Integrative transcriptome-wide association analyses reveal PRKCG-linked GABAergic dysfunction in Fragile X-associated tremor/ataxia syndrome

Domain: biology
Published: 2026-01-08
Editorial outcome: major_revision

Abstract
Fragile X-associated tremor/ataxia syndrome (FXTAS) is a neurodegenerative disorder caused by CGG repeat expansions in the FMR1 gene. While CGG repeat toxicity is established, the precise molecular mechanisms driving neurodegeneration remain unclear. Here, we show that a multi-omics strategy combined with TWAS reveals brain-region-specific molecular signatures and striking gene dysregulation in inhibitory neurons. Using conditional mouse models, we demonstrate that selective expression of expanded CGG repeats in GABAergic neurons is sufficient to recapitulate key pathologic hallmarks of FXTAS. We identify PRKCG as a genetic modifier of FXTAS, with cross-species evidence linking its overexpression to disease onset. Many dysregulated mRNAs in GABAergic neurons are targets of hnRNPA2/B1, an RNA-binding protein sequestered by CGG repeat RNA. Functional screening in Drosophila further establishes PRKCG as a potent modulator of CGG-associated neurotoxicity. These findings uncover a critical role of GABAergic neurons in FXTAS pathogenesis and position PRKCG as a promising therapeutic target.

Introduction
The aberrant expansion of CGG repeats in the 5′ untranslated region (UTR) of the Fragile X messenger ribonucleoprotein 1 (FMR1) gene is implicated in Fragile X-associated disorders. Alleles harboring 55–200 repeats, falling within the premutation (PM) range, lead to the onset of Fragile X-related tremor/ataxia syndrome (FXTAS), a neurodegenerative disorder characterized by intention tremor, cerebellar gait ataxia, progressive cognitive decline, and Parkinsonism1. However, FXTAS shows incomplete penetrance, affecting only ~40% of male and 16% of female premutation carriers in late adulthood2, highlighting the need to better understand the molecular and cellular factors driving the onset of disease.

The “RNA toxicity” and “repeat-associated non-AUG (RAN) translation” are two major models proposed to explain FXTAS pathogenesis3–7. FXTAS carriers exhibited increased mRNA levels of FMR1 but displayed either no detectable or a slight reduction in FMRP protein levels, which is proportional to the length of the expanded CGG repeats8,9. In the RNA toxicity model, the upregulation of expanded CGG-repeat FMR1 mRNA triggers the formation of nuclear RNA aggregates, which can sequester various RNA-binding proteins, such as Pura3, heterogeneous nuclear ribonucleoprotein A2B1 (hnRNPA2B1)10, src-associated substrate during mitosis of 68kDa (SAM68)11, DiGeorge syndrome critical region gene 8 (DGCR8)12, and TAR DNA binding protein of 43kDa (TDP-43)13. This sequestration is believed to impede their normal biological function. In the RAN translation model, the FMR1 mRNA containing expanded CGG repeats initiates translation outside the traditional AUG start codon5. This leads to the generation of a polyglycine-containing protein, FMRpolyG, along with additional repeat-associated RAN translation products. FMRpolyG can be detected in brain tissues of FXTAS patients and mouse models and has been shown to be toxic in flies and cultured neurons, implicating its role in FXTAS pathogenesis5,7,14,15. Despite these insights, it remains unclear whether FXTAS is driven primarily by toxic RNA gain-of-function, RAN translation, or a combination of both mechanisms.

Animal models of FXTAS offer a valuable platform for advanced examination of how the CGG premutation induces alterations in gene activity across various tissues. A recent study suggested that expression of both FMRpolyG and expanded CGG repeat RNA (FMRpolyG + RNA) in the brain is pathogenic, recapitulating key FXTAS phenotypes, such as intranuclear inclusion formation and locomotor dysfunction7. As one of the key neuropathological hallmarks of FXTAS, intranuclear inclusions are found in neurons and astroglia throughout the brain that are immunoreactive for ubiquitin16–19, raising the question of how individual cell types are affected by the expression of expanded CGG repeats. However, previous studies provided only a limited understanding of the cell-type specificity associated with FXTAS pathogenesis. It remains unclear whether certain cell types have unique transcriptional signatures upon the expression of expanded CGG repeats. Additionally, it remains to be determined whether specific cell types are more vulnerable to the toxic effects of CGG repeats.

Our previous work using a robust Drosophila screening platform has uncovered genetic factors modulating CGG-associated toxicity, including the identification of PSMB5 as a suppressor of CGG-associated neurodegeneration in FXTAS3,20,21. Given the incomplete penetrance of FXTAS among fragile X premutation carriers, there remains a critical need to identify additional modifiers that could serve as genetic biomarkers and therapeutic targets. One promising strategy is transcriptome-wide association study (TWAS), which integrates eQTL data from relevant tissues/cell types with GWAS findings to pinpoint genes that drive disease risk through altered gene regulation. In several neurodegenerative diseases, such as Alzheimer’s disease (AD)22–24, Parkinson’s disease (PD)25–27, and amyotrophic lateral sclerosis (ALS)28–31, TWAS has provided insights into disease pathogenesis by uncovering the downstream effects of genetic risk loci. This approach holds significant promise for advancing our understanding of FXTAS and for prioritizing candidate genes for further study.

In this study, we employed a comprehensive multi-omics strategy in a FXTAS mouse model to map the molecular landscape in the brain. Our analyses revealed striking brain–region–specific signatures and profound gene dysregulation in inhibitory neurons induced by FMRpolyG and CGG-repeat RNA expression. By selectively expressing expanded CGG repeats in distinct neuronal populations using conditional mouse models, we demonstrated that the expression of expanded CGG repeats in GABAergic neurons alone is sufficient to recapitulate key FXTAS hallmarks while having minimal impact on Glutamatergic neurons. TRAP-seq further identified gene expression alterations specific to GABAergic neurons, many of which are bound by hnRNPA2B1, an RNA-binding protein sequestered by CGG repeat RNA. Moreover, a TWAS on premutation carriers identified 66 genes significantly linked to onset of FXTAS, with nine genes modulating CGG repeat–associated toxicity in an FXTAS fly model. Notably, cross-comparison of our datasets from various model systems revealed that upregulation of PRKCG, a known risk gene for SCA14, is associated with the onset of FXTAS. Finally, we showed that overexpression of human PRKCG and its fly ortholog, aPKC, induces neuronal toxicity in the FXTAS fly model. Together, our integrated analyses highlight the critical role of GABAergic neurons in driving FXTAS pathogenesis and identify PRKCG as a promising therapeutic target.

Results
Distinct transcriptional dysregulation in different brain regions of the FXTAS mouse model
Transgenic mice expressing both expanded CGG repeats and FMRpolyG in the brain exhibit FXTAS-like motor symptoms7, implicating the brain as the primary site of pathology. In this mouse model, the human FMR1 5′-UTR harboring 99 CGG repeats is inserted into the Rosa26 locus under a CAG promoter, with transcription silenced by a loxP-flanked polyadenylation cassette. Crossing these mice with Nestin-Cre transgenic animals, which express Cre recombinase in neuronal and glial progenitors, removes the cassette and activates expression of both CGG-repeat RNA and FMRpolyG protein throughout the brain (Fig. 1a). To elucidate the molecular mechanisms underlying FXTAS, we analyzed transcriptomic profiles from the cortex, cerebellum, and hippocampus at the onset of phenotype (3–4 m) and late progression stage (6–7 m) stages of FXTAS and control mice.

Based on the number of differentially expressed genes (DEGs) detected across disease progression, the cerebellum was identified as the most affected brain region, with the cortex showing an intermediate number of DEGs relative to the cerebellum and the hippocampus (Supplementary Data 1). The hippocampus displayed the fewest DEGs.

At the onset stage (3–4 months; Fig. 1b), we observed subtle transcriptomic changes in the bulk brain tissue of FXTAS mice compared to the control animals (n DEGs with P<0.01, cortex n=127; cerebellum n=162; hippocampus n=94; Supplementary Fig. 1a, b). Pairwise Fisher’s exact tests revealed significant differences in the numbers of DEGs among brain regions (all P<0.05). Minimal overlap among brain regions and distinct KEGG pathway enrichments indicate strong region-specific signatures (Fig. 1c). Notably, some of these pathways have been implicated in FXTAS or ataxia32–36, encompassing the observation of Ras and MAPK signaling cascade in the cortex and the enrichment of mitophagy and Parkinsonism disease in the cerebellum. Hist1h1e, an established intellectual disability (ID)-associated gene37, was the only shared DEG identified across all three regions.

At the late progression stage (6–7 months; Fig. 1d), we found profound gene dysregulation, aligning with an increased statistical power (n DEGs with FDR<0.05, cortex n=671; cerebellum n=1142; hippocampus n=205; Supplementary Fig. 1c) (Supplementary Data 1). Cross-region comparisons (Fig. 1e) showed that downregulated genes exhibited greater overlap than upregulated genes, likely reflecting conserved pathways involved in neuronal dysfunction or cell loss across regions38. Fisher’s exact tests confirmed that overlaps of both up- and downregulated DEGs were significant (all P<1e−19), with downregulated overlaps showing higher odds ratios and smaller P values. Heatmaps of DEGs clearly separated brain regions by their expression profiles (Supplementary Fig. 1d). Gene Ontology (GO) analysis illuminated both shared molecular pathways and brain region-specific signaling cascades altered in the cortex and cerebellum, particularly for those upregulated DEGs (Fig. 1f). In both brain regions, we found significant upregulation of genes involved in the chromatin organization and ubiquitin-dependent protein catabolic processes, suggesting that expression of expanded CGG repeats significantly impacts chromatin structure and protein stability in the brain. Moreover, the cerebellum exhibited notable upregulation of genes involved in mRNA processing and DNA damage response, suggesting an increased effort to maintain genomic integrity and eliminate abnormal mRNA molecules. In the cortex, some up-regulated genes are specifically associated with the protein folding process, revealing an increased emphasis on preventing the accumulation of misfolded or aggregated molecules, which could be toxic to the brain. KEGG analysis further highlighted brain-region-specific molecular cascades altered by the expression of expanded CGG repeats (Supplementary Fig. 1e, f). In comparison, downregulated DEGs displayed more convergent functional profiles across cortex and cerebellum (Fig. 1g), with consistent overrepresentation of lipid metabolism, ion transport, and regulating locomotor behavior. This observation could provide molecular insights into systemic features of the FXTAS mice, including motor dysfunction and obesity7.

Single-nuclei RNA-seq for the prefrontal cortex of FXTAS and control mice
The brain tissue’s profound cellular heterogeneity makes it challenging to study molecular alterations in particular cell types that contribute to disease pathogenesis. In FXTAS, cell type-specific gene expression changes are poorly understood, particularly at time points relevant to the onset of disease-related symptoms. To gain deeper insights into cellular perturbations associated with the pathogenesis of FXTAS, we applied snRNA-seq to the prefrontal cortex (PFC) of FXTAS animals and littermate controls at the onset of the phenotype stage (3–4 m) (Fig. 2a). After quality control, a total of 13,902 high-quality nuclei were analyzed, comprising 7025 nuclei from FXTAS animals and 6877 from controls. Unsupervised clustering identified 13 transcriptionally distinct clusters with consistent expression patterns across individuals (Fig. 2b–d). Based on the expression profile of well-established marker genes of mouse brain39,40, we annotated these clusters to seven major cell types, including excitatory neurons (Exc, 5329 nuclei, 38.3% of total), inhibitory neurons (Inh, 3365, 24.2%), astrocytes (Ast, 1382, 9.5%), oligodendrocytes (Olig, 1834, 13.2%), oligodendrocyte precursor cells (OPC, 512, 3.7%), microglia (Mic, 1004, 7.2%), and endothelial cells (End, 530, 3.8%).

