression and functional enrichment analyses identify genes and pathways with altered mean expression levels, they do not capture whether the underlying coordination of gene activity within biological programs is preserved. To assess whether T21 disrupts the structural organization of transcriptional networks, we first applied weighted gene co-expression network analysis (WGCNA) to identify unsupervised gene modules in PFC and hippocampus. This analysis did not reveal major differences in overall module architecture between T21 and control samples (Fig. S7).

We next used a gene set preservation framework to test whether predefined biological gene sets retained coordinated expression across groups, an approach that can detect systems-level dysregulation even in the absence of changes in gene abundance (see “Methods”) (Supplemental Data 5). In the PFC, eight gene sets exhibited markedly reduced preservation in T21, including those involved in astrocyte activation, sodium ion transport, and macrophage differentiation, suggesting altered regulatory interactions among glial cell types (Fig. 3C, D). In contrast, the hippocampus showed more widespread co-expression disruption, with 259 gene sets demonstrating poor preservation between individuals with T21 and controls (Fig. 3E, F). These gene sets clustered into four major domains: (1) neurodevelopment and synaptic plasticity (e.g., synapse maturation, long-term synaptic depression, positive regulation of excitatory postsynaptic potential); (2) chromatin and epigenetic regulation (e.g., ATP-dependent chromatin remodeler activity, chromatin protein adapter activity); (3) mitochondrial metabolism (e.g., NADH dehydrogenase ubiquinone activity, oxidoreductase activity, NADH metabolic process); and (4) immune signaling (e.g., interleukin-4 production, regulation of neuroinflammatory response, monocyte differentiation). These findings suggest that T21 disrupts the integrity of gene co-regulation across multiple biological systems, particularly those governing neuronal signaling, energy metabolism, and glial immune responses.

Meta-analysis across developmental datasets validates chromosome 21 dosage effects in T21
To validate key findings and assess the generalizability of chromosome 21 dosage effects, we conducted a meta-analysis combining our fetal RNA-seq data with nine publicly available RNA-sequencing datasets (Table 1). Altogether, we analyzed 108 T21 and 117 euploid control samples from ten datasets, spanning early gestational stages and diverse neural lineages (e.g., iPSCs, NPCs, neurons, fibroblasts), thereby capturing diverse but developmentally relevant cellular states. To minimize inter-study bias, all datasets were reprocessed through a unified RNA-seq analysis pipeline, standardizing quantification and differential expression estimation across studies. For each dataset, a linear model with empirical Bayes moderation was applied to estimate log₂ fold-changes, adjusting for available biological and technical covariates (e.g., age, sex, cell type proportion, and sequencing depth). These covariate-adjusted estimates were then synthesized across datasets using a random-effects meta-regression framework to account for between-study heterogeneity. One previously published microarray study of T21 fetal brain3 was excluded from our meta-analysis due to technical and analytic limitations. Despite profiling overlapping regions and developmental stages, it showed only modest transcriptome-wide concordance with our RNA-seq data (PFC: r=0.065; hippocampus vs. cerebellum: r=0.11) and weak correlation for chromosome 21 genes (r=0.27 and 0.10, respectively; Fig. S8, Supplemental Data 6). These discrepancies, along with the platform’s inability to assess RNA editing, precluded its inclusion in our synthesis. While cellular contexts varied across datasets, our goal was to identify genes consistently dysregulated across developmental lineages, rather than those specific to any single cell type. To ensure analytical consistency across platforms and study designs, each dataset was reprocessed from raw FASTQ files using a standardized RNA-seq pipeline for alignment, quantification, and normalization (see “Methods”).

Despite variability in sample sizes and experimental conditions, a consistent pattern emerged: genome-wide fold-change distributions revealed a distinct and reproducible elevation of gene expression on chromosome 21 in all comparisons (Fig. S9). When plotting log₂ fold changes across the transcriptome, chromosome 21 genes consistently showed an upward shift relative to the rest of the genome, reflecting their dosage-driven overexpression in T21. This global effect, observed across iPSCs, NPCs, neurons, and fibroblasts, reinforces the robustness of chromosome 21 gene dysregulation as a defining feature of T21, regardless of tissue or developmental context. Notably, this effect was not uniformly distributed across the chromosome: only a subset of chromosome 21 genes showed consistent overexpression (Fig. S10), reinforcing a model of targeted rather than global dosage sensitivity, consistent with emerging concepts of dosage compensation and chromatin architecture.

To formally synthesize findings across datasets, we performed a transcriptome-wide meta-analysis, identifying 543 upregulated and 437 downregulated genes in T21 at strict genome-wide significance (FDR< 1%) (Fig. 4A, Supplemental Data 7), reflecting a more conservative threshold appropriate for integrating heterogeneous datasets and reducing false positives. Among the upregulated genes, 149 (27.4%) were located on chromosome 21. The average log₂ fold-change for chromosome 21 genes approximated 0.58 (1.5-fold), consistent with expectations for gene dosage, while variation in effect sizes pointed to selective transcriptional sensitivity across loci (Fig. 4B). This analysis produced a robust catalog of consistently dysregulated chromosome 21 genes across diverse cellular contexts, including ANKRD18A, LNCTSI, MX1, IL10RB, and APP (Fig. 4C). Forest plots confirmed reproducible overexpression for multiple high-confidence genes, including DYRK1A, IFNAR1, IFNAR2, and ADARB1 (Fig. 4D–G). Notably, transcriptome-wide differential expression patterns in our fetal PFC and hippocampus closely mirrored those from independent datasets, underscoring the robustness and cross-context validity of our meta-analytic framework (Fig. S11).

Among these, ADARB1 was consistently overexpressed across most independent studies, making it a particularly compelling candidate for further investigation due to its known role in RNA editing and neurodevelopment. This gene encodes an adenosine deaminase involved in A-to-I editing, a post-transcriptional mechanism that modifies RNA sequences to regulate protein function and neuronal excitability during development9. Despite its consistent overexpression in both our fetal brain dataset and across external studies, ADARB1 has received relatively limited attention in the context of T21. Its dosage sensitivity, role in RNA editing, and potential interaction with interferon signaling pathways position it as a strong candidate for investigating the downstream consequences of gene dosage imbalance. We therefore next examined whether ADARB1 overexpression in T21 is associated with altered A-to-I editing in the developing brain.

ADARB1 is associated with increases in A-to-I editing and excessive RNA recoding
A-to-I editing is catalyzed by the ADAR family of enzymes, including ADAR1 and ADAR2 (ADARB1), which act on double-stranded RNA structures to diversify the transcriptome25. ADAR1, encoded on chromosome 1, exists in two isoforms: the constitutively expressed nuclear isoform p110 and the cytoplasmic, interferon-inducible isoform p150, which plays a key role in immune signaling26. ADAR2 is highly expressed in the central nervous system and mediates site-specific editing of protein-coding transcripts essential for neuronal development27. In contrast, ADAR3 (ADARB2), also brain-specific, lacks catalytic activity and is thought to act as a dominant-negative regulator of A-to-I editing through competition for RNA substrates28.

In our fetal brain RNA-seq data, we observed significant dosage-sensitive upregulation of ADARB1 in individuals with T21, in both the PFC (FDR p=4.0×10−⁷) and hippocampus (FDR p=0.04), with no significant changes in expression for ADAR or ADARB2 (Fig. 5A, Supplemental Data 8). To assess the impact of this dosage effect on global A-to-I editing activity, we computed the Alu editing index (AEI), defined as the ratio of A-to-G edited reads to total adenosine coverage in Alu elements across the transcriptome (see “Methods”). AEI values were significantly elevated in T21 compared to controls in the PFC (p=0.01, linear regression), and showed a marginal trend in the hippocampus (p=0.05) (Fig. 5B). To evaluate the contribution of each ADAR enzyme to global editing levels, we performed a bootstrapped linear regression of AEI values against the expression of ADAR, ADARB1, and ADARB2. Among the three, ADARB1 exhibited the strongest positive association with AEI (bootstrapped slope=0.27), while ADAR (slope=0.03) and ADARB2 (slope=0.11) showed weaker and more variable effects (Fig. 5C). Confidence intervals for ADAR and ADARB2 were broad, reflecting greater inter-sample variability. These results position ADARB1 as the primary contributor to elevated A-to-I editing in T21 fetal brain, with the steepest and most consistent slope indicating the greatest per-unit effect of gene expression on editing activity, capturing regulatory influence more directly than correlation-based approaches.