Uniform manifold approximation and projection (UMAP) visualization showed broadly similar clustering patterns between FXTAS and control samples (Supplementary Fig. 2b). We next compared the abundance of the annotated cell types between FXTAS and controls. Most cell types were present at comparable proportions; however, inhibitory neurons were significantly increased in FXTAS (1834 nuclei, 26%) compared with controls (1531 nuclei, 22%; Welch’s t-test, P<0.05) (Fig. 2e). This result was independently validated by immunofluorescent staining with an anti-GABA antibody, which confirmed an increase in GABA+ neuronal density in FXTAS PFC relative to controls (Welch’s t-test, P<0.05; Supplementary Fig. 3). In addition, a specific inhibitory subcluster (Inh_2) contained a higher proportion of nuclei in the FXTAS group compared to controls (Welch’s t-test, P<0.05) (Fig. 2f). It is important to note that due to the sensitivity of different cell types to tissue dissociation, the estimated proportions may not accurately reflect their actual distribution in the mouse brain. Nevertheless, the changes in cell-type ratios between FXTAS mice and controls may reflect a disease-associated cellular change linked to the expression of expanded CGG repeats in the brain.

Profound gene expression dysregulation in inhibitory neurons of FXTAS mice
We next examined cell type-specific transcriptional alterations in FXTAS. Across all populations, we identified 1670 DEGs (adjusted p<0.05, Supplementary Data 2), with inhibitory neurons accounting for the majority (79%, n=1285) (Fig. 3a). Of these, 1216 DEGs also met a log fold-change threshold of >0.1 (Supplementary Fig. 2c). Inhibitory neurons showed a strong bias toward gene downregulation, indicative of a broad suppression of transcriptional programs compared to other cell types (Fig. 3a, b). In contrast, excitatory neurons displayed moderate change by harboring 351 DEGs, while non-neuronal cells displayed minimal alterations. Across comparison of DEGs between cell types suggested cell type-specific gene expression patterns (Supplementary Fig. 2d). We then investigated the perturbed molecular signatures associated with the pathogenesis of FXTAS. Inhibitory neuron DEGs were linked to critical pathways implicated in neurodegenerative disorders, including endocytosis, axon guidance, and ubiquitin-mediated proteolysis (Fig. 3c). This observation suggested that the expression of expanded CGG repeats dramatically disrupts cellular homeostasis, compromises neuronal connectivity, and impairs protein quality control in inhibitory neurons. In contrast, excitatory neuron DEGs were enriched for pathways related to parathyroid hormone (PTH) secretion and action, phosphoinositide (PI), and cyclic adenosine monophosphate (cAMP) signaling (Fig. 3c).

We then examined the concordance between snRNA-seq and bulk RNA-Seq on the cortex (3–4 m) from FXTAS mice by examining the correlation of log-fold changes of genes between the two datasets. Despite modest overall correlations, inhibitory and excitatory neurons exhibited the strongest correlations with bulk gene expression (Fig. 3d; P<0.001), particularly for DEGs with substantial fold changes. To further clarify the functional components associated with the most significant gene expression changes, we performed GO analyses on the top 200 DEGs in each neuronal cell type, considering their statistical significance and expression fold changes (Fig. 3e). Although several GO terms were common to both cell types, inhibitory neurons uniquely exhibited enrichment in categories related to locomotor behavior and balance control (Fig. 3f), suggesting their specific contribution to FXTAS-associated motor impairments.

Finally, we tested whether these transcriptional alterations could be attributed to higher transgene expression in inhibitory neurons. We appended the GFP sequence (fused in-frame with the human FMR1 5′-UTR transgene) to the mouse reference genome and reprocessed the snRNA-Seq data. Transgene expression was estimated by calculating the summed GFP counts normalized to the number of nuclei per cell type in the FXTAS samples. As shown in Supplementary Fig. 2e, GFP-positive nuclei were prominently detected in excitatory neurons, inhibitory neurons, astrocytes, and OPC, but were rare in microglia and oligodendrocytes and absent in endothelial cells (Supplementary Fig. 2e). Average GFP expression was broadly comparable across these major neuronal and glial types. These results demonstrate that transgene expression is broadly distributed across neurons and glial populations derived from Nestin-positive progenitors. Therefore, the pronounced transcriptional dysregulation observed in inhibitory neurons is unlikely to be explained simply by transgene dosage alone, but instead suggests an intrinsic susceptibility of this neuronal population to CGG-repeat toxicity.

Mice expressing FMRpolyG+RNA in GABAergic neurons display the full spectrum of the phenotype
To further dissect the contribution of specific neuronal subtypes to FXTAS pathogenesis, we generated conditional mouse models by crossing Tagger mice41 with two Cre lines driven by Gad2 or CaMKIIα promoters. The Tagger mouse line bears a quad-cistronic transgene that stoichiometrically expresses four proteins to capture distinct populations of nucleic acids41. The resulting Gad2-Tagger and CaMKIIα-Tagger animals were then independently crossed with floxed FXTAS (FMRpolyG+RNA) transgenic mice to selectively express FMRpolyG protein and CGG repeat RNA in GABAergic and glutamatergic neurons, respectively (Fig. 4a). Quantitative RT-PCR revealed high expression of expanded CGG RNA in both cortex and cerebellum (Fig. 4b), validating the effective excision of the loxP cassette and successful transgene expression. Additionally, we found lower transgene expression in brain tissues of transgenic FMRpolyG+RNA; Gad2-Tagger mice compared to FMRpolyG+RNA; CaMKIIα-Tagger animals. This finding is consistent with the known abundance of neuronal types: ~70–85% of cortical neurons are excitatory and 15%-30% are inhibitory, confirming the selected promoters’ specificity.

We then performed behavior assays and neuropathological staining to determine whether expanded CGG repeats in specific neurons could recapitulate the deficits observed in mice with brain-specific expression of CGG repeats7. Intriguingly, starting at 4 months of age, mice expressing expanded CGG repeats in GABAergic neurons displayed progressive locomotor deficiency (Fig. 4c) and significant weight gain compared to controls (Fig. 4e). Two-way ANOVA confirmed significant effects of genotype, age, and genotype×age interaction on rotarod performance (F(1,11)=78.57, P<0.0001; F(3,33)=4.15, P=0.0133; F(3,33)=12.48, P<0.0001), indicating progressive motor decline. Body weight analyses also revealed strong genotype, age, and interaction effects (F(1,25)=15.83, P<0.0001; F(1.74,43.56)=308.8, P<0.0001; F(4,100)=99.53, P<0.0001), indicating genotype-associated weight gain over time. Of note, male mice displayed more pronounced motor deficits, with phenotypic changes observable as early as 3 months of age (Supplementary Fig. 4a, b). In contrast, females displayed delayed motor decline but earlier obesity (Supplementary Fig. 4c, d). This observation suggested a potential role of gender-specific factors in the progression of FXTAS. In contrast, mice expressing the transgene in glutamatergic neurons showed no evidence of weight gain or locomotor deficits (Fig. 4d, f). ANOVA tests revealed no significant genotype or interaction effects on rotarod performance. For body weight, only age effects were detected (F(4,211)=11.57, P<0.0001), consistent with normal growth. These results suggest that expression of expanded CGG repeats in GABAergic neurons alone is sufficient to drive FXTAS phenotypes.

To better evaluate whether body weight as a confounding factor in rotarod deficits, we weight-matched subsets of FMRpolyG+RNA; Gad2-Tagger mice and controls at 3–4 months, when male Gad2 cohorts first exhibited motor decline. Even after weight matching, FMRpolyG+RNA; Gad2-Tagger mice exhibited significantly shorter rotarod latencies than wild-type controls at both ages (Supplementary Fig. 5), indicating that the observed onset of motor impairment is not attributable to differences in body weight. We further investigated whether the expression of expanded CGG repeats in GABAergic neurons could reproduce the phenotypic changes observed in FMRpolyG+RNA; Nestin-Cre animals. Remarkably, both transgenic mouse lines displayed similar levels of mobility impairment at 4 months of age (Supplementary Fig. 4e). However, by 6 months, FMRpolyG+RNA; Nestin-Cre mice had a higher body weight compared to FMRpolyG+RNA; Gad2-Tagger mice (Supplementary Fig. 4f), suggesting that in addition to inhibitory neurons, other cell types may also contribute to energy balance regulation. Finally, neuropathological examination showed a marked reduction of calbindin-labeled Purkinje cells and the accumulation of FMRpolyG-positive aggregates in FMRpolyG+RNA; Gad2-Cre mice, mirroring findings in FXTAS mice (Fig. 4g, h; Supplementary Fig. 6a, b). Taken together, these findings demonstrated that the expression of CGG repeats in GABAergic neurons alone was sufficient to induce FXTAS-like motor, metabolic, and neuropathological phenotypes, whereas the expression in glutamatergic neurons had minimal impact.

TRAP-Seq identifies age-dependent and brain region-specific dysregulation in GABAergic neurons of FXTAS
To further explore molecular signatures of GABAergic neurons during disease progression, we performed anti-HA translating ribosome affinity purification (TRAP) followed by RNA sequencing (TRAP-seq) on both cortex and cerebellum from FMRpolyG+RNA; Gad2-Tagger mice and their littermate controls (Fig. 5a). In these animals, an HA epitope is fused to the large ribosomal subunit protein 22 (Rpl22), allowing selective capture of ribosome-bound mRNAs from GABAergic neurons in response to expanded CGG repeats. Western blot analysis confirmed the expression of FMRpolyG in brain lysates of FMRpolyG+RNA; Gad2-Tagger individuals, but not in control animals (Supplementary Fig. 7a, b). Successful immunoprecipitation (IP) of HA-tagged ribosomes was further validated by anti-HA immunoblotting of brain lysates before and after IP (Supplementary Fig. 7c, d).

TRAP-seq revealed substantial transcriptional dysregulation across both developmental stages, identifying 1485 DEGs in the cortex and 783 in the cerebellum (Fig. 5b) (Supplementary Data 3). Cross comparisons and KEGG pathway analyses indicated age-dependent and brain region-specific alterations (Supplementary Fig. 8a). For instance, DEGs at 3–4 months in GABAergic neurons were associated with key processes in axon development, such as the enrichment of key processes, including axon guidance, semaphorin-plexin signaling pathway, and axogenesis in both regions (Supplementary Fig. 8b, c). In the cerebellum, DEGs are associated with neuronal and motor-related processes, such as locomotor behavior (3–4 m) and skeletal muscle contraction (6–7 m), among a broader set of categories (Supplementary Fig. 8d). These molecular alterations indicate that pathways important for motor function are disrupted in GABAergic neurons, potentially contributing to the locomotor impairments observed in behavioral assays.

We next sought to leverage these cell type-specific gene expression profiles to the RNA toxicity mechanism contributing to FXTAS pathogenesis. HnRNPA2/B1 is one of the RNA-binding proteins that can become sequestered within the RNA foci formed by the accumulation of expanded CGG repeats RNA10. To link our current data to this model, we compared the TRAP-Seq DEGs with previously generated hnRNPA2/B1 eCLIP-seq datasets from adult mouse brain tissues (Supplementary Data 4). Surprisingly, nearly half of those TRAP-DEGs in GABAergic neurons overlapped with hnRNPA2/B1 targets, with the exception of cerebellar DEGs at the late stage (Fig. 5c, e). Fisher’s exact tests confirmed that these overlaps were highly significant at both stages and in both brain regions (Supplementary Data 6). Among them, 31 genes in the cortex and four genes in the cerebellum were consistently dysregulated at both early and late stages, including factors linked to neuronal development (Nfix, Nfia, Myt1l, Gdf11, Casz1) and protein folding (Unc45a, Hspb6). Other hnRNPA2/B1 targets were dysregulated in an age-dependent manner and exhibited distinct KEGG pathways (Fig. 5d, f), suggesting their dynamic, age-dependent roles in GABAergic neurons. Notably, we found that hnRNPA2/B1 targets mRNA of genes involved in axonal modulation and chromatin organization processes, particularly at the onset of the disease stage. These results could support a role for RNA-binding protein sequestration in driving GABAergic neuron dysfunction in FXTAS.