We next profiled high-confidence A-to-I sites across the transcriptome using a curated database and a supervised detection pipeline, followed by stringent quality control filtering (see “Methods”). This approach yielded 19,346 sites in the PFC and 18,585 in the hippocampus, with ~80% of sites shared between regions (Fig. 5D). As expected, most editing sites were located within 3′ UTRs and introns. Next, differential editing analyses were conducted between T21 and control samples, adjusting for estimated neuronal cell type proportions, RIN, gestational age, sex, and percentage of mRNA reads, within each region. We identified 39 dysregulated sites in the PFC and 151 in the hippocampus (FDR<5%), the vast majority of which were over-edited in T21 (Fig. 5E, F, Supplemental Data 9), with predominant localization to 3′ UTRs. In fact, 3′ UTR sites showed enrichment among differentially edited sites in the PFC (1.20-fold enrichment; p=0.049, one-sided FET) and hippocampus (1.56-fold enrichment; p=1.6×10−⁵, one-sided FET). Notably, exonic sites showed significant enrichment for differentially edited sites in the PFC (~7.1-fold enrichment, p=0.00064, one-sided FET) with a suggestive enrichment in the hippocampus (1.78-fold enrichment; p=0.19, one-sided FET). Across genes, RNA editing in the 3′ UTR showed an inverse correlation with expression (Fig. S12), consistent with the idea that increased A-to-I editing destabilizes transcripts by altering structure, miRNA binding, or translational efficiency. Similar negative coupling across other genic regions suggests a pervasive link between editing activity and reduced mRNA abundance. Notably, intersecting differentially expressed genes with transcripts harboring over-edited sites revealed minimal overlap, with only one gene in the PFC (MRPL30) and one in the hippocampus (BASP1-AS1), indicating limited convergence between transcriptional and editing dysregulation.

Among the differentially edited sites, we identified seven well-characterized RNA recoding events where A-to-I editing results in nonsynonymous amino acid changes that were consistently over-edited in T21 and are known to play critical roles in early brain development (Fig. 5G, Fig. S13). Several of these sites reached statistical significance in both brain regions, while others were significant in one region and showed consistent trends in the other. For example, two recoding sites within GRIK2 (p.Q621R and p.Y571C) were significantly over-edited in both the PFC (FDR p=0.001, FDR p=0.01, respectively) and hippocampus (FDR p=0.05, FDR p=0.02, respectively). Over-editing was also observed at GRIA2 (p.R764G) and GRIA3 (p.R775G) in the PFC (FDR p=0.0009, FDR p=0.15, respectively) and hippocampus (FDR p=0.07, FDR p=0.01, respectively). Additional recoding sites showing over-editing included GABRA3 (p.I342M) and CYFIP2 (p.K294E) in the PFC (FDR p=0.01 and 0.02) and hippocampus (FDR p=0.02 and 0.15), and COG3 (p.I635V) in the hippocampus (FDR p=0.03) (Fig. S13). Editing levels at these loci were strongly correlated with ADARB1 expression (Fig. 5H), consistent with its known regulatory role in mediating editing at recoding sites.

To further define the molecular identity of T21-associated editing changes, we integrated reference A-to-I editing maps from purified human brain cell types23. This analysis revealed that most over-edited sites in T21, particularly those within protein-coding regions, are strongly enriched in neurons, with minimal editing in glial populations (Fig. S14). This neuron-specific pattern argues against a secondary inflammatory or immune origin. Moreover, cell type deconvolution of bulk RNA-seq showed no significant differences in neuronal composition between T21 and control samples (Fig. S1), further excluding cell proportion as a confounding factor. Many of these neuron-enriched sites, especially recoding events, were also found to be highly edited in fresh brain tissue but markedly reduced in postmortem samples (Fig. S14), underscoring their dynamic regulation and supporting a functional role in neuronal plasticity rather than passive degradation artifacts.

These recoding sites undergo tightly regulated, site-specific changes during typical brain maturation, with editing levels gradually increasing and peaking postnatally, as shown using BrainVar typical cortical developmental data29 (Fig. 5I). To contextualize editing levels in T21, we overlaid our fetal brain data onto these normative trajectories, focusing on BrainVar samples from fetal Periods 5 and 6, which correspond to our gestational window. Across all six selected recoding sites, T21 samples consistently exhibited elevated editing relative to both age-matched controls and the BrainVar developmental distribution (Fig. 5J). To quantify these shifts relative to typical brain development, we normalized editing levels using z-scores calculated against the BrainVar reference dataset. Specifically, for each site and sample, we computed z-scores using the mean and standard deviation of editing levels from neurotypical BrainVar samples during developmental Periods 5 and 6 (see “Methods”). This normalization anchored our T21 and control measurements to a well-characterized mid-gestational baseline. Across all six sites, T21 samples exhibited consistently positive z-scores, indicating premature or excessive editing relative to normative expectations (Fig. 5K). In contrast, control samples were centered near the BrainVar developmental mean (Fig. 5K). These results suggest that T21 is associated with editing at key neurodevelopmental loci, potentially reflecting premature post-transcriptional maturation and altered proteomic remodeling during fetal brain development.

Meta-analysis of A-to-I editing across diverse tissues confirms over-editing in T21
Although A-to-I editing is known to vary across cell types, tissues, and developmental stages, we sought to validate our primary findings by performing a cross-study meta-analysis encompassing diverse cellular contexts. Using a harmonized editing quantification pipeline and consistent site detection criteria across datasets, we identified 68 high-confidence A-to-I editing sites under strict FDR<5% and an additional 69 A-to-I sites under FDR<10% that were over-edited in T21 compared to controls (Fig. 6A). These sites were selected from a transcriptome-wide screen and detected consistently across multiple studies. Totally, ~51% of these sites (71 sites) localized to 3′ untranslated regions (3′ UTRs) (Fig. 6B), highlighting a potentially conserved role for post-transcriptional dysregulation in T21. Using a harmonized editing quantification pipeline and consistent site detection criteria across datasets, we identified 68 high-confidence A-to-I editing sites that were significantly over-edited in T21 compared to controls at a stringent false discovery rate (FDR<5%), along with an additional 69 sites reaching FDR<10% (Fig. 6A). To complement the high-confidence sites, we expanded the threshold to FDR<10% to cast a broader net and detect consistent, lower-magnitude editing changes that may still be biologically relevant for T21. While more exploratory, these sites were retained only if consistently detected across datasets and offer candidates for future validation (see “Methods”). Notably, ~51% (71 sites) localized to 3′ untranslated regions (3′ UTRs) (Fig. 6B), underscoring a potential conserved mechanism of post-transcriptional dysregulation in T21.

We further modeled the impact of editing on miRNA–mRNA interactions using thermodynamic predictions (see “Methods”). Edited 3′ UTRs exhibited significantly lower miRNA binding affinity than their unedited counterparts (p=2.3×10−³², Mann–Whitney U test) (Fig. 6B), suggesting that over-editing in T21 may disrupt canonical miRNA regulation during critical developmental windows. While these findings are based on computational predictions, they support a mechanistic link between A-to-I editing and post-transcriptional gene silencing in the context of T21.

To further evaluate the developmental relevance of T21 over-edited sites, we examined their editing trajectories across typical cortical development. All sites exhibited increasing editing from the prenatal to postnatal period, with mean levels rising from ~35% to ~50% (Cohen’s d=3.67, Fig. 6C), suggesting tight temporal regulation during maturation. Among the recoding sites elevated in T21, GRIA3 (p.R775G) emerged as the most consistently over-edited locus across both fetal brain and multiple in vitro models (Fig. 6D). This site, which regulates AMPA receptor desensitization kinetics, showed a statistically robust ~8% average increase in editing in T21 (RE model: p=0.0011), with most datasets demonstrating concordant over-editing. Our findings suggest that in T21, premature attainment of these editing thresholds may disrupt synaptic refinement and shift excitatory–inhibitory (E/I) balance during critical periods of circuit formation. Moreover, the reproducibility of this editing phenotype across diverse cell types, from iPSCs and NPCs to fibroblasts and postmortem brain, underscores its biological relevance and supports a model in which ADARB1-driven editing at functionally constrained sites contributes to altered excitatory signaling in T21.