A combination of TWAS and Drosophila screen identifies 9 genes as genetic modifiers of FXTAS
To identify genetic modifiers of FXTAS, we performed a TWAS by integrating genetic and brain region-specific gene-expression panels from GTEx Consortium (cortex N=184, cerebellum N=189, and cerebellar hemisphere N=158) with WGS summary statistic data from 159 FMR1 premutation carriers. Our analysis revealed 66 genes that reached a Bonferroni-corrected significant threshold of α=1×10−4 across the three brain regions (Fig. 6a, b, c), with 31 genes meeting a more stringent threshold of 2.1×10−6 (Supplementary Data 5). These candidate genes play roles in RNA binding, protein ubiquitination, chromatin organization, phosphorylation, and DNA replication (Fig. 6d). Notably, nine genes are presented in at least two examined tissues, including LSM2, PSMB1, PRKCG, PPP6R1, RP11-289I10.2, DXO, SYNGAP1, VARS and CCDC106 (Supplementary Fig. 9a, b). Some candidates, such as PRKCG and RING1, have been implicated in ataxia42–44 or Parkinson’s disease45–48. Interestingly, PSMB1, which encodes a subunit of the 26S proteasome, was identified in this study, which aligns with our previous discovery of PSMB5 (encodes another subunit of the 26S proteasome) as a modifier of FXTAS through a WGS analysis21.

Due to the limited statistical power of current data, instead of taking a statistical approach for prioritizing candidate genes, we prioritized 22 candidate genes for functional screening in our FXTAS Drosophila model by considering the ranking scores of their fly homologs. Knockdown of these genes using the UAS-GAL4 system in the fly eye revealed that 9 genes exhibited genetic modulation of CGG toxicity, as evidenced by a notable enhancement of disrupted eye morphology or the induction of lethality (Fig. 6e; Supplementary Fig. 11a). These results provide a set of candidate genes for further functional investigation.

PRKCG modulates the CGG repeat toxicity associated with FXTAS
The estimated coefficients (β) of TWAS candidate genes revealed distinct patterns of their enrichment of gene expression levels between FXTAS individuals and controls (Supplementary Data 5). A positive β-value indicates predicted upregulation in FXTAS, while a negative value suggests downregulation. To determine whether these TWAS candidate genes are dysregulated in response to the expression of expanded CGG repeats in mammalian systems, we compared TWAS genes with expression profiles from bulk RNA-seq of FXTAS mouse brains. Five TWAS genes showed significant differential expression in cortex or cerebellum (Fig. 7a). Notably, Prkcg expression was elevated in cortex during the development of the disease, while others were primarily dysregulated at the late progression stage (6–7 m). Western blot analysis confirmed increased PKCγ protein in cortical tissue at 3–4 months but not at 6–7 months (Supplementary Fig. 10), might suggest a transient early upregulation that normalizes at later stages. Integration with TRAP-Seq data from the conditional FXTAS mouse model revealed 7 TWAS genes with altered expression in GABAergic neurons (Fig. 7b). Fisher’s exact test demonstrated significant overlap between TWAS candidates and TRAP-seq DEGs in cerebellar GABAergic neurons at 6–7 months (P=0.018; supplementary Data 6). Among these, Prkcg was specifically upregulated in cerebellar GABAergic neurons at disease onset. TWAS analysis further indicated consistent positive enrichment of PRKCG across all three regions (Supplementary Fig. 9d), implying its upregulation in premutation carriers. Validation by using RT-qPCR in both mouse cortex and patient-derived neural progenitor cells (NPCs) confirmed significantly elevated PRKCG expression compared with controls (Fig. 7c, d). Additionally, we found that hnRNPA2/B1 binds to Prkcg mRNA. Together, these findings implicate increased PRKCG expression in FXTAS pathogenesis.

PRKCG, encodes protein kinase C gamma (PKCγ), a neuron-enriched isoform of the protein kinase C (PKC) family49. To further investigate its functional role in FXTAS, we overexpressed the Drosophila ortholog of PKC (aPKC) in the eye of the FXTAS fly model, resulting in the enhancement of the rough eye phenotype (Fig. 7f, the middle panel). Given the absence of a specific PRKCG ortholog in fly, we cloned the full-length cDNA of human PRKCG (hPRKCG) into a pUAST Drosophila vector and generated transgenic UAS lines that overexpressed hPRKCG under the control of a GAL4 driver (Fig. 7e). Upon crossing these transgenic lines with the FXTAS fly model, we observed that overexpression of hPRKCG induced the rough eye phenotype in the progenies (Fig. 7f, the right panel; Supplementary Fig. 11b), mirroring the effects of aPKC and demonstrating that PRKCG overexpression exacerbates CGG repeat-induced neurotoxicity.

To further validate this mechanism in mammalian cells, we manipulated PRKCG expression in Neuro2A (N2A) cells expressing CGG-repeat RNA by using knockdown and overexpression approaches. Short hairpin RNA (shRNA)-mediated knockdown of Prkcg significantly improved cell viability measured at 96h (Welch’s t-test, P<0.001) (Fig. 7g). Conversely, overexpression of hPRKCG further reduced cell viability compared with CGG repeats RNA expression alone, consistent with our fly genetic results. Taken together, the results from human genetic analyses, mouse models, fly screening, and cell-based toxicity assays demonstrate a critical role of PRKCG in FXTAS. These findings establish PRKCG as a biologically relevant modifier of CGG repeat–associated neuronal toxicity and support its prioritization as a candidate therapeutic target.

Methods
Animals
Mice were housed in a pathogen-free facility at the Emory University Division of Animal Resources under standard conditions. Animals were maintained on a 12-h light/12-h dark cycle (lights on at 7:00 AM) with ad libitum access to food and water. The ambient temperature was maintained at 20–22°C with a relative humidity of 40–60%. All animals were group-housed unless single housing was required for a specific procedure. Nestin-Cre transgenic (strain #003771; with C57BL/6 genetic background), Camk2a-Cre transgenic mice (strain #005359; with C57BL/6 genetic background), and Gad2-IRES-Cre knock-in mice (strain #010802; with C57BL/6 genetic background) were obtained from The Jackson Laboratory. Transgenic mice (with C57BL/6 genetic background) containing the entire human 5′ UTR of FMR1 with expanded 99 CGG repeats expressing CGG RNA and FMRpolyG protein were obtained from Baylor College of Medicine. Transgene expression was controlled by a loxP-flanked polyadenylation cassette and activated by Cre-mediated recombination.

The Tagger mouse line (with C57BL/6 genetic background) was obtained from Dr. Walker Jackson41. Under the control of CAG promoter and two transcription-terminating STOP cassettes (Frt-NeoR-Frt and LoxP-STOP-LoxP), Tagger mice can stoichiometrically express four proteins for the capture of distinct populations of nucleic acids, including HA-tagged ribosomal protein L22 (Rpl22-HA, for Ribo-Tag), Uracil phosphoribosyltransferase (TgUPRT, for TU-Tag), red fluorescent protein with a triple NLS (RFP-NLS, for Nuc-Tag), and FLAG-V5-tagged Argonaute 2 (FLAG-V5-Ago2, for Ago-Tag). The mice were first crossed with ROSA26-Flpo mice (stock 007844, The Jackson Laboratory, Bar Harbor ME) to remove the FNF cassettes, creating LSL-Tagger lines. Then, Gad2-Cre and Gad2-IRES-Cre mouse lines were used to activate cell-type specific expression of Tagger transgene. The resulting animals were further crossed with FXTAS mice to obtain progenies with cell type-specific expression of both FMRpolyG, CGG RNA and Tagger transgene.

Male mice were used for all tissue dissection and all sequencing experiments including bulk RNA-seq, snRNA-seq, and TRAP-seq, to reduce molecular variability associated with sex-specific gene expression, particularly variability introduced by the female estrous cycle73. Both male and female mice were used for behavioral assays to ensure that the phenotypic consequences of expanded CGG repeats are assessed across sexes, thereby increasing the generalizability and translational relevance of the findings. Age- and sex-matched littermates were randomly allocated to experimental groups. The age and exact number of animals used in each experiment are provided in the corresponding figure legends and the Reporting Summary.

Time-series weight measurement and rotarod test
Both male and female mice were used for body weight measurements and rotarod tests. Body weights of mice with correct genotypes are recorded from 2 to 6 months old. Rotarod tests were conducted from 3 to 6 months old each month. The rod was cleaned and set to a fixed speed of 4.0 RPM before the test. Three testing trails were performed, during which the rotation speed accelerated from 4 to 40 rpm in 5 min. As the mice fall from the rod, each lane’s trip plate will stop the timer and display the latency in seconds. Trials were separated by 5–10 min intervals. The mean of latency was calculated and used as an indicator of locomotor performance.

Isolation of RNA from mouse brain tissue
For bulk RNA-seq, male mice were used, with a minimum of three biological replicates included for each genotype. Cortex, cerebellum and hippocampus were dissected from each mouse and homogenized in 1 mL of TRIzol (Invitrogen, 15596026) followed by standard RNA isolation protocol. Briefly, samples were incubated in TRIzol at RT for 5 min. Following incubation, 200 μL chloroform was added and samples were vigorously shaken for 15 s. The mixture was then incubated at RT for 15 min and centrifuged at 20,000×g for 15 min at 4°C. The upper aqueous layer, containing RNA, was carefully transferred to a new tube. To precipitate the RNA, 600 μL of isopropyl alcohol was added, and the samples were incubated at RT for 10 min before centrifugation at 20,000×g for 10 min at 4°C. The resulting RNA pellet was washed with 75% ethanol, air-dried for 10 min, and dissolved in RNase-free water. The quality and concentration of the RNA were assessed using the Qubit 2.0 RNA HS Assay (Thermo Fisher Scientific, Q32852).

Translating ribosome affinity purification (TRAP) and RNA isolation
The TRAP procedure41 were performed on cortex and cerebellum tissues from male mice. Brain tissues were dissected in pre-cold PBS buffer and homogenized in PBS containing 50 mM Tris (pH 7.5), 100 mM KCl, 12 mM MgCl2, and 1% Nonidet P-40, one mM DTT, 100 U/mL Ribolock RNase inhibitor, 100 μg/mL cycloheximide, and 1 tab/5 mL SigmaFast protease inhibitor cocktail (with EDTA, Sigma-Aldrich, S8830-2TAB). The homogenates were kept on ice for over 15 min and centrifuged at 20,000 xg for 10 min at 4°C to remove nuclei and cell debris. Approximately 50 μL of S1 supernatant was kept for input RNA isolation, and the remaining supernatant was pre-cleared with Protein G Dynabeads (Thermo Fisher Scientific, 10003D) at 4°C on a rotator for 30 min. The S2 supernatant was incubated with 10 ul HA antibody (Roche, Anti-HA, 12CA5) on a rotator at 4°C for 2 h, followed by incubating with protein G beads for an additional 2 h at the same condition. Afterward, the beads were collected and washed 3 times with High Salt Buffer (HSB: 50 mM Tris (pH 7.5), 300 mM KCl, 12 mM MgCl2 and 1% Nonidet P-40, one mM DTT, 50 U/mL Ribolock RNase inhibitor (Thermo Fisher Scientific, EO0382), and additional 3X wash by using Extra High Salt Buffer (EHSB: identical to HSB but containing additional 300 mM NaCl). Per each wash, beads were rotated gently at 4°C for 2 min. During the last wash, 10% of the beads were saved for western blot, and the remaining beads were used for RNA isolation by TRIzol Reagent. The quality and concentration of the RNA were assessed using the Qubit 2.0 RNA HS Assay.

RNA-Seq and data analysis
The isolated mRNAs were prepared for the library according to the manufacturer’s protocols. Paired-end sequencing (2×150) was performed on an Illumina NovaSeq X-plus instrument. Sequencing reads were aligned to the mm10 mouse genome using TopHat v2.1.1. The number of reads aligned to each gene was tabulated using FeatureCount v1.5.3. All reads mapped to gene bodies were used for differential gene expression analysis using R package DEseq2 v1.30.1. DEGs were selected as those with a P<0.05. DAVID web server was employed to perform GO analyses. Heatmaps were generated using R package pheatmap v1.0.13 with hierarchical clustering applied to rows.

Isolation of nuclei from mouse cortex
All steps were performed on ice or at 4°C. Frontal cortex from two male FXTAS mice and two male littermate controls was rapidly dissected in ice-cold homogenization buffer (HB: 260 mM sucrose, 30 mM KCl, 10 mM NaCl, 20 mM Tricine-KOH pH 7.8, 1 mM DTT, 0.5 mM Spermidine, 0.2 mM Spermine, 0.3% NP40, cOmplete Protease inhibitor (Sigma-Aldrich, 11697498001), and Ribolock RNase inhibitor (Thermo Fisher Scientific, EO0382) and immediately transferred to a Dounce homogenizer filled with 1 mL HB. Tissue was homogenized by 20 strokes with each pestle A and pestle B. The homogenate was then filtered through a 70 um strainer followed by centrifugation for 10 min at 300×g at 4°C. The pallets were then resuspended in a homogenization buffer and mixed with 1 volume of 50% Iodixanol (Sigma-Aldrich, D1556) to make a final concentration of 25% iodixanol. Without disturbing the mixture, 30% Iodixanol was slowly layered underneath the 25% iodixanol mixture, and then 40% Iodixanol was gently added under the 30% mixture to generate a density gradient. A clearly defined interface should be visible between all three layers. The nuclei were centrifuged for 20 min at 1500×g and 4°C in a swinging bucket centrifuge with the brake off. After the centrifugation, the nuclei band was transferred from the interface between 30% and 40% iodixanol solutions to a pre-chilled tube. The nuclei were washed with wash buffer at 4°C and counted after adding trypan blue.

Library preparation for snRNA-Seq
Libraries were generated with the 10x Genomics Chromium Single Cell Gene Expression reagent kit following the manufacturer’s instructions with minor modifications. Briefly, per sample, 16,100 nuclei were resuspended in 1x diluted nuclei buffer (10x Genomics) with 2% BSA, and 10,000 nuclei were captured. For 10,000 nuclei capture targeted, single nuclei expression libraries were sequenced to 20,000 read pairs per nuclei on an Illumina NovaSeq 6000 instrument at the Florida State University Translational Science Laboratory.

Data pre-processing and analyses of snRNA-seq
Raw reads were obtained with 10x Genomics pipeline CellRanger (v5.0.0) “mkfastq” with default parameters. Reads from each sample were then aligned to the mouse genome (mm10) with reference data “refdata-cellranger-mm10-2020-A-2.0.0”, and the gene-by-cell count matrices were generated. R package Seurat (V4.3.0.1) was performed for quality control (QC) analyses and downstream analyses, including data normalization, clustering, highly variable gene selection, etc. We created a Seurat object containing all four samples (two wild types and two FXTAS) and performed QC on the percentage of reads mapped to the mitochondrial (MT) genome. The MT percentage ranges from 0 to 3.37% with a mean of 0.01%, presenting high data quality; therefore, we did not filter any cells based on MT percentage. Next, according to the distribution of number of genes expressed in cells, we kept cells with at least 200 and at most 4000 genes expressed, resulting in 17,794 cells with 32,285 genes measured. In the downstream analyses, we first performed data normalization using “NormalizeData” with scale factor set as 10,000 and log-transformation. Then, “FindVariableFeatures” was used to select the top 2000 genes that exhibit high cell-to-cell variability with variance-stabilizing transformation. Data was then center scaled with “ScaleData,” and dimension reduction was performed using “RunPCA” with 30 principal components (PCs). To mitigate batch effect between conditions, we used harmony (v0.1.1) to perform batch correction. Next, “RunUMAP” was performed on the top 20 embeddings from harmony to obtain UMAP embeddings for visualization purposes. Based on the top 20 embeddings from harmony, we performed “FindNeighbors” to construct a shared nearest neighbors (SNN) graph and “FindClusters” with a resolution of 0.6 to obtain clusters using the Leiden algorithm.

Cell type annotation and differential gene expression analysis
Both unsupervised clustering and supervised classification methods were applied to identify seven major cell types. For unsupervised clustering, we used “FeaturePlot,” “DotPlot,” and “DoHeatmap” to visualize known marker genes (Slc17a7, Gad1, Gad2, Neurod2, Nrn1, Dlx1, Mobp, Mog, Acsbg1, Aqp4, Csf1r, Ctss, Flt1, Pdgfra) in each cluster for cell type identification purposes. In the meantime, two supervised classification methods (Cellcano74 and Seurat V4.3.0.1) were performed to double-confirm the identified cell types using two well-annotated prefrontal cortex datasets (GSE124952, GSE116470) as reference datasets. After careful curation, we removed clusters and cells that could not be identified and obtained 13,902 cells. Based on identified cell types, we performed differential gene expression analysis with “FindMarkers” in Seurat between conditions. Average log2FC (avg_log2FC) larger than 0.1 and adjusted p value less than 0.05 were used as cutoffs to identify DEGs per cell type.

Immunohistochemistry and immunofluorescence
Transcardiac perfusion was performed with saline followed by 4% paraformaldehyde (PFA) to clear blood and brain tissue. Brain tissue was post-fixed in 4% PFA overnight at 4°C, and the Neuropathology Core of Emory Center for Neurodegenerative Disease prepared paraffin-embedded brain sections. Sections were deparaffinized at 58°C for 40 min, immersed in xylene for 10 min, rehydrated through graded ethanol (100% and 95%), rinsed three times in water and three times in PBS. Antigen retrieval was performed in sodium citrate buffer (pH 6.0) at 95°C for 30 min, followed by rinsing in PBS. To quench endogenous peroxidase activity, sections were incubated in 0.3% H₂O₂ in PBS for 10 min at room temperature, then permeabilized in 0.3% Triton X-100/PBS for 1 h and blocked in 2% normal goat serum (NGS) and 3% BSA for 2 h at room temperature. Sections were incubated overnight at 4°C overnight with primary antibody against FMRpolyG (Sigma-Aldrich, MABN2280, clone 8FM-2F7) or Calbindin-D28k (ProteinTech, 66394-1-PBS) diluted in PBS containing 3% BSA, 2% NGS, and 0.1% Triton X-100. After three washes in PBS, sections were incubated for 1 h with biotin-conjugated goat anti-mouse secondary antibody (Jackson ImmunoResearch, 115-065-166; 1:500 in PBS with 3% BSA and 2% NGS), followed by three PBS washes. Detection was performed using the VECTASTAIN® Elite® ABC Kit (PK-6100) for 1 h, with three PBS washes, and visualized with 1× DAB substrate. Sections were washed three times in water and counterstained with hematoxylin. Finally, slides were dehydrated, covered, and imaged using a ZEISS Axio Imager. A2 microscope. For immunofluorescent, sections were co-stained overnight at 4°C with primary antibodies against GABA (Thermo Fisher, PA5-32241) and NeurN (Sigma-Aldrich, MAB377), diluted in PBS containing 3% BSA, 2% NGS, and 0.1% Triton X-100. After three washes in PBS, sections were incubated for 1 h with secondary antibodies (Invitrogen: Alexa FluorTM 568 goat anti-mouse IgG (H+L), and Alexa FluorTM 647 goat anti-rabbit IgG (H+L)), followed by three PBS washes. Nuclear staining was performed with DAPI for 3 min at room temperature, after which sections were washed with PBS and mounted with antifade mounting medium. Slides were cured in the dark at room temperature for 1 h. Images were acquired using a Leica DMi8 microscope and analyzed with ImageJ.

Western blot analysis of mouse brain lysate
The brain tissue was homogenized using a Dounce homogenizer in lysis buffer containing 50 mM Tris-HCl, pH 7.5, 150 mM NaCl, 1% Triton X-100, and cOmplete Protease inhibitor (Sigma-Aldrich, 11697498001). Homogenates were incubated on ice for 30 min and centrifuged at 1200×g for 10 min at 4°C. The supernatant was transferred to a new tube and centrifuged again at 15,000×g for 20 min at 4°C. The final supernatant was collected on ice, and protein concentrations were quantified using a NanoDrop spectrophotometer according to the manufacturer’s instructions. Proteins were mixed with 4× Laemmli sample buffer (Bio-Rad, 34229167) containing 10% β-mercaptoethanol (Fisher Scientific, 21985-023) and denatured at 95°C for 10 min. Samples were resolved by SDS–PAGE on 4–12% Bis-Tris gels and transferred to PVDF membranes. Membranes were blocked in 5% non-fat dry milk in PBST for 1 h at room temperature and incubated with primary antibodies in 5% milk overnight at 4°C. After three washes in PBST, membranes were incubated with HRP-conjugated secondary antibodies, and protein bands were visualized by chemiluminescent detection reagent (Cytiva, RPN2235) on X-ray film (GE Healthcare). Band intensities were quantified using ImageJ. Primary antibodies used: anti-HA (Roche, 12CA5), anti-FMRpolyG (Sigma-Aldrich, MABN2280, 8FM-2F7), anti-GAPDH (Thermo Fisher, MA1-16757), anti-ACTIN (Thermo Fisher, MA5-11869), anti-PKCgamma (Thermo Fisher, 13-3800).

Study population of FMR1 premutation carriers and WGS
In this study, participant recruitment and sample acquisition were coordinated through the National Fragile X Center at Emory University as described previously21. Once a participant was screened for eligibility and provided consent, a blood or saliva sample was collected, and a general medical history questionnaire was completed. For FXTAS, 159 participants who were carriers of the FMR1 premutation were recruited21. Specifically, 94 males and females with onset of FXTAS motor symptoms before age 65 are enrolled as cases, while 56 males without FXTAS symptoms by age 68 are enrolled as controls21. All samples were mapped to the human genome using PEMapper, and the variants were called using PECaller75. To correct for confounding due to ancestry or batch effects, principal component analysis was performed on a set of 567,720 independent SNPs using PLINK 1.9. The top 2 PCs were retained for covariate adjustment in subsequent studies.

TWAS analysis
We used TIGAR-V276 to train prediction models of gene expression using reference transcriptomic data and genetic data from various tissues collected by the Genotype-Tissue Expression (GTEx) project V8. Expression prediction models were derived from the brain cortex (N=184), cerebellum (N=189), and cerebellar hemisphere tissues (N=158) within GTEx. For each tissue, we performed pre-processing of the tissue expression and genetic data from GTeX using standard QC pipelines. Specifically, TIGAR-V2 considered only genes with gene expression of transcripts per million (TPM) > 0.1 in ≥ 10 samples and variants with minor allele frequencies > 0.01, missing rates < 20%, and Hardy-Weinberg equilibrium p > 10−5 for fitting gene expression prediction models. TIGAR-V2 adjusted gene expression for age, body mass index, top five genotype PCs, and top probabilistic estimation of expression residual factors for each gene. TIGAR-V2 then fit prediction models on the adjusted gene expression data treating cis-SNPs (within ± 1 Mb region of transcription start sites of the target gene) as predictors. Assuming an additive genetic model for the expression quantitative trait (Eg) of target gene g, the prediction model can be represented as follows.