Peripheral blood RNA-seq reveals immune-linked ADAR activation in T21
To contextualize CNS-specific RNA editing dysregulation in T21, we analyzed matched bulk RNA-seq data from peripheral whole blood samples (n=304 T21, n=96 controls)20. ADARB1 expression was significantly elevated in T21 blood (FDR p=3.14×10−⁸), consistent with gene dosage effects, whereas ADAR and ADARB2 expression were unaltered by karyotype (Fig. S15A). However, in contrast to the brain, global editing activity (AEI) was not elevated across all T21 samples (Fig. S15B). To evaluate immune-related editing effects, we stratified samples by transcriptional subtype (MS1–MS3), revealing that only MS3 individuals (characterized by high innate immune activation) exhibited increased AEI and elevated ADAR expression (Fig. S15C, D). Notably, ADARB1 expression tracked with karyotype but not immune tone and did not correlate with AEI (r=−0.0514, p=0.24), whereas ADAR strongly correlated with AEI (r=0.599, p=1.07×10−⁵³) (Fig. S15E). These findings highlight that while ADARB1 is dosage-sensitive in both tissues, its role in RNA editing is CNS-specific, whereas ADAR mediates immune-linked editing in peripheral blood. This contrast underscores tissue-specific regulatory mechanisms and supports a dual model in which gene dosage and immune signaling independently shape A-to-I editing dynamics across biological systems.

Methods
Postmortem tissue acquisition and DNA testing
All tissue samples were collected at the University Hospital of Obstetrics and Gynecology, Sofia, in collaboration between the Lieber Institute for Brain Development (LIBD) and the Medical University of Sofia, Bulgaria. The postmortem brain tissues were collected after written informed consent was obtained from the mothers and stored fresh frozen at the biobank of the Molecular Medicine Center, Medical University of Sofia, before transport to LIBD for further processing. Altogether, fetal brain tissues from 20 T21 cases (14 male, 6 female) and 27 euploid controls (17 male, 10 female) between 13–22 gestational weeks were used in the study. All 20 T21 cases analyzed in this study were confirmed as full trisomy 21 by clinical karyotyping reports obtained from the source biobank. No partial or mosaic cases were included. The fetal material was collected after termination of pregnancy for medical reasons (severe congenital anomalies or genetic disorder). Invasive prenatal aneuploidy testing using Aneufast™ QF-PCR Kit (Molgentix Sl) was performed at the University Hospital of Obstetrics and Gynecology, and microarray testing of DNA from the abortive tissue was done using SurePrint G3 Human CGH Microarray Kit, 4×180K (Agilent Technologies Inc.) at the Molecular Medicine Center.

All research involving human fetal tissue was conducted in accordance with institutional and national ethical guidelines and regulations. Written informed consent was obtained from all donors prior to tissue collection. No embryos or gametes were created or used for research purposes. The research project “Genetic and epigenetic research of human fetal brain development” was reviewed and approved by the Institutional Ethics Review Board of the Medical University of Sofia (Protocol No. 2981/06.07.2012).

Portions of the fetal brain tissue samples were consumed during RNA extraction and downstream molecular analyses. Any remaining unused tissue material is conserved at the biobank of the Molecular Medicine Center, Medical University of Sofia. Due to ethical and regulatory constraints associated with human fetal tissue, remaining biological material is not broadly distributable; however, access requests may be considered on a case-by-case basis, subject to local ethical approval and material transfer agreements. Inquiries regarding potential access to remaining tissue material should be directed to the Molecular Medicine Center, Medical University of Sofia, in coordination with the Lieber Institute for Brain Development.

RNA-sequencing data generation
We assembled a discovery cohort consisting of paired prefrontal cortex (PFC) and hippocampal formation tissue samples (referred to as “hippocampus” throughout this body of work) from 20 individuals with trisomy 21 (T21) and 27 euploid controls, collected between 13 and 22 post-conception weeks (PCW), a critical period for human brain development. At these early developmental stages, the hippocampal formation represents the broader precursor region giving rise to the mature hippocampus. Donor metadata are detailed in Supplemental Data 1. The cohort included 16 females and 31 males, with a mean gestational age of 18.4±3.2 weeks. All samples were from individuals of Caucasian ancestry with no reported major brain malformations. Total RNA was extracted from frozen tissue using the AllPrep DNA/RNA Mini Kit (Qiagen, Cat No. 80204). RNA quality was assessed using the Agilent 2100 Bioanalyzer; all included samples had RNA Integrity Numbers (RIN) ≥9.0 (9.8±0.3). RNA-seq libraries were prepared from 300ng of total RNA using the TruSeq Stranded Total RNA Library Prep Kit with Ribo-Zero Gold (Illumina), which depletes both cytoplasmic and mitochondrial ribosomal RNA. Libraries were sequenced on an Illumina HiSeq 3000 platform at the Promega sequencing core, generating 200-bp paired-end reads at an average depth of ~34.5 million reads per sample.

RNA-sequencing data processing and quality control
Sequencing reads were processed using the RAPiD-nf pipeline (https://github.com/CommonMindConsortium/RAPiD-nf). Reads were aligned to the GRCh38 human reference genome (Ensembl release 95) using STAR v2.7.3a48. Alignment quality was assessed using RSeQC and Picard tools, including metrics for GC content, duplication rates, insert size, gene body coverage, and library complexity. PCR duplicates were marked but retained during quantification. Gene-level quantification was performed using featureCounts49 (Subread v2.0.1) based on GENCODE v32 annotations. Genes with non-zero counts in at least one-third of all samples were retained. Counts were normalized using the VOOM transformation from the limma package (v3.48.3)50 to form region-specific expression matrices (PFC and hippocampus). A total of 16,472 genes were analyzed in the PFC, and 17,035 genes were analyzed in the hippocampus. Principal component analysis (PCA) did not identify any expression outliers using a 95% confidence interval across the top three PCs.

Cellular deconvolution of bulk tissues
Cell-type composition was estimated using non-negative least squares (NNLS) implemented in the est_frac function of the bMINDR package (v1.2.0)51. This approach applies Bayesian inference to estimate cell-type–specific expression profiles while constraining coefficients to non-negative fractions. Prior information for the model was derived from a reference single-cell expression signature matrix from Pei et al.52. This matrix includes transcriptional profiles for 11 fetal brain cell types: astrocytes, microglia, oligodendrocytes, oligodendrocyte precursor cells (OPCs), endothelial cells, pericytes, intermediate progenitor cells (IPCs), neuroepithelial cells (NEPs), and three neuronal subtypes (quiescent, replicating, transitional). Log2-transformed counts per million (CPM) were used as input53. Deconvolution results were used to adjust for cellular heterogeneity in downstream modeling. This approach enabled us to assess and adjust for potential shifts in cell type composition while minimizing confounding due to cellular heterogeneity, consistent with prior studies of fetal brain transcriptomics.

Partitioning gene expression variance
We used the variancePartition R package54 to fit linear mixed models quantifying the proportion of expression variance attributable to technical and biological factors. Covariates included neuronal proportion (quiescent + replicating + transitional neurons), % mRNA reads, RIN, gestational age, sex, library depth, and genetic diagnosis. Donor identity was modeled as a random effect. These estimates informed covariate selection for differential expression and network modeling.

Differential gene expression analysis
Differential expression was performed using the limma-voom pipeline53. An initial analysis compared PFC versus hippocampus, adjusting for diagnosis, percentage of mRNA reads, gestational age, sex, and RIN, with donor modeled via duplicateCorrelation. T21-specific effects were then assessed separately for each region using region-specific VOOM-normalized matrices (16,471 genes in PFC; 17,035 in hippocampus). Models included covariates for neuronal proportion, percentage of mRNA reads, age, sex, and RIN. DEGs were defined as those with Benjamini-Hochberg FDR<0.05.

Weighted Gene Co-expression Network Analysis (WGCNA)
To identify coordinated transcriptional programs disrupted in T21, we performed weighted gene co-expression network analysis (WGCNA)55 separately for the PFC and hippocampus using VOOM-normalized expression matrices. A total of 16,471 genes (PFC) and 17,035 genes (hippocampus) were retained. Signed co-expression networks were constructed by computing absolute Pearson correlation coefficients between all gene pairs, followed by soft-thresholding to approximate scale-free topology (target R²>0.80). Power parameters (β) of 5 and 9 were selected for the PFC and hippocampus, respectively, based on scale-free fit and mean connectivity criteria. Networks were clustered using the dynamic tree cut algorithm with a minimum module size of 50 and a cut height of 0.999. Modules were summarized by their eigengenes (first principal component of each module’s expression matrix) and tested for association with T21 diagnosis using linear models in limma, adjusting for the same covariates as in the differential gene expression analyses (neuronal proportion, % mRNA, age, sex, and RIN). Module significance was assessed via moderated t-tests. Qualitative inspection of module eigengene expression across diagnostic groups was used to confirm biologically interpretable patterns. These analyses enabled the identification of gene networks associated with T21 that may not be captured through single-gene differential expression alone.