Eg = Gref w + ε; ε ~ N(0, σ2ref I) (1)

where Gref represents the vector of cis-SNP genotypes for gene g in the GTeX data with corresponding effect sizes w. Within the model, TIGAR-V2 estimates w for each gene using a popular nonparametric Bayesian Dirichlet process regression (DPR) procedure. Second, using estimates of cis-eQTL effect sizes w^ from each tissue, we applied TIGAR-V2 to impute the genetically regulated gene expression (GReX) of gene g in our FXTAS sample as G d ReX = G test w^, where G test represents the corresponding cis-SNP genotypes for gene g in the FXTAS dataset). Finally, we tested the association between GReX and FXTAS case/control status using a logistic regression model that further adjusted for the covariates of FMR1 repeat length (both linear and quadratic terms) and the top 2 PCs of ancestry.

Genetic screen using Drosophila model
As previously described, the transgenic fly line expressing r(CGG)90 was established20. The GMR-GAL4 and UAS-TRiP lines were obtained from Bloomington Stock Center. All Drosophila lines were maintained, and crosses were performed in the standard medium at 25°C. To determine the genetic interaction between CGG repeats and candidate genes, eye phenotypes of RNAi lines crossed with the CGG repeat stable line were compared with the eye phenotype of the CGG repeat stable line. The screen was performed by scoring eye phenotype in progeny with n > 10, which was visualized using light microscopy and confirmed with scanning electron microscopy (SEM).

Generation of Drosophila lines overexpressing human PRKCG
Full-length human PRKCG cDNA was obtained from a tagged ORF clone (OriGene Technologies, PRKCG Human Tagged ORF Clone, RG208502). The PRKCG open reading frame subcloned into the pUAST Drosophila expression vector77 (Addgene) using standard restriction enzyme–based cloning. Both the PRKCG insert and the pUAST backbone were digested with Not1 and Kpn1 (New England Biolabs, R3189S and R3142S) at 37°C for 30 min and ligated using T4 DNA ligase (New England Biolabs, M0202) at 16°C overnight, followed by heat inactivation at 65°C for 10 min. The resulting construct (pUAST-hPRKCG) was verified by Sanger sequencing. For transgenesis, the validated plasmid was injected into embryos for germline transformation using P-element–mediated insertion. Embryo injections and founder selection were performed by BestGene Inc. (Chino Hills, CA; https://www.thebestgene.com). Transgenic UAS-hPRKCG lines were established and maintained using standard Drosophila genetic crosses. Overexpression of human PRKCG in the FXTAS fly model was achieved by crossing UAS-hPRKCG flies with the appropriate GAL4 driver lines as indicated in the figure legends.

Cell lines and culture
Murine Neuro2A (N2A) cells (ATCC, CCL-131), derived from a male Mus musculus, were cultured in Eagle’s Minimum Essential Medium (EMEM) (Corning; 10-009-CV) with 2.5% fetal bovine serum (FBS). Cells were maintained at 37°C with 5% CO₂ and passaged using standard trypsin–EDTA dissociation.

Skin biopsy samples were obtained from individual diagnosed with FXTAS (two female and one male) and age- and sex-matched health controls. CGG repeat sizes were verified by short tandem repeat (STR) based genotyping performed at Emory Integrated Genomics Core. All studies were conducted under an approved IRB protocol, and informed consent was obtained from all participants. These fibroblast lines are not commercially available. Fibroblasts from skin biopsies were reprogrammed into iPSCs using a CytoTune-iPS 2.0 Sendai reprogramming kit (Thermo Scientific, A16517) according to the manufacturer’s instructions to generate integration-free iPSCs. After transduction, cells were maintained in fibroblast medium [DMEM supplemented with 10% FBS and 2 mM L-glutamine (Thermo Fisher, 25030081). Transduced fibroblasts were transferred onto mouse embryonic fibroblast (MEF) dishes in fibroblast medium. Emerging iPSC colonies were monitored and manually picked for expansion. Established iPSCs were transferred to Matrigel-coated six-well plates, maintained in mTeSR1 medium (STEMCELL Technologies, 85850), and passaged with ReLeSR (STEMCELL Technologies, 05873). NPCs were differentiated from hiPSC as described in refs. 78, 79 with some modifications. Colonies of hiPSC colonies were detached using collagenase IV at 1 mg mL⁻¹ (Thermo Fisher, 17104019) for 1 h and transferred to low-adhesion dishes in EB medium consisting of DMEM/F12 (Corning, 10-092-CV), 20% KnockOut Serum Replacement (Thermo Fisher, 10828028), 2 µM dorsomorphin (Tocris, 3093), and 2 µM A-83-01 (Tocris, 2939). Embryoid bodies (EBs) were cultured for 7 days with daily half-medium changes. EBs were plated onto growth factor–reduced Matrigel-coated dishes (Corning, 354230) and cultured for 7 days in DMEM/F12 supplemented with N2 (Thermo Fisher, 17502048) and laminin (µg/mL; Sigma, L2020) to induce neural rosette formation. Neural rosettes were manually isolated on day 14 and expanded for 7 days in NPC medium composed of a 1:1 mixture of DMEM/F12 and Neurobasal medium (Thermo Fisher, 21103049) supplemented with N2, B27 minus retinoic acid (Thermo Fisher, 12587010), bFGF (20 ng/mL; PeproTech, 100-18B), human LIF (10 ng/mL; PeproTech, 300-05), CHIR99021 (3 µM; Tocris, 4423), and A-83-01 (2 µM; STEMCELL Technologies, 72024). Y-27632 (10 µM; Tocris, 1254) was included only during the first 24 h after replating. The resulting neural progenitor cells were collected and used for downstream assays.

Cell viability assay
Predesigned shRNAs targeting the coding sequence of mouse Prkcg (Sigma; TRC Clone IDs TRCN0000022684, TRCN0000022687, TRCN0000022688) in the pLKO.1 lentiviral vector were obtained. N2a cells were plated in EMEM (Corning, 10-009-CV) containing 2.5% FBS. Twenty-four hours after plating, cells were transfected with the 5′ UTR CGG99×FMR1-EGFP plasmid, which expresses 99 CGG repeats within the human FMR1 5′ UTR fused in-frame with EGFP, or with the empty vector control (pcDNA3-EGFP), using GenJet (SignaGen SL100488; 300 ng per well of a 96-well plate). After additional 24 h, cells were transfected with either the PRKCG expression vector or a mixture of the three shRNAs (300 ng per well). Four hours after transfection, the media was replaced with new EMEM. Ninety-six hours after transfection, cell viability was measured using CellTiter Blue (Promega, 8081) according to the manufacturer’s instructions. The fluorescence of resorufin converted from resazurin by live cells was recorded with FLUOstar Omega microplate reader (BMG LABTECH) at fluorescence (560 Excitation/590 Emission). The average reading value of triplicate empty wells and blank wells with culture media without cells were subtracted from the reading value. The fluorescence values of treated groups were further normalized with empty control vector or control shRNA value.

Quantitative real-time RT-PCR
Total RNAs from mouse tissues or cells were isolated by TRIzol™ Reagent (Invitrogen) according to the manufacturer’s instructions. cDNAs were synthesized from 1 μg of total RNA using the SuperScript® III First-Strand Synthesis System. All primers and TaqMan Assays used for RT-PCR are listed in Supplementary Data 7. Primer sequences for quantifying transgene expression in the conditional mouse models were obtained from Sellier et al.7 and synthesized by IDT. PRKCG expression in mouse cortex and NPCs were quantified using TaqMan Assays. To measure expression of the transgene, qPCR was performed using PowerUp™ SYBR™ Green Master Mix (Applied Biosystems, A25742) on a QuantStudio 3 instrument (Applied Biosystems) with the following cycling conditions: 94°C for 15 min followed by 50 cycles of 94°C for 15 s, 58°C for 20 s, and 72°C for 20 s. PRKCG expression was quantified using TaqMan™ Universal PCR Master Mix (Applied Biosystems, 4364340) with standard parameters: 95°C for 10 min C followed by 40 cycles of 95°C for 15 s and 60°C for 1 min. The mRNA levels of Rplpo or Gapdh were used as an internal control. Relative expression was calculated using the ΔΔCt method with GraphPad Prism 9 software.