Gene Ontology and enrichment analysis
To interpret the functional significance of differentially expressed genes, we performed gene set enrichment analysis using the CAMERA method56, a correlation-adjusted competitive test that accounts for inter-gene correlation within gene sets. Differential expression t-statistics were input from the limma-based comparisons between T21 and control samples for each brain region. CAMERA ranks genes based on differential expression and tests whether predefined gene sets are overrepresented at the extremes of this ranked list relative to random expectation. Gene sets were derived from Gene Ontology (GO) biological processes, molecular functions, and MSigDB hallmark gene sets. Variance inflation due to inter-gene correlation was corrected using the default inter-gene correlation parameter (set to 0.01), and resulting enrichment p-values were adjusted for multiple testing using the Benjamini-Hochberg method. Enrichment results were used to identify biological processes and pathways disproportionately affected in T21 fetal brain tissues.

Permutation-based gene set preservation in T21
We assessed whether co-regulatory structures within predefined gene sets were disrupted in T21 using the Zsummary preservation statistic from WGCNA57, computed with 200 permutations. This statistic integrates measures of intramodular density and connectivity across conditions. Zsummary scores <2 indicate no preservation, 2–10 suggest weak to moderate preservation, and >10 reflect strong preservation. Because unsupervised gene co-expression network structure appeared preserved between T21 and controls, we focused on smaller curated gene sets to detect more targeted disruptions, including GO biological processes, GO molecular functions, and MSigDB hallmark gene sets. To correct for the known dependency of preservation scores on module size, we modeled Zsummary of a random gene set matched for gene set size, as a comparative for each tested gene set, as a function of module size and derived an expected preservation score (Zrandom_predicted) for each gene set. We then computed an adjusted rank score incorporating (1) the observed Zsummary, (2) its FDR-adjusted p-value, and (3) the deviation of random module preservation from expectation. Gene sets meeting strict criteria (Zsummary<2, p.adj> 0.1, Zsummary ≤ Zrandom_predicted− 3, and p.random.adj <0.05) were classified as non-preserved. These modules exhibited significantly lower preservation than size-matched random sets (Wilcoxon p<0.05, PFC and hippocampus), supporting loss of co-regulatory structure in biological pathways.

Cross-Study Meta-Analysis of RNA-Seq Datasets
To validate and generalize transcriptomic findings across diverse developmental contexts, we performed a harmonized meta-analysis of our data with nine additional publicly available RNA-seq datasets profiling T21 and euploid control samples (Table 1). These datasets spanned a range of early gestational tissues and in vitro cell types, including induced pluripotent stem cells (iPSCs), neural progenitor cells (NPCs), cortical neurons, fetal fibroblasts and immune cells. In total, the meta-analysis encompassed 108 T21 and 117 control samples across the following GEO accession numbers: GSE52249, GSE185192, PRJNA751601, GSE101942, GSE144857, GSE55504, GSE79842, GSE84531, and GSE128621. For each dataset, raw FASTQ files were reprocessed through a standardized RNA-seq pipeline (RAPiD-nf; https://github.com/CommonMindConsortium/RAPiD-nf) to ensure uniformity in alignment, quantification, and normalization. Reads were aligned to the human genome (hg38), and gene-level expression was quantified using featureCounts49. Differential gene expression within each dataset was computed using limma with voom transformation44, adjusting for relevant covariates such as age, sex, batch, and neuronal fraction where available.

For each gene, log₂ fold-change and standard error estimates were extracted from limma-voom analyses of each dataset. Genes with low expression (not expressed in at least one-third of samples per dataset) were excluded. Meta-analysis was performed using a random-effects model implemented via the rma.uni() function in the metafor R package (REML estimator) was used to account for between-study heterogeneity58. Only genes expressed in ≥4 datasets and with consistent directionality in at least 70% of datasets were retained in the final result set. Genes with Benjamini-Hochberg adjusted p-values< 0.05 were considered significantly dysregulated. Meta-analysis summary statistics, including forest plots for key dosage-sensitive genes, are publicly available (https://andyyang.shinyapps.io/Ts21-dashboard/). This approach enabled robust quantification of consistent transcriptional changes across diverse developmental contexts and experimental platforms.

T21 microarray data reprocessing and differential expression analysis
To evaluate the comparability of our RNA-seq findings with prior transcriptomic data, we processed and analyzed a publicly available fetal brain microarray dataset profiling Trisomy 21 (T21) and control samples (GEO accession: GSE59630)3. This dataset was generated using the Affymetrix Human Exon 1.0 ST Array platform and includes 58 paired tissue samples from 15 T21 and 15 euploid control brains, ranging in age from 14 post-conception weeks (PCW) to 42 years old. For consistency with our fetal RNA-seq dataset, we focused our analysis on the cerebral cortex and hippocampus, corresponding to the neocortical (CBC) and hippocampal (HIP) samples in the original study.

Raw .CEL files were downloaded and imported using the oligo package in R59. Robust Multi-array Average (RMA) normalization60 was applied to compute expression values at the exon level, followed by log₂ transformation. Metadata for all samples was manually curated and aligned to expression profiles, with key covariates including sex, post-mortem interval (PMI), RNA integrity number (RIN), ethnicity, anatomical region, and developmental stage (converted to numeric age in years, accounting for fetal and postnatal time points). Genes were filtered to retain those expressed above the sample-wise median in at least one-third of samples, resulting in a matrix of robustly expressed transcripts. Gene expression variance was decomposed using the variancePartition R package54, employing a linear mixed model that included fixed effects for age, RIN, PMI, and developmental period, and random intercepts for sex, ethnicity, and diagnosis. Differential expression was assessed using the limma framework. A design matrix was constructed to model expression as a function of T21 diagnosis, RIN, PMI, age (in years), and developmental period. Duplicate samples per subject were accounted for by controlling for correlated measurements. A contrast was specified to test for differential expression between DS and control samples. Empirical Bayes moderation was applied to stabilize variance estimates. Significance was defined using a Benjamini-Hochberg false discovery rate (FDR) threshold of <1%.

Fold-changes were directionally aligned to match RNA-seq comparisons, with log₂ fold-changes sign-inverted such that positive values indicate higher expression in T21. Differentially expressed genes (DEGs) were classified as upregulated in T21, upregulated in controls, or non-significant. The resulting gene-level summary statistics were exported for comparison with RNA-seq–derived DEG lists.

RNA editing site detection and annotation
To quantify RNA editing across fetal brain samples, we employed a supervised approach that focused on known A-to-I editing sites. Specifically, we queried nucleotide positions curated from high-confidence databases: (i) REDIportal61, (ii) previously annotated sites from human brain cell types28, and (iii) a comprehensive list of evolutionarily conserved A-to-I recoding sites62. Using samtools mpileup, we extracted reference and edited read counts for these known sites directly from STAR-aligned BAM files, following our previously validated pipelines16,28,29,34. We then applied stringent quality control criteria to ensure the inclusion of bona fide A-to-I editing events. Supervised sites with <5 total reads or <3 edited reads were excluded. The resulting set of high-confidence editing sites was functionally annotated using ANNOVAR63, assigning gene symbols based on RefGene, repetitive elements based on RepeatMasker v4.1.164, and known editing annotations via the latest REDIportal database. Evolutionary conservation scores were incorporated using phastCons scores from the PHAST package65 to highlight conserved editing events across mammals.

Alu Editing Index (AEI)
To measure global RNA editing activity, we calculated the Alu Editing Index (AEI)66, a quantitative metric reflecting A-to-I editing levels in repetitive SINE/Alu elements, where the majority of non-coding editing occurs. The AEI was computed using AEI v1.0, which takes STAR-aligned BAM files as input. Specifically, the AEI is calculated as the percentage of A-to-G mismatches relative to total adenosine coverage within annotated Alu elements (from UCSC RepeatMasker annotations, hg38). Known polymorphic sites were masked using coordinates from the UCSC Common Genomic SNPs 150 track to reduce false positives due to DNA variation. The AEI method preserves signal even in low-coverage regions and has demonstrated robustness across hundreds of postmortem human brain RNA-seq samples generated with varied protocols. AEI values were multiplied by 100 to report editing levels as percentages. This metric served as a transcriptome-wide surrogate for ADAR-mediated editing activity and was used to assess global dysregulation in T21 across brain regions and external replication datasets.

Quantifying and normalizing RNA recoding dynamics in typical and trisomy 21 fetal brain
To assess RNA recoding dynamics during fetal brain development and in trisomy 21 (T21), we analyzed A-to-I editing levels at canonical recoding sites across two datasets: (1) neurotypical mid-fetal cortical samples (Periods 5 and 6) from the BrainVar project chromosome 21, and (2) fetal PFC samples from individuals with T21 and matched controls (this study). Editing levels were computed as the ratio of guanosine (G) reads to total adenosine + guanosine (A+G) reads at each site, based on samtools pileups. To contextualize editing disruptions in T21, we visualized recoding site editing levels in T21 and control samples against the backdrop of typical developmental trajectories. Specifically, for each recoding site, we generated boxplots using BrainVar Period 5–6 PFC samples (n=67) and overlaid individual T21 (n=20) and euploid control (n=27) fetal brain samples as points. This approach allowed for direct visual comparison of editing levels relative to normative developmental distributions.

To formally quantify deviation from typical editing patterns, we computed z-scores for each recoding site and individual sample. Z-scores were calculated using the mean (μ) and standard deviation (σ) of editing levels in typical brain development (TBD) BrainVar samples (Periods 5–6, PFC only), according to:

Z = (Editing_sample - μ_TBD) / σ_TBD

This yielded a normalized deviation score for each site per sample, allowing quantitative comparison across groups. Z-scores were visualized separately by diagnosis (T21 vs. control) and brain region. Group differences in z-scores were assessed using two-sided Wilcoxon rank-sum tests. To account for multiple comparisons across recoding sites, p-values were adjusted using the Benjamini-Hochberg procedure (FDR<0.05 threshold). This approach enabled the identification of recoding events where editing in T21 significantly deviated from neurotypical developmental trajectories.

RNA editing meta-analysis across developmental datasets
For each editing site, the mean difference in editing rate (T21–control) and corresponding standard error were computed from each dataset using linear models adjusted for relevant covariates when provided in the corresponding metadata files (e.g., age, batch, and RNA quality). Sites with low coverage (<10 total reads in at least one-third of samples) were excluded to ensure reliability. Editing site meta-analysis was performed using a random-effects model implemented via the rma.uni() function in the metafor R package, using the restricted maximum likelihood (REML) estimator to account for between-study heterogeneity. Only sites detected in ≥4 datasets and exhibiting consistent directionality of effect (same sign in at least 70% of datasets) were retained. Sites with Benjamini-Hochberg adjusted p-values< 0.05 were considered significantly differentially edited. This analysis identified high-confidence A-to-I sites consistently over-edited in T21 across fetal brain and in vitro models.

miRNA–mRNA interactions
miRNA-mRNA interactions were studied using miRANDA67 as previously described29,68. In brief, to assess the impact of RNA editing on miRNA binding, we compared the minimum free energy (ΔG) of miRNA-3′ UTR interactions at edited versus unedited sites. Next, we extracted matched 101bp sequences surrounding each editing site (with either A or G), aligned them to all mature miRNAs using miRANDA, and calculated ΔG for each interaction, focusing especially on seed region matches using strict parameters. Statistical differences in ΔG were evaluated using the Mann–Whitney U test.

Secondary T21 peripheral blood RNA-sequencing data analysis
To examine ADAR expression and A-to-I RNA editing profiles in peripheral blood, we analyzed bulk RNA-seq data from 304 individuals with T21 and 96 euploid controls obtained from GEO (GSE190125)20. Raw FASTQ files were processed using the same standardized pipeline as for the fetal brain and in vitro datasets to ensure analytic consistency. Reads were aligned to the GRCh38 reference genome using STAR48, and gene-level expression quantification was performed with featureCounts49. ADAR, ADARB1, and ADARB2 expression levels were extracted from VOOM normalized44 values for downstream analysis. The AEI66 was calculated for each sample as a transcriptome-wide proxy for global editing activity. To investigate subgroup-specific effects, we stratified individuals by previously defined transcriptional subtypes from the original study (e.g., MS1, MS2, and MS3). Differences in gene expression between T21 and controls, and among molecular subgroups, were performed using limma. All analyses were conducted in R (v4.2.2).

Representative brain diagrams
Brain diagrams found in Fig. 1 and Fig. 2A, B were constructed in the cerebroVizR package (v1)69.

Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability
The RNA-sequencing data generated in this study have been deposited in the NCBI Gene Expression Omnibus (GEO) database under accession code GSE301886. These data are publicly available without restriction. The GEO accession includes raw sequencing data (FASTQ files), processed gene-level expression matrices, RNA editing summaries, and associated sample metadata required to reproduce all analyses reported in this study. RNA-sequencing datasets used for cross-study gene expression and RNA editing meta-analyses are also publicly available from GEO or the European Nucleotide Archive under accession codes GSE52249, GSE185192, PRJNA751601, GSE101942, GSE144857, GSE55504, GSE79842, GSE84531, GSE128621, and GSE190125. All source data underlying the main and Supplementary Figs., including differential expression statistics, RNA editing results, and meta-analysis summary statistics, are provided in the Supplementary Data files and Source Data accompanying this paper. To enable gene-by-gene inspection of between-study effect sizes and heterogeneity, we developed an interactive RShiny application (https://andyyang.shinyapps.io/Ts21-dashboard/) that generates meta-analytic forest plots for any gene of interest across the harmonized datasets. For each gene, the application displays study-specific log fold changes, standard errors, standardized mean differences, confidence intervals, and random-effects model estimates, as well as expression boxplots and developmental expression trajectories. This RShiny application also hosts all processed gene-level expression matrices, associated sample metadata, and summary files generated in the current study. Source data are provided with this paper.