Statistics and reproducibility
Statistical analysis was performed using GraphPad Prism 9.0 software. Sample sizes were selected based on prior experience with similar mouse, cellular, and genetic studies and are reported in the figure legends and Methods. To assess for differences between two groups, unpaired Welch’s t-test or two-way ANOVA test followed by Šídák’s multiple correction was performed otherwise stated. Correlations between logFCs of DEGs in bulk sequencing (cortex) and logFC of snRNA-seq data within cell types were assessed using two-sided Spearman’s rank correlation tests. All data in bar charts were presented as mean ± SD unless otherwise stated. No data were excluded from the analyses. Within each genotype, sex- and age- matched mice were assigned to experimental groups randomly. Unless otherwise specified, all experiments were performed with at least three independent biological replicates, and all results were successfully reproduced across independent replicates. Histological analyses and behavioral assessments were performed blinded to treatment groups.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "FXTAS is a neurodegenerative disorder with an incomplete penetrance, highlighting the importance of identifying its modifier genes. The authors used multi-omics analysis and transcriptome-wide association study (TWAS), a technique that has been used to identify risk genetic factors in AD, PD and ALS, to discover a striking dysregulation of gene expression in inhibitory neurons in FXTAS mice. Then they expressed CGG repeats in GABAergic neurons only and found this conditional mouse model recapitulates key pathologic hallmarks of FXTAS. Among many dysregulated mRNAs in GABAergic neurons that are also targets of hnRNPA2/B1, an RNA-binding protein sequestered by CGG repeat RNA is PRKCG, whose upregulation is neurotoxic and seems to be partially responsible for FXTAS pathogenesis. These mostly well-executed experiments have provided some novel mechanistic insights into FXTAS pathogenesis, highlighting a key role for GABAergic neurons and suggesting PRKCG knockdown as a potential therapeutic avenue.\nMinor comments:\n1. The abbreviation PM is only used once. To make it easier to read, maybe it is better to just say “premutation carriers” in line 61. Similarly, in Figure 1 b,c, it is better to spell out the full names of these brain regions.\n2. In Line 128, please describe the transgenic mouse model in more detail, such as what is the promotor etc. This piece of information is essential to help readers understand the cell-type differences reported in this study.\n3. Please state in Figure 1 legend how many mice were used for each genotype. I could not find this information in Methods section neither. I tried but also could not find how many animals were used for snRNA-seq analysis.\n4. These two sentences from line 197-200 should be deleted, because the reduction in the % of oligodendrocytes is not statistically significant. Thus, the conclusion that “these findings provide valuable insights into the cellular heterogeneity associated with FXTAS pathogenesis” is inaccurate.\n5. In Line 208-209, it is unclear what the phrase “a repressed cellular state” means.\n6. In the “Profound gene expression dysregulation in inhibitory neurons of FXTAS mice” section, if possible, the authors need to provide some data showing the relative expression levels of transgene in inhibitory neurons, versus excitatory neurons and non-neuronal cells. One wonders whether the observed gene expression dysregulation is due to higher transgene expression and/or sensitivity of inhibitory neurons.\n7. Related to Comment #6, it remains possible that the observed difference in disease phenotypes between FMRpolyG+RNA; Gad2 mice and FMRpolyG+RNA; CaMKIIα mice (Fig. 4) is due to expression levels. If this is technically different to document, maybe the authors can state in the Discussion section that this is a limitation or caveat of the current study.\n8. In Fig. 6e and Fig. 7f, the authors present one image for each modifier gene. As we know, the eye phenotype varies among different flies. It would be more convincing to readers if the authors can please present a quantification panel.\n9. On Page 12, the authors state that “Knockdown of these genes using the UAS-GAL4 system in the fly eye revealed that 9 genes exhibited genetic modulation of CGG toxicity.” It is unclear whether PRKCG is one of them.\n10. In line 368, the authors stated that “Given the absence of a specific PRKCG ortholog in fly, we cloned the full-length cDNA of human PRKCG (hPRKCG) into a pUAST Drosophila vector”, but in the Abstract, they also made a contradictory statement that “functional screening in Drosophila further establishes PRKCG and its fly ortholog, aPKC, as potent modulators of CGG-associated neurotoxicity”. Please clarify whether they think aPKC is fly ortholog of PRKCG or not. Does aPKC knockdown rescue neurodegeneration in the fly model? The authors can also perform a relatively easy experiment to knockdown PRKCG in cultured mammalian neurons and see whether it rescues FMRpolyG+RNA toxicity.", "reviewer_2": "Jin, Cao, Ma and colleagues used a multi-omics approach to investigate the pathophysiological basis of FXTAS. There are a lot of different experiments and many new findings. The main contributions of the paper are: (1) the rich transcriptomic data that uncovers large age-differences between FXTAS model mice and controls (which fits the more advanced pathology in older animals), as well as region-specific differences (although it’s unclear if those might just reflect overwhelming differences in gene expression between regions; see below); (2) snRNAseq data that points to GABAergic neurons as the cell type with the most DEGs (mostly downregulated); (3) an interesting new analysis of TWAS data that identify novel dysregulated genes, followed by a RNAi knockdown screen in Drosophila; and (4) the identification of Prkcg upregulation in FXTAS as a potential target for intervention.\nConsidering how little is known about FXTAS, these contributions seem important. The authors deploy several different approaches, which is laudable. I appreciate how ambitious this project was, how many different experiments were performed. Still, the manuscript should be strengthened with additional analyses and a couple of new experiments, as well as changes to the text to clarify things, or tone down some interpretations. The hope is that such changes will help connect what are otherwise somewhat disjointed results and superficial analyses.\nAdditional experiments:\n1) The increase in GABAergic neuron numbers in FXTAS mice from scRNAseq counts needs confirmation: they will need to count cell densities in PFC and ideally also in CBM (it seems GABAergic neuron fate is different in CBM than in CX)\n2) The Prkgc dysregulation finding needs validation at the protein level in mouse neurons (both in CBM and CX). The differences between genotypes seems subtle\n3) A link between Prkgc overexpression and a FXTAS phenotype in mice is missing; they will need to either show that overexpression in GABAergic neurons of WT mice leads to loss of PC neurons in CBM, motor deficits, or that lowering Prkgc (CRISPR or shRNA) in FXTAS mice rescues their phenotypes.\nAdditional analyses:\ni. The behavior data has a worrisome potential confound. First, they will need to compare rotarod data in a subset of FXTAS mice with similar weights as WT controls. Also, they need to correlate weight with latency to fall in rotarod. If the correlation exists (esp. in WT mice) and there is no difference in latency between mice with similar weights, then more studies of motor behavior will be needed that are not sensitive to weight differences (grid walk, beam walking, grip testing); see below for comments on Fig. 4.\nii. Quantify PC density for Fig. 4, preferably at multiple ages, in Gad2-Cre and Nestin-Cre, but also in Camk2-Cre mice\niii. In the snRNAseq data, was Prkgc a DEG in Exc or GABAergic neurons, and if so, in which direction (up or down)?\niv. Better statistics throughout:\n- in line 133-4 (Fig. 1b), is the difference in # DEGs between regions significant? If not, then remove that statement.\n- In line 149-50: how do they know that any of the overlap, particularly the greater overlap for downregulated genes, is not due to chance alone (given more DEGs were downregulated in all 3 regions). They should use Odd Ratio tests to assess this. This applies to Fig. 5c, e and Fig. 7a-b as well.\n- In Fig. 4c-f the stats should be ANOVAs (if data is normally distributed) and they should state whether there is an age effect (it looks like both latency-to-fall and weight increase with age)\n- State which statistical test was used for each graph in the figure legends.\nAdditional comments for every figure:\nFigure 1:\n- Fig. 1b-e: Would also be nice to show correlations of fold change for all DEGs in CX vs. CBM\n- Why not show volcano plots for bulk RNAseq?\n- In 1f-g, is the analysis done for both ages or just oldest mice?\n- Fig. S1a, c: Would it be possible to expand the heat maps in y-axis and name at least the top 20 up and down DEGs? Also point out some interneuron-specific genes\nFigure 2:\n- The rationale for doing snRNAeq in PFC but not CBM is not explained\n- What does Inh_2 cluster in PFC correspond to – does it express Pvalb, Sst, or Vip? Consider splitting “Inh” in Fig. 2d according to the 2 inh categories.\n- They should validate the higher number of GABAergic neurons in FXTAS mice by performing in situ hybridization (Gad2) or immunos for inhibitory markers (GABA, PVALB) and count densities in PFC (or use the Tagger mice with their RFP tag). Otherwise, it seems unwise to make any conclusions about this subtle difference from counting nuclei alone (and yet the authors refer to it multiple times in Results and Discussion). It’s particularly perplexing given that, in the CBM, the main GABAergic cells (Purkinje neurons) have died.\nFigure 3:\n- In Fig. 3f aren’t most GO terms shared by Exc and Inhib neurons? this is not mentioned in the Results.\n- In Fig 3b why are there DEGs near zero (minimal change) for Inh neurons? (shouldn’t it look like Exc neurons because of some statistical threshold?\n- Data in Fig 3d seems neither here nor there given extremely low correlations. I would recommend moving this to suppl and merging Figs 2 & 3 into a single figure for snRNAseq.\nFigure 4:\n- The main result is that as the Gad2-Cre mice get older and gain a lot of weight, their latency to fall shortens. This is not mentioned by the authors and yet greater weight alone could be the reason why they fall sooner (the Camk2-Cre cohort is not impaired in rotarod because the don’t get fat). They should show correlation graphs between weight and latency for all groups and ages, they also compare Gad2-Cre mice with control mice of the same weight. If this phenotype is indeed due to weight, then a lot of the excitement for this paper goes away and they’ll need to find other ataxia phenotypes.\n- They should make the y-axes of graphs in Figs. 4c & 4d the same (up to 300 sec in both, and without cutting the axis; no reason to do that). It seems that controls had longer latencies to fall in 4d for some reason. What’s the p value for 5 months in 4d?\n- Same issue with y-axis matching applies to Figs 4e & 4f and Figs S3a-b and S3c-d\n- Fig. 4g: The images show dramatic loss of Purkinje cell in Gad2-Cre mice but there is no quantification of PC density to get a sense of how extensive this is and whether it indeed matches loss seen in Nestin-Cre mice. Also, they need to show data for Camk2-Cre mice; otherwise, we cannot conclude that overexpression in GABAergic neurons is unique in reproducing key phenotypes of FXTAS mice.\nFigure 5:\n- Can they confirm cell type-specificity of TRAP-seq by showing expression of genes that are uniquely expressed in each, like what they did for snRNA-seq (Fig. 2d).\n- There are important differences between these data and the ones from bulk sequencing: Is it unusual that they saw more DEGs in cortex this time (compared to Fig 1)? In TRAPseq, why do they no longer see an increase in # DEGs with age, as degeneration progresses? (with bulk sequencing they did)\n- It’s generally accepted to interpret RNAseq results in the context of phenotypes, but the authors seem to stretch this a bit too much; in L299-300 it’s an exaggeration to cherry-pick a couple of GO terms (locomotor behavior or skeletal muscle contraction) from a longer list to argue that this explains the rotarod deficits of the mice (even though they are due to obesity). They should tone this down a bit.