Code availability
Computational code is provided on GitHub (https://github.com/BreenMS/Trisomy21) and includes R code for: RNA-sequencing quality control, short-read mapping, counting, and normalization, differential gene expression, cell type estimation, A-to-I editing quantification, differential A-to-I editing comparisons, and meta-analyses. Contact the lead contact (M.S.B) for any questions.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript, Breen et al investigated gene expression pattern in fetal dorsolateral prefrontal cortex (DLPFC) and\nhippocampal tissues of the individuals with Trisomy 21 (T21) using total RNA sequencing and found that many genes\nlocated on chromosome 21 were overexpressed. This finding was confirmed by using their original data obtained from 20\nT21 cases and 27 controls, and also by meta-analysis using the deposited data. Among overexpressed genes with high\nconfidence, they focused on ADARB1 gene, encoding ADAR2, an RNA editing enzyme, given that its role in the\nneuropathology of T21 remains uncertain. Then, the authors found that some of RNA editing sites in 3’UTRs and exons,\nwhich are preferentially edited by ADAR2, are indeed overedited, which might contribute to the development of\nneuropathology in T21. This manuscript is of high value in that it has confirmed that many genes on chromosome 21 (most of\nthem are highly expressed genes), including ADARB1 gene, are overexpressed in the individuals of T21, but the\nsignificance of overediting in the pathology of T21 remains unclear, given that in vivo data to support this idea is lacking.\nSpecific comments are shown below.\nMajor points\n1. It is uncertain whether partial T21 cases are involved in 20 T21 cases examined in this study. The authors should clarify\nthis point and show how it affects the results, such as the data shown in Figure 2.\n2. Page 12, lines 26-29: It is hard to understand these sentences. It seems that Ref 38 and Ref 39 investigated GRIA2 but\nnot GRIA3. As far as the reviewer knows, there has been no study to elucidate in vivo significance of GRIA3 R/G site. In\naddition, to date, in vivo studies about how over-editing affects the biological development and growth, which is essential to\nreinforce the significance of this study, are not sufficient. The phenotypes of Adar2 KO mice are mostly caused by lack of\nRNA editing at GRIA2 Q/R site and the contribution of other sites is limited (PMID 21467037, 30087110, 11182093). Several\nstudies show that RNA editing is increased under epileptic situation, including GRIA3 R/G site, whereas its significance\nremains undermined (PMID 15006707, 21589914). ADAR2 overexpression in rodents induces hyperphagia but it is\nunknown whether the phenotypes related to T21 are observed in these lines (PMID 17567573, 23323881). As far as the\nreviewer knows, there has been several studies that show over-editing at certain sites deteriorates the development and\ngrowth (PMID 19036977, 20394819, 27851964), although this study did not investigate RNA editing sites in CAPS1 or 5-\nHT2CR. Thus, the significance of overediting in the pathology of T21 remains unclear.\nMinor points\n1. Page 5, lines 21-23: The authors should indicate the dots of IFNGR2, IFNAR1, and IFNAR2 genes in Figures 2A and 2B.\n2. Page 6, lines 5-3: “mitochondrial translation” and “ECM organization” are found only in hippocampus and “oxidative\nphosphorylation” is not found in Figure 3A.\n3. Page 6, line 36: biological systems. particularly those > biological systems, particularly those\n4. Figure 3: The authors should explain what is “CAMERA”. In addition, the legend mentions that “The y-axis lists the top five\nmost significantly enriched gene sets”, whereas where the top five genes are listed?\n5. Figure 4: The genes in red characters are not overlapped between Figure 4A and Figure 4C, and it is difficult to know the\ncriteria for selecting these gene names.\n6. Figure S1: The authors should explain what is “MIND algorithm”.\n7. Figure S2: The authors should explain what are “VOOM-normalized” and “LOESS regression” in Figure S2A and Figure\nS2E, respectively.", "reviewer_3": "This manuscript investigates how trisomy 21 (T21) perturbs transcriptomic regulation during human fetal brain development,\nwith a focus on RNA editing. Using bulk RNA-seq from fetal cortex and hippocampus, complemented by harmonized meta-\nanalysis of nine external datasets, the authors show robust dosage-sensitive overexpression of ADARB1 and an associated\nincrease in A-to-I RNA editing, including premature over-editing of canonical recoding sites in glutamate receptor genes.\nThey also report widespread 3′UTR editing changes and propose a “dual model” where ADARB1 dosage predominates in\nthe CNS while ADAR1 is more influential in immune tissues. The work highlights RNA editing as a novel post-\ntranscriptional mechanism potentially contributing to Down syndrome neuropathology.\nThe fetal brain cohort and integration with external datasets are important strengths. However, several technical and\ninterpretive issues need to be addressed before the conclusions are fully convincing.\nThe authors harmonized nine datasets through a common pipeline, which is a strength. But between-study heterogeneity is\nnot deeply reported. More transparency here would increase confidence.\nIt is not entirely clear whether neuronal proportion estimates were explicitly included as covariates in the differential editing\nanalysis, as they were for differential expression. Editing is highly cell-type specific, and even subtle differences in neuronal\nsubtypes or glial fractions could confound results. The authors should clarify and, if not done, repeat analyses including\nneuronal proportion as a covariate.\nThe finding that 3′UTR sites are preferentially over-edited is interesting, but the functional interpretation is limited to\npredicted effects on miRNA binding affinity. This remains speculative. Can the authors assess whether these sites correlate\nwith mRNA stability or expression levels of host genes in their dataset? Even a correlation analysis between editing level\nand RNA abundance would help address whether editing has measurable regulatory consequences.\nADARB2, although not catalytically active, is thought to act as a dominant-negative regulator of editing. In Figure 5,\nADARB2 appears to correlate positively with AEI, Could the authors expand on this observation, or at least discuss the\npotential role of ADARB2 in their system?\nWhile several recoding sites (e.g., GRIA2, GRIA3, GRIK2) are highlighted, the effect sizes reported are modest (~5–10%).\nPrior literature suggests small changes can matter, but without functional assays, the biological impact here is uncertain.\nThe authors should temper causal language (“drives,” “accelerates”) and more explicitly acknowledge that the evidence is\ncorrelative.\nMinor:\nThe blood RNA-seq analysis, while interesting, feels somewhat detached from the CNS focus. The authors may wish to\nstreamline or frame this more clearly as a complementary observation.\nSome redundancy exists in the Results and Discussion, especially regarding repeated statements of ADARB1\noverexpression. Streamlining would improve clarity.\nA few typographical and editorial oversights were noted (e.g., repeated sentences in page 10, paragraph 1). Careful editing\nwould strengthen presentation."}, "author_response": "We thank the reviewers for their careful evaluation and constructive feedback. The revisions substantially\nstrengthen the manuscript’s clarity and mechanistic interpretation. Below we provide detailed responses to each\ncomment and indicate how all concerns were addressed in the revised manuscript. All figure, text, and method\nupdates have been incorporated accordingly.\nReviewer #1\nIn this manuscript, Breen et al investigated gene expression pattern in fetal dorsolateral prefrontal cortex (DLPFC)\nand hippocampal tissues of the individuals with Trisomy 21 (T21) using total RNA sequencing and found that\nmany genes located on chromosome 21 were overexpressed. This finding was confirmed by using their original\ndata obtained from 20 T21 cases and 27 controls, and also by meta-analysis using the deposited data. Among\noverexpressed genes with high confidence, they focused on ADARB1 gene, encoding ADAR2, an RNA editing\nenzyme, given that its role in the neuropathology of T21 remains uncertain. Then, the authors found that some of\nRNA editing sites in 3’UTRs and exons, which are preferentially edited by ADAR2, are indeed overedited, which\nmight contribute to the development of neuropathology in T21. This manuscript is of high value in that it has\nconfirmed that many genes on chromosome 21 (most of them are highly expressed genes), including ADARB1\ngene, are overexpressed in the individuals of T21, but the significance of overediting in the pathology of T21\nremains unclear, given that in vivo data to support this idea is lacking. Specific comments are shown below.\nMajor Points\n1. It is uncertain whether partial T21 cases are involved in 20 T21 cases examined in this study. The authors\nshould clarify this point and show how it affects the results, such as the data shown in Figure 2.\nRESPONSE: All 20 T21 cases analyzed in this study were confirmed as full trisomy 21 by clinical karyotyping\nreports obtained from the source biobank. No partial or mosaic cases were included. This clarification has been\nadded to the Methods section (page 14). As a result, all differential expression and editing and expression effects reflect full T21\ndosage and are not confounded by mosaicism or segmental duplication.\n2. Page 12, lines 26-29: It is hard to understand these sentences. It seems that Ref 38 and Ref 39 investigated\nGRIA2 but not GRIA3. As far as the reviewer knows, there has been no study to elucidate in vivo significance of\nGRIA3 R/G site. In addition, to date, in vivo studies about how over-editing affects the biological development\nand growth, which is essential to reinforce the significance of this study, are not sufficient. The phenotypes of\nAdar2 KO mice are mostly caused by lack of RNA editing at GRIA2 Q/R site and the contribution of other sites\nis limited (PMID 21467037, 30087110, 11182093). Several studies show that RNA editing is increased under\nepileptic situation, including GRIA3 R/G site, whereas its significance remains undermined (PMID 15006707,\n21589914). ADAR2 overexpression in rodents induces hyperphagia but it is unknown whether the phenotypes\nrelated to T21 are observed in these lines (PMID 17567573, 23323881). As far as the reviewer knows, there has\nbeen several studies that show over-editing at certain sites deteriorates the development and growth (PMID\n19036977, 20394819, 27851964), although this study did not investigate RNA editing sites in CAPS1 or 5-\nHT2CR. Thus, the significance of overediting in the pathology of T21 remains unclear.\nRESPONSE: We thank the reviewer for the insightful comment. We have substantially revised the relevant\nsection of the Discussion to clarify the distinction between GRIA2 and GRIA3 editing. As noted, prior in vivo\nstudies have firmly established that the severe phenotypes of Adar2 knockout mice are largely attributable to loss\nof GRIA2 Q/R site editing (PMIDs 21467037, 30087110, 11182093) whereas the contribution of other recoding\nsites, such as GRIA3 R/G, remains limited and less well understood. We now explicitly state that no study to date\nhas functionally validated the in vivo significance of GRIA3 R/G editing during development.\nWe also revised the text to clearly distinguish correlational from causal interpretations in our study. While our\ndata reveal premature over-editing of GRIA3 R/G in T21 fetal brain and in vitro models, we now clarify that this\nobservation reflects a strong association with ADARB1 overexpression rather than a demonstrated pathogenic\nmechanism. We further reference evidence that GRIA3 R/G editing is dynamically regulated under epileptic\nconditions (PMIDs 15006707, 21589914) though the physiological impact of this regulation remains to be\nelucidated. Lastly, we acknowledge that while ADAR2 overexpression in mice has been shown to produce\nphenotypes such as hyperphagia (PMIDs 17567573, 23323881) these phenotypes have not been linked to T21 or\nediting at the specific sites investigated in our study. We now include this nuance to ensure the implications of\nover-editing in T21 pathology are appropriately contextualized.\nThe heavily revised discussion (and limitation section) now appropriately tempers mechanistic claims,\nacknowledges relevant literature, and delineates the current limits of functional validation for the observed editing\nchanges.\nMinor Points\n1. Page 5, lines 21-23: The authors should indicate the dots of IFNGR2, IFNAR1, and IFNAR2 genes in Figures\n2A and 2B.\nRESPONSE: The positions of IFNGR2, IFNAR1, and IFNAR2 have been labeled in Figures 2A–B. These genes\nare now clearly highlighted to illustrate their dosage-sensitive overexpression.\n2. Page 6, lines 5-3: “mitochondrial translation” and “ECM organization” are found only in hippocampus and\n“oxidative phosphorylation” is not found in Figure 3A.\nRESPONSE: The text and figure have been harmonized to correctly reflect the enriched pathways.\n“Mitochondrial translation” and “ECM organization” are reported in Figure 3A because they are among the top\n5 enriched pathways. “Oxidative phosphorylation” was not one of the top enriched pathways and is not in Figure\n3A but is in Supplemental Table 4. To avoid any inconsistencies between the text and main figures we removed\ndiscussion of oxidative phosphorylation. Additionally, we clarify the results on page 6 emphasizing regional\nenrichment of ECM organization: “In contrast, over-expressed genes were enriched for sodium channel activity\nacross both regions and extracellular matrix (ECM) organization specifically in the hippocampus…”\n3. Page 6, line 36: biological systems. particularly those > biological systems, particularly those\nRESPONSE: Thank you. This sentence on page 6 (“biological systems. particularly those…”) has been corrected\nto “biological systems, particularly those…”.\n4. Figure 3: The authors should explain what is “CAMERA”. In addition, the legend mentions that “The y-axis\nlists the top five most significantly enriched gene sets”, whereas where the top five genes are listed?\nRESPONSE: We have added an expanded explanation in the Methods and within the legend of Figure 3 that\nCAMERA (Correlation Adjusted MEan RAnk) is a competitive gene set enrichment method that accounts for\ninter-gene correlation when testing whether predefined gene sets are enriched among differentially expressed\ngenes. The description of the y-axis for Figure 3A has been corrected to “top five significantly enriched gene\nsets”. We also now highlight the top 5 genes within each gene-set within Figure 3B.\n5. Figure 4: The genes in red characters are not overlapped between Figure 4A and Figure 4C, and it is difficult\nto know the criteria for selecting these gene names.\nRESPONSE: The genes in red labels in Figures 4A delineate the top 10 over-expressed genes in TS1 meta-\nanalysis. The genes in red labels for Figure 4C highlight ADARB1 together with 8 chr21 genes that have been\nconsistently linked to Trisomy 21 for context. The criteria for these distinctions are now explicitly stated in the\nlegend of Figure 4.\n6. Figure S1: The authors should explain what is “MIND algorithm”.\nRESPONSE: We have elaborated on the MIND method within Figure S1 legend and in the corresponding\nmethods section: “Cell-type composition was estimated using non-negative least squares (NNLS) implemented in\nthe est_frac function of the bMIND R package (v1.2.0) 47. This approach applies Bayesian inference to estimate\ncell-type–specific expression profiles while constraining coefficients to non-negative fractions. Prior information\nfor the model was derived from a reference single-cell expression signature matrix from Pei et al. (2021) 48.”\n7. Figure S2: The authors should explain what are “VOOM-normalized” and “LOESS regression” in Figure S2A\nand Figure S2E, respectively.\nRESPONSE: We now define both terms in the figure legend and Methods.\nRegarding panel A: “(A) Raw gene-level expression counts were first filtered to exclude lowly expressed genes\nand then normalized using the voom transformation from the limma package, which models the mean–variance\nrelationship in log-transformed count data and generates precision weights for linear modeling. These voom-\nnormalized values were used for Principal Component Analysis (PCA), visualized separately for prefrontal cortex\n(PFC, circles) and hippocampus (triangles) samples, with color indicating estimated neuronal fraction. PC1 and\nPC2 account for 29.3% and 17.8% of the total variance, respectively, and reveal clear regional separation, with\nneuronal content as a major driver of transcriptomic variation.”\nRegarding panel E: “ (E-F) LOESS regression lines with 95% confidence intervals were used for visualizing cell\ntype proportions over developmental trajectories because they do not assume a predefined parametric form and\ninstead flexibly fit local subsets of the data; especially valuable when cell type dynamics exhibit inflection points\nor vary across tissue types and conditions. Developmental trajectories in cell composition are broadly similar\nacross groups, though subtle deviations in T21 samples are observed in select cell types.”\nReviewer #3\nThis manuscript investigates how trisomy 21 (T21) perturbs transcriptomic regulation during human fetal brain\ndevelopment, with a focus on RNA editing. Using bulk RNA-seq from fetal cortex and hippocampus,\ncomplemented by harmonized meta-analysis of nine external datasets, the authors show robust dosage-sensitive\noverexpression of ADARB1 and an associated increase in A-to-I RNA editing, including premature over-editing\nof canonical recoding sites in glutamate receptor genes. They also report widespread 3′UTR editing changes and\npropose a “dual model” where ADARB1 dosage predominates in the CNS while ADAR1 is more influential in\nimmune tissues. The work highlights RNA editing as a novel post-transcriptional mechanism potentially\ncontributing to Down syndrome neuropathology.\n1. The authors harmonized nine datasets through a common pipeline, which is a strength. But between-study\nheterogeneity is not deeply reported. More transparency here would increase confidence.\nRESPONSE: We thank the reviewer for this helpful comment. To address it, we have now explicitly clarified in\nthe manuscript that our interactive RShiny App (available at [https://andyyang.shinyapps.io/Ts21-dashboard/])\nenables per-gene forest plot visualization of effect sizes across all studies, including individual log fold-change,\nstandard error, standardized mean difference, 95% confidence intervals, and random-effects model estimates and\np-values. This provides transparent access to between-study variability, strengthens confidence in meta-analysis\nresults, and enables users to identify genes with consistent or context-specific dysregulation across tissues, cell\ntypes, and developmental stages. This is all outlined in our revised Data Availability section.\nFurthermore, we have four examples in main Figure 4 D-E (ADARB1, DYRK1A, IFNAR1, and IFNAR2), which\nillustrates between-study variation across cortical neurons, iPSCs, NPCs, fibroblasts, and immune cell datasets,\nand reveals consistent upregulation in T21.\n2. It is not entirely clear whether neuronal proportion estimates were explicitly included as covariates in the\ndifferential editing analysis, as they were for differential expression. Editing is highly cell-type specific, and even\nsubtle differences in neuronal subtypes or glial fractions could confound results. The authors should clarify and,\nif not done, repeat analyses including neuronal proportion as a covariate.\nRESPONSE: Thank you, and yes, our differential editing analysis mirrors our differential expression analysis.\nIndeed, there is a strong neuronal effect with regards to editing levels. Both adjusted for the possible influence of\nestimated neuronal cell type proportions, RIN, gestational age, sex, and percentage of mRNA reads within each\nregion. This is now clarified in our methods and results sections.\n3. The finding that 3′UTR sites are preferentially over-edited is interesting, but the functional interpretation is\nlimited to predicted effects on miRNA binding affinity. This remains speculative. Can the authors assess whether\nthese sites correlate with mRNA stability or expression levels of host genes in their dataset? Even a correlation\nanalysis between editing level and RNA abundance would help address whether editing has measurable regulatory\nconsequences.