\nFigure 6:\n- It’s a nice screen, but none of the candidates are pursued further\n- Prosbeta5 should be removed since the RNAi is toxic to control flies\nFigure 7:\n- The levels of Prkcg are not changed in FXTAS mice vs. controls. Elevated Prkcg mRNA in human NPCs is from only n=3 mice\n- They should mention adjusted p values for the Prkcg in legend of Fig 7a-b\n- Isn’t it strange that Prkcg is down in the CBM in the bulk RNAseq, but in TRAPseq it is up in GABAergic neurons?\n- The counts for CTX at 3-4 months in Fig 7A do not seem to match with those of Fig 1B (Fig1B CTX 3-4m is 127, Fig7A is 496); same for CB (Fig 1b 162, Fig 7A is 576)\n- Fig 7b. The counts for 3-4 months CB Fig.5e 328; Fig 7b 523; CTX 3-4 months Fig 5e 728 fig 7b 811. the results match for 6-7 mont\nMinor:\n- L151: “widespread neuronal death” needs a reference\n- Fig. S1: ‘z-score’ misspelled in color scale bar in a-d\n- L166 “Overpenetration” – do they mean “overrepresentation”? also is it “ion transport” or “iron transport” (different things in figure vs. text)\n- L192: they should introduce the clustering method for Fig 2f\n- Fig. S5d: “adult” is misspelled under adult locomotory behavior; “Negative” is misspellled.\n- Fig. 4f-h: what age were immunos done?\n- How many mice were used for TRAPseq?\n- Fig 5: the labels for DEGs in volcano plots are a bit messy: the lines don’t always reach a dot and the words are on top of each another\n- Fig 7B error bar is off center for Ttyh1.\n- Line 349: I count 6 TWAS gene that overlap with CTX not 5", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "In this study, Yulin Jin et al. have made great effort to conduct multi-omics strategy combined with TWAS to decode the molecular mechanisms of FXTAS, and identified a novel prominent driver gene of FXTAS pathogenesis, which is of significance for guiding its clinical practice. However, there are also a number of issues which should be addressed.\n1. The authors employed different criterion for the identification of DEGs at the onset of phenotype (p<0.01), late progression stage (FDR<0.05) and for specific cell types (adjusted p-value<0.05), is it reasonable? Are these identified DEGs comparable?\n2. I suggest the authors to briefly describe the sample size used in the omics data analysis in the Results section, such as the transcriptomic profiling and snRNA-seq data analysis.\n3. The prior marker genes for cell-type annotation that the study used should be listed in the Method section so that other researchers can repeat these relative analyses.\n4. Which statistical test method does the authors use to compare cell proportions between FXTAS and control animals, it seems that the difference of inhibitory neurons is not that remarkable. The method should be described before the p-value.\n5. Which method was used for the CGG repeat toxicity evaluation?\n6. Why does the authors select TIGAR-V2 to conduct TWAS analysis, to my knowledge, several tools such as PrediXcan and FUSION are more popular.\n7. The colors for distinguishing the FXTAS and Control groups in Figure 3e are suggested to be changed, which are easily confused with the heatmap gradient colors. Similarly, the white boxes in Figure 4c are not ideal.\n8. Is it necessary to list those genes with p-value or adjust larger than 0.05 in supplementary tables.\n9. There are some syntax errors in the manuscript, such as ‘We used TIGAR-V2 69was used to’ in line 653 on page 23.", "reviewer_5": "This paper “Integrative transcriptome-wide association reveals PRKCG-linked GABAergic dysfunction in fragile-X associated tremor/ataxia syndrome”.(FXTAS) by Jin and colleagues describes the detailed molecular analysis of mouse models for this late-onset neurodegenerative disease that is caused by CGG repeat expansion. `Both toxic expanded-repeat RNA-gain of function as well as RAN translation have been suggested to contribute to pathogenesis, though the relative importance of each of these phenomena is under intense debate. The authors here provide multi-omics data suggesting both mechanisms are important for pathogenesis. Moreover, using coitional knockouts, the authors find that expression of the repeat in GABAergic neurons alone is sufficient to drive FXTAS pathogenesis. Finally, PRKCG is identified as a novel modifier of the disease as confirmed in fly studies.\nThe study represents an enormous amount of work and was well designed and executed and so contributes to our current understanding of FXTAS pathogenesis.\nMajor comments:\nThe authors show that increased expression of PRKCG leads to a robust detoriation of the rough eye phenotype in flies and conclude this gene is a modifier of FXTAS. However, in order to prove this is a true modifier, an elegant experiment would be to show that a deletion of this (or related) gene results in an amelioration of the FXTAS phenotype.\nThe study could mention the level of expression of the toxic RNA in their model, as this has an important effect on the clinical presentation. In addition, while a repeat construct of 90 repeat units is used, it may be worthwhile to measure at least in some of the experiments the repeat length in the tissues under investigation, as the repeat may inherit unstably, and such repeat-length differences may influence the outcome of the analysis.\nMinor comments:\nL 67 “or a slight reduction in FMRP protein levels”\nAdd this is propotional/related to the amount of length increase of the CGG repeat\nL74 “This leads to the …….FMRployG.”\nAddional RAN translation products are also made\nL134 “…with the cortex exhibiting a subsequent level of observation”\nMeaning of this sentence not clear\nL142 “Hist1he1”\nPerhaps relevant to mention this is an established ID gene\nL151 “the widespread overlap … in upregulated genes”\nExplain to the reader l255(in half a sensence or so) why this is the case\nL255-259 “on the difference in phenotypic presentation between males/females”\nThese findings to some extend parallel the gender diffences in FXTAS patients. However, in man, the locus is on the X, whereas here the expansion is autosomal perhaps suggesting this difference may not solely be due to the presence of the expansion on the X chromosome. This could be mentioned in this paper (here or in the discussion)"}, "author_response": "Response to Reviewers’ Comments\nWe would like to express our sincere gratitude to the reviewers for their time and thoughtful\ncomments on the manuscript. Following their suggestions, we have carried out a substantial\nnumber of experiments and analyses to address their concerns. Below are detailed responses\nto each reviewer’s comments.\nReviewer #1 (Remarks to the Author):\nFXTAS is a neurodegenerative disorder with an incomplete penetrance, highlighting the\nimportance of identifying its modifier genes. The authors used multi-omics analysis and\ntranscriptome-wide association study (TWAS), a technique that has been used to identify risk\ngenetic factors in AD, PD and ALS, to discover a striking dysregulation of gene expression in\ninhibitory neurons in FXTAS mice. Then they expressed CGG repeats in GABAergic neurons\nonly and found this conditional mouse model recapitulates key pathologic hallmarks of\nFXTAS. Among many dysregulated mRNAs in GABAergic neurons that are also targets of\nhnRNPA2/B1, an RNA-binding protein sequestered by CGG repeat RNA is PRKCG, whose\nupregulation is neurotoxic and seems to be partially responsible for FXTAS pathogenesis.\nThese mostly well-executed experiments have provided some novel mechanistic insights into\nFXTAS pathogenesis, highlighting a key role for GABAergic neurons and suggesting PRKCG\nknockdown as a potential therapeutic avenue.\nMinor comments:\n1. The abbreviation PM is only used once. To make it easier to read, maybe it is better to\nsay “premutation carriers” in line 61. Similarly, in Figure 1 b,c, it is better to spell out the full\nnames of these brain regions.\nWe appreciate this helpful suggestion. (1) We have replaced “PM” with “premutation” in line\n59. (2) In Figure 1a, we have introduced the abbreviations CTX, CB, and HP to denote\ncerebral cortex, cerebellum, and hippocampus, respectively. Accordingly, Figures 1b and 1c\nnow use CTX, CB, and HP to label each brain region, ensuring full consistency with Figure\n1a.\n2. In Line 128, please describe the transgenic mouse model in more detail, such as what is the\npromotor etc. This piece of information is essential to help readers understand the cell-type\ndifferences reported in this study.\nWe appreciate this helpful suggestion. We have added a more detailed description of the\ntransgenic mouse model in lines 126-131.\n3. Please state in Figure 1 legend how many mice were used for each genotype. I could not find\nthis information in Methods section neither. I tried but also could not find how many animals\nwere used for snRNA-seq analysis.\nWe appreciate this helpful suggestion. We have included sample size descriptions for Fig. 1\n(bulk RNA-Seq) and Fig. 2 (snRNA-Seq) in the Figure legends. For bulk RNA-Seq, Brain\ntissues were collected from 4 FXTAS mice and 4 littermate controls, except for the cortex at\n6–7 months, where 3 FXTAS mice were analyzed. The snRNA-seq was performed on PFC\ntissues from 2 FXTAS mice and 2 littermate controls.\n4. These two sentences from line 197-200 should be deleted, because the reduction in the %\nof oligodendrocytes is not statistically significant. Thus, the conclusion that “these findings\nprovide valuable insights into the cellular heterogeneity associated with FXTAS pathogenesis”\nis inaccurate.\nWe thank the reviewer for this helpful suggestion. We agree that the original wording\noverstated the interpretation. To address this, we removed the description for\noligodendrocytes and revised the text to retain only a brief note on potential dissociation\nsensitivity. In addition, we have modified the conclusion (Line 202-203) in the revised\nmanuscript to “the changes in cell-type ratios between FXTAS mice and controls may reflect\na disease-associated cellular change linked to the expression of expanded CGG repeats in the\nbrain”.\n5. In Line 208-209, it is unclear what the phrase “a repressed cellular state” means.\nThank you for highlighting this. To improve clarity, we have revised the wording in lines\n210–212 to “indicative of a broad suppression of transcriptional programs compared to other\ncell types (Fig. 3a, b).”\n6. In the “Profound gene expression dysregulation in inhibitory neurons of FXTAS mice”\nsection, if possible, the authors need to provide some data showing the relative expression levels\nof transgene in inhibitory neurons, versus excitatory neurons and non-neuronal cells. One\nwonders whether the observed gene expression dysregulation is due to higher transgene\nexpression and/or sensitivity of inhibitory neurons.\nWe thank the reviewer for this important suggestion. We agree that distinguishing between\ndifferential transgene expression and intrinsic neuronal susceptibility is critical for\ninterpreting our findings. Our snRNA-seq libraries were generated using a 3′-capture\nprotocol and aligned to the mouse reference transcriptome, which does not natively include\nthe expanded CGG-repeat transgene. To address this, we appended the GFP sequence (fused\nin-frame with the human FMR1 5′UTR transgene) to the reference genome and reprocessed\nthe data using our established pipeline (Methods). Using this approach, we quantified\ntransgene expression by calculating the average GFP expression per nucleus within each cell\ntype in the FXTAS samples. Summed GFP counts were normalized to the number of nuclei\nper cell type to estimate mean transgene expression levels. As shown below (included in\nSupplementary Fig. S2e), GFP-positive nuclei were detected in both excitatory and inhibitory\nneurons, as well as in astrocytes and oligodendrocyte precursor cells (OPCs), but were rare\nin microglia and oligodendrocytes and absent in endothelial cells. Compared with inhibitory\nneurons, astrocytes, OPCs, and excitatory neurons exhibited relatively higher average GFP\nexpression. These results demonstrate that transgene expression is broadly distributed\nacross neuronal and glial populations derived from 60\nNestin-positive progenitors (Bernal et al., 2018).\ndysregulation observed in inhibitory neurons is unlikely\n20 to be explained simply by transgene dosage alone but\ninstead suggests an intrinsic susceptibility of this\nx cit at or\nI\ny\nn hi bit o\nA\nr\ny\nstr o c yt e\ns\nO P\nC\nMi cr o g\ne\nl\nn\ni\na\ndr o c y\nE\nt e\nn\ns\nd ot h eli\nal n\ns\nta\nt\ne\nu\nr\nu\ng\nd\nr\ne\ni\no\ne\nt\nn\ne\ns\na\nd\nu\nl\ns\ns\np\ne\nin\nq\no\ng\np\nu\nu\ne\nc\nn\nl\ne\na\nc\nl\nt\ni\nl\ni\nn\n–\no\ng\nt\nn\ny p\no\nt\nr\ne\no\n–\ns\nr\nC\np\ne\na\ns\nG\no\nt\nG\ni\nl\na\nv\n-\nl\ne\nr\nd\ne\nR\np\nN\na\ne\np\na\nA\nt\np r\ni\nt\no\nm\no\na\nx\na\nc\ni\nh\ng\nc\ni\ne\ni\nn\nt\ns\ny\ng\n,\n.\n,\ns\nw\nF\nuc\nu\nil\nh\nt\nl\nu\nb\na\nr\ne\ne\ns\nd\no\ng\nOli valuable for further clarifying transgene expression\nlevels across individual cell types.\n2\nfo\necnadnuba\nevitaleR\n)%(\nstnuoc\nPFG\n93\n03\n84\n24\n4 4\ntherapeutic implications. Cellular and Molecular Life Sciences, 75(12), pp.2177-2195.\n7. Related to Comment #6, it remains possible that the observed difference in disease\nphenotypes between FMRpolyG+RNA; Gad2 mice and FMRpolyG+RNA; CaMKIIα mice (Fig.\n4) is due to expression levels. If this is technically different to document, maybe the authors\ncan state in the Discussion section that this is a limitation or caveat of the current study.\nWe thank the reviewer for raising this important point. 1) The FXTAS transgene (5′UTR\nFMR1 (CGG)99x) is driven by the strong CAG promoter (Sellier 2017), which is known to\nrobustly drive gene expression in brain (Yaguchi 2013; Mo 2015; Thirtamara Rajamani 2021).\nAs shown in Fig. 4b, qRT-PCR from bulk cortex and cerebellum indicates lower transgene\nlevels in FMRpolyG+RNA; Gad2-Tagger than in FMRpolyG+RNA; CaMKIIα-Tagger mice,\nconsistent with the relative abundance of inhibitory versus excitatory neurons. 2) A direct,\nabsolute comparison of transgene levels across these Cre lines is technically challenging, and\nit is difficult to determine to what extent expression level contributes to the observed\nphenotypes. First, the inhibitory/excitatory ratio differs across brain areas and can shift with\ndevelopment and neurodegeneration. Second, Gad2-Cre and CaMKIIα-Cre are widely used,\nhigh-efficiency neuronal drivers (Taniguchi 2011; Nguyen 2016), but their recombination\npatterns can vary by brain region and developmental stage (Dragatsis et al., 2000; Luo et al.,\n2020), meaning an observed “expression level” reflects both dose per cell and the proportion\nof cells recombined. 3) Together with our breeding design, qRT-PCR detection, GFP-based\nsnRNA-seq analysis and the expected phenotypes (e.g., Calbindin-based Purkinje-cell loss in\nGad2/Nestin but not CaMKIIα cohorts), these data support that transgene induction was\nsuccessful in both lines. Future work using cell-type–resolved quantification (e.g., targeted\nsequencing or spatial RNA imaging) will be essential to address this question. We have\nincluded this in the Discussion line 523-534.\nTaniguchi, H., He, M., Wu, P., Kim, S., Paik, R., Sugino, K., Kvitsiani, D., Fu, Y., Lu, J., Lin,\nY., et al. 2011. A Resource of Cre Driver Lines for Genetic Targeting of GABAergic\nNeurons in Cerebral Cortex. Neuron 71, 995–1013\nNguyen, A.Q., Dela Cruz, J.A.D., Sun, Y., Holmes, T.C., and Xu, X. 2016. Genetic cell\ntargeting uncovers specific neuronal types and distinct subregions in the bed nucleus of\nthe stria terminalis. J. Comp. Neurol. 524, 2379–2399\nDragatsis, I. and Zeitlin, S., (2000). CaMKIIα-Cre transgene expression and recombination\npatterns in the mouse brain. genesis: Genesis: The Journal of Genetics and\nDevelopment, 26(2), pp.133-135.\nLuo, L., Ambrozkiewicz, M.C., Benseler, F., Chen, C., Dumontier, E., Falkner, S., Furlanis,\nE., et al. 2020. Optimizing nervous system-specific gene targeting with Cre driver lines:\nprevalence of germline recombination and influencing factors. Neuron, 106(1), pp.37-65.\nMo, A., Mukamel, E.A., Davis, F.P., Luo, C., Henry, G.L., Picard, S., Urich, M.A., Nery, J.R.,\net al. 2015. Epigenomic signatures of neuronal diversity in the mammalian\nbrain. Neuron, 86(6), pp.1369-1384\nThirtamara Rajamani, K., Leithead, A.B., Kim, M., Barbier, M., Peruggia, M., Niblo, K., et\nal. 2021. Efficiency of cell-type specific and generic promoters in transducing oxytocin\nneurons and monitoring their neural activity during lactation. Scientific Reports, 11(1),\np.22541\n3\n8. In Fig. 6e and Fig. 7f, the authors present one\nimage for each modifier gene. As we know, the\neye phenotype varies among different flies. It\nwould be more convincing to readers if the\nauthors can please present a quantification\npanel.\nThanks for your concern. We have added\nquantification panels for Fig.6e and Fig.7f, as\nbelow in a and b, respectively. Genotypes are\ndepicted below each column. The number in\neach column indicates the percentage of flies\nwith enhanced rough eye phenotype for each\ncross. We also added this results in the\nSupplementary Fig. S11.\n9. On Page 12, the authors state that “Knockdown of these genes using the UAS-GAL4 system\nin the fly eye revealed that 9 genes exhibited genetic modulation of CGG toxicity.” It is unclear\nwhether PRKCG is one of them.\nThank you for raising this important point. PRKCG was not among the nine genes that\nexhibited genetic modulation of CGG toxicity through knockdown in Fig. 6e. The nine genes\nare RBM8A, SLIT3, CNOT3, VARS, PSMB1, PDCD2, U2AF2, BRSK1, and CCDC124. To\nimprove clarity, we have added the corresponding human gene names for each Drosophila\northolog to the legend of Fig. 6e.\n10. In line 368, the authors stated that “Given the absence of a specific PRKCG ortholog in fly, we cloned the full-length cDNA of human PRKCG (hPRKCG) into a pUAST Drosophila vector”, but in the Abstract, they also made a contradictory statement that “functional screening in Drosophila further establishes PRKCG and its fly ortholog, aPKC, as potent modulators of CGG-associated neurotoxicity”. Please clarify whether they think aPKC is fly ortholog of PRKCG or not. Does aPKC knockdown rescue neurodegeneration in the fly model? The authors can also perform a relatively easy experiment to knockdown PRKCG in cultured mammalian neurons and see whether it rescues FMRpolyG+RNA toxicity.\nThanks for this important comment. 1) aPKC (gene PKC98E) is the closest PKC-family\nhomolog of mammalian PRKCG in Drosophila based on sequence/domain similarity; however,\nit is not a one-to-one, high-confidence ortholog. Accordingly, we have revised the Abstract to\nreplace “its fly ortholog” with “the fly PKC ortholog, aPKC.” Because FlyBase ortholog\nmapping does not support aPKC as a high confidence ortholog\n1.5\nof PRKCG, we did not pursue aPKC knockdown in flies for this study.\n2) To address the reviewer’s suggestion for a complementary\n1.0\nexperiment, we performed shRNA-mediated knockdown of Prkcg in\nmouse Neuro2A (N2A) cells expressing CGG-repeat RNA. Cells were\n0.5\ntransfected with 5′UTR-CGG99-FMR1-EGFP (99 CGG repeats in the\nhuman FMR1 5′UTR) or a control vector. As reported previously\n0.0\nL L G (Kong et al., 2022), CGG-repeat RNA significantly reduced cell\ns h R N A C /s T h R N A _ C R T N A _ P R K C v k i n a o b c i k li d ty ow . n A s s ig s n h i o f w ica n n t i l n y r th es e c u F e i d g u th re is b C e G lo G w - , i n w du e ce fo d u t n o d x ic t i h ty a t a t P 9 rk 6 c h g\nC G G99 G9 / 9 s h post-transfection (Welch’s t-test, P<0.001). This result supports a\nG\nC\n4\nytilibaiV\nlleC\nevitaleR\n✱✱✱\n✱✱✱\nfunctional role for PRKCG in modulating repeat-associated neurotoxicity and provides\ncomplementary mammalian evidence to support our conclusions. We have included these\nresults in Fig. 7g.\nReviewer #2 (Remarks to the Author):\nJin, Cao, Ma and colleagues used a multi-omics approach to investigate the\npathophysiological basis of FXTAS. There are a lot of different experiments and many new\nfindings. The main contributions of the paper are: (1) the rich transcriptomic data that\nuncovers large age-differences between FXTAS model mice and controls (which fits the more\nadvanced pathology in older animals), as well as region-specific differences (although it’s\nunclear if those might just reflect overwhelming differences in gene expression between regions;\nsee below); (2) snRNAi knockdown screen in Drosophila; and (4) the\nidentification of Prkcg upregulation in FXTAS as a potential target for intervention.\nConsidering how little is known about FXTAS, these contributions seem important. The\nauthors deploy several different approaches, which is laudable. I appreciate how ambitious\nthis project was, how many different experiments were performed. Still, the manuscript should\nbe strengthened with additional analyses and a couple of new experiments, as well as changes\nto the text to clarify things, or tone down some interpretations. The hope is that such changes\nwill help connect what are otherwise somewhat disjointed results and superficial analyses.\nAdditional experiments:\n1. The increase in GABAergic neuron numbers in FXTAS mice from scRNAseq counts needs\nconfirmation: they will need to count cell densities in PFC and ideally also in CBM (it seems\nGABAergic neuron fate is different in CBM than in CX)\nThank you for this helpful suggestion. 1) To address this point, we performed\nimmunofluorescent (IF) staining of GABAergic neurons in the PFC using a validated anti-\nGABA antibody (Thermo Fisher, PA5-32241), together with the neuronal marker NeuN\n(Sigma-Aldrich, MAB377). As shown in Fig. a & b below, FXTAS mice exhibit a significant\nincrease in GABA⁺ neuronal density versus controls (p < 0.01, Welch’s t-test), in agreement\nwith our snRNA-seq results. We have included these results in the Supplementary Fig. S3\nand added a description of IF in the Methods. 2) In the cerebellum, the same anti-GABA\nantibody primarily labels Purkinje cells and their processes (Fig. c). Thus, global GABA\nimmunoreactivity in the cerebellum conflates cell number with per-cell signal. Because our\nFXTAS cohorts show reduced Purkinje cell density (Calbindin-D28k counts; Fig. 4), decreased\nGABA signal in the cerebellum likely reflects PC loss rather than altered interneuron fate.\nAccordingly, we report Purkinje cell density (Calbindin⁺ PCs per mm of Purkinje layer) as\nthe anatomically appropriate cerebellar readout (Fig. 4).\n5\n2. The Prkgc dysregulation finding needs validation at the protein level in mouse neurons\n(both in CBM and CX). The differences between genotypes seem subtle.\nWe appreciate the reviewer’s request for protein-level validation of PRKCG (PKCγ). 1)\nCortex tissue: We performed Western blots on bulk cortex from FMRpolyG+RNA; Nestin-\nCre(+) (FXTAS) mice and control littermates at 3-4 months (onset stage) and 6-7\nmonths (late stage). PKCγ bands (∼78 kDa) were quantified and normalized to GAPDH.\nAcross 3 mice per group at each age, cortical PKCγ levels were significantly increased in\nFXTAS at 3–4 months, whereas no difference was detected at 6–7 months. These data\nindicate an early upregulation of PKCγ in the cortex, suggesting its potential role in driving\ndisease progression. We have included these results in Supplementary Fig. S10. 2)\nCerebellum: PKCγ is highly enriched in Purkinje cells and a key regulator of PC\ndevelopment and function (e.g., Tanaka et al., 1992; Osório et al., 2024; Watanabe et al., 2022).\nBecause the FXTAS cohorts exhibit marked PC loss (Fig. 4), bulk cerebellar Western would\ntherefore be confounded by cell-number differences: an apparent decrease in band intensity\ncould arise from fewer PCs rather than reduced PKCγ level. Notably, our TRAP-seq showed\nincreased Prkgc in GABAergic cerebellar neurons, but bulk protein from whole cerebellum\ncannot resolve such cell-type–specific changes. For these reasons, we did not include CBM\n6\n;)-/xolf(ANR+GylopRMF\n)+(erC-nitseN\nTW\nDAPI GABA NeuN Merge\n0.3\n0.2\n0.1\n0.0\nDAPI GABA NeuN Merge\noly\nG +\nN\nR\ne\nN st A i n ( - f C l/\n+\nr e\n);\n( +) C o ntr\nols\np\nR\nM\nF\n)%(\nsnoruen\ndlebbal-ABAG\naa b\nP = 0.0051\nDAPI GABA NeuN Merge\nDAPI GABA NeuN Merge\n;)+/lf(ANR+GylopRMF\n)+(erC-nitseN\nTW\nc\nWesterns in this revision; instead, we present cortical protein data here and cerebellar PC\ndensity (Calbindin-based, Fig. 4) as complementary readouts.\n3-4months 0.5 6-7months\nFMRpolyG+RNA (fl/+); 0.4 FMRpolyG+RNA (fl/+);\nNestin-Cre(+) Controls Nestin-Cre(+) Controls\nPKCγ 0.3 PKCγ\nGAPDH 0.2 GAPDH\n0.1\n0.0\nR N\nA(fl/\n+); Controls\nG + +) F M N R e p s o ti l n y - Cre( F M R esti n-\nN\nytisnetni\nevitaler\nfo\nnoitaR\nOsório, C., White, J.J., Solé, P.T., Mandemaker, N., Olivero, F., Kirwan, F., de Winter, F., et\nal. 2024. Purkinje cell intrinsic activity shapes cerebellar development and function. biorxiv,\n2024-09.\nWatanave, M., Takahashi, N., Hosoi, N., Konno, A., Yamamoto, H., Yasui, H., Kawachi, M.,\net al. 2022. Protein kinase Cγ in cerebellar Purkinje cells regulates Ca2+-activated large-\nconductance K+ channels and motor coordination. Proceedings of the National Academy\nof Sciences, 119(7), e2113336119.\nTanaka, C. and Saito, N., 1992. Localization of subspecies of protein kinase C in the\nmammalian central nervous system. Neurochemistry international, 21(4), 499-512.\n3. A link between Prkgc overexpression and a FXTAS phenotype in mice is missing; they will\nneed to either show that overexpression in GABAergic neurons of WT mice leads to loss of\nPC neurons in CBM, motor deficits, or that lowering Prkgc (CRISPR or shRNA) in FXTAS\nmice rescues their phenotypes.\nWe appreciate the reviewer’s insightful suggestion to establish a direct link between Prkcg\nexpression and FXTAS-associated phenotypes. We fully agree that generating in vivo models\nwith cell