\nRESPONSE: We appreciate the reviewer’s suggestion to test whether 3′UTR editing is associated with changes\nin RNA abundance. In response, we performed a systematic correlation analysis between RNA editing levels and\ngene expression across all matched samples in the PFC dataset (Figure S12). Consistent with prior studies\nsuggesting that A-to-I editing can destabilize transcripts by altering RNA secondary structure, miRNA\ninteractions, or translational efficiency, we observed a broadly negative relationship between editing and\nexpression levels. This effect was pronounced for 3′UTR sites, but extended across other genic regions as well,\nindicating a pervasive inverse coupling between editing activity and mRNA abundance. These results suggest\nthat 3′UTR editing is not merely predictive of altered miRNA affinity but is also correlated with measurable\nreductions in transcript levels, supporting a functional role for post-transcriptional regulation via A-to-I editing.\nThese findings suggest that 3′UTR editing is not just predictive of altered miRNA affinity but also correlates with\nmeasurable reductions in transcript levels, supporting a functional link between A-to-I editing and mRNA\nstability.\n4. ADARB2, although not catalytically active, is thought to act as a dominant-negative regulator of editing. In\nFigure 5, ADARB2 appears to correlate positively with AEI, Could the authors expand on this observation, or at\nleast discuss the potential role of ADARB2 in their system?\nRESPONSE: We appreciate the reviewer’s thoughtful observation. In response, we have expanded the\nDiscussion to address the potential regulatory role of ADARB2 in the context of RNA editing activity. While\nADARB2 lacks deaminase activity due to mutations in its catalytic domain, it retains the ability to bind double-\nstranded RNA (dsRNA) and is hypothesized to function as a dominant-negative regulator by competing with\nADAR1 or ADAR2 (ADARB1) for RNA substrates or protein interaction partners.\nInterestingly, in our dataset, we observed a positive correlation between ADARB2 expression and the Alu Editing\nIndex (AEI). This initially counterintuitive finding may reflect transcriptional co-regulation among ADAR family\nmembers, particularly under conditions of elevated ADARB1 expression, or a compensatory mechanism in\nresponse to increased editing demand during neurodevelopment. Alternatively, it is possible that ADARB2\ncontributes to editing regulation through indirect means, such as stabilizing RNA-protein complexes or\nsequestering regulatory cofactors. These possibilities are now explicitly discussed in the revised Discussion, and\nwe thank the reviewer for prompting a deeper exploration of this point.\n5. While several recoding sites (e.g., GRIA2, GRIA3, GRIK2) are highlighted, the effect sizes reported are modest\n(~5–10%). Prior literature suggests small changes can matter, but without functional assays, the biological impact\nhere is uncertain. The authors should temper causal language (“drives,” “accelerates”) and more explicitly\nacknowledge that the evidence is correlative.\nRESPONSE: Much in line with R1, we have revised the manuscript throughout to avoid causal phrasing.\nStatements such as “drives” or “accelerates” have been replaced with “associated with” or “consistent with.” This\nwording accurately conveys correlation while preserving biological plausibility. Here are three key locations in\nthe discussion where text has been re-worded:\n“Finally, while the observed association between ADARB1 and aberrant editing is compelling, causal\nrelationships remain to be established. For example, we did not perform functional assays to test the impact of\nindividual recoding sites such as GRIA3 R/G.”\n“These results suggest that T21 is associated with editing at key neurodevelopmental loci, potentially reflecting\npremature post-transcriptional maturation and altered proteomic remodeling during fetal brain development.”\n“Furthermore, ADAR2 overexpression in rodents has been associated with behavioral phenotypes such as\nhyperphagia45, although these phenotypes have not been causally linked to GRIA3 editing or to T21. While we\nobserve a robust and premature shift in editing at GRIA3 R/G and other recoding sites, we interpret these findings\nas correlative with ADARB1 upregulation rather than directly causal. Future site-directed editing studies will be\nneeded to determine whether specific recoding changes contribute to altered neurodevelopment in T21. ”\nMinor Points\n6. The blood RNA-seq analysis, while interesting, feels somewhat detached from the CNS focus. The authors\nmay wish to streamline or frame this more clearly as a complementary observation.\nRESPONSE: We revised the framing of the peripheral blood RNA-seq analysis to directly address this concern.\nRather than presenting it as a standalone observation, we now emphasize its role as a complementary tissue-\ncontrol analysis, designed to contextualize the brain-specific dysregulation of RNA editing. Specifically, we\nhighlight how ADARB1 overexpression and editing changes in T21 exhibit a CNS-specific pattern, whereas\nADAR-mediated editing in blood appears driven by immune activation. This tissue-contrast approach clarifies\nthe biological specificity and mechanistic distinctness of editing regulation across systems, reinforcing the CNS\nfocus of the manuscript while adding valuable comparative insight. The revised section is below:\n“To contextualize CNS-specific RNA editing dysregulation in T21, we analyzed matched bulk RNA-seq data from\nperipheral whole blood samples (n=304 T21, n=96 controls)20. ADARB1 expression was significantly elevated\nin T21 blood (FDR p = 3.14 × 10⁻⁸), consistent with gene dosage effects, whereas ADAR and ADARB2 expression\nwere unaltered by karyotype (Figure S14A). However, in contrast to the brain, global editing activity (AEI) was\nnot elevated across all T21 samples (Figure S14B). To evaluate immune-related editing effects, we stratified\nsamples by transcriptional subtype (MS1–MS3), revealing that only MS3 individuals (characterized by high\ninnate immune activation) exhibited increased AEI and elevated ADAR expression (Figure S14C–D). Notably,\nADARB1 expression tracked with karyotype but not immune tone and did not correlate with AEI (r = –0.0514, p\n= 0.24), whereas ADAR strongly correlated with AEI (r = 0.599, p = 1.07 × 10⁻⁵³) (Figure S14E). These findings\nhighlight that while ADARB1 is dosage-sensitive in both tissues, its role in RNA editing is CNS-specific, whereas\nADAR mediates immune-linked editing in peripheral blood. This contrast underscores tissue-specific regulatory\nmechanisms and supports a dual model in which gene dosage and immune signaling independently shape A-to-I\nediting dynamics across biological systems.”\n7. Some redundancy exists in the Results and Discussion, especially regarding repeated statements of ADARB1\noverexpression. Streamlining would improve clarity.\nRESPONSE: Repetitive mentions of ADARB1 overexpression have been removed. The narrative now introduces\nADARB1 once in the Results and returns to it in the Discussion when linking to editing outcomes.\n8. A few typographical and editorial oversights were noted (e.g., repeated sentences in page 10, paragraph 1).\nCareful editing would strengthen presentation.\nRESPONSE: All typographical errors and repeated sentences (previously noted on page 10) have been corrected.\nThe entire manuscript has been carefully re-edited for clarity and consistency.\nWe thank the reviewers for their careful evaluation and constructive feedback. The revisions substantially\nstrengthen the manuscript’s clarity and mechanistic interpretation. Below we provide detailed responses to each\ncomment and indicate how all concerns were addressed in the revised manuscript. All figure, text, and method\nupdates have been incorporated accordingly."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The manuscript by Breen et al has been well revised. Only minor points are listed below.\n1) Figure S15E shows that ADAR expression but not ADARB1 expression strongly correlated with AEI in peripheral whole\nblood samples. This finding is very interesting and important, because the authors did not distinguish ADAR transcripts\nbetween ADAR1 p150 and ADAR1 p110, which is methodologically difficult. It is estimated that ADAR1 p150 is highly\nexpressed in peripheral whole blood samples, whereas ADAR1 p110 is known to be dominant in CNS. AEI is usually\ncorrelated with the expression of ADAR1 p150 but not p110. This may be the reason why ADAR expression is not correlated\nwith AEI in CNS. The authors should mention this possibility in Discussion section.\n2) Figure 2D: its legend mentions that Spearman correlation coefficient (p) quantifies regional concordance, but it seems to\nbe shown as (r) but not (p) in the figure.", "reviewer_3": "Thank you for addressing my concerns."}, "author_response": "RESPONSE: We agree and now note this in the Discussion, see underlined and bold text:\n“To contextualize the CNS-specificity of our findings, we extended our analysis to peripheral blood RNA-seq data\nfrom individuals with T21 and controls20. ADARB1 expression was significantly elevated in T21 blood samples,\nconsistent with gene dosage effects, yet global editing activity (AEI) was not increased (Figure S15). However,\nwhen stratified by transcriptional subtype, only individuals with high interferon signaling (MS3 group) exhibited\nelevated AEI alongside increased ADAR expression. These observations underscore canonical immune-linked\nediting dynamics in blood, in contrast to the fetal brain, where AEI was driven by ADARB1 expression,\nindependent of immune activation or ADAR levels. This tissue-specific divergence may reflect ADAR1 isoform\nusage, as the p150 isoform that most strongly correlates with global editing predominates in peripheral tissues,\nwhereas the p110 isoform is dominant in the CNS and is less tightly coupled to AEI49. This divergence highlights\na tissue-specific regulatory axis in T21: ADARB1 predominantly governs CNS editing, consistent with our prior\nreports16,28,29,35, while ADAR responds to inflammatory signaling in immune tissue21,22. Notably, we also observed\na positive correlation between ADARB2 and AEI in brain tissue. Despite its lack of catalytic activity, ADARB2\nmay act as a dominant-negative modulator or reflect co-regulated expression patterns with ADARB1 during\ndevelopment. Together, these findings support a dual regulatory model of RNA editing in T21, shaped by both\ngene dosage and immune signaling, and emphasize the importance of tissue context in interpreting A-to-I editing\ndynamics.”\n2) Figure 2D: its legend mentions that Spearman correlation coefficient (p) quantifies regional concordance, but\nit seems to be shown as (r) but not (p) in the figure.\nRESPONSE: Thank you. This has been modified.\nReviewer #3 (Remarks to the Author):\nThank you for addressing my concerns.\nRESPONSE: Thank you for your time."}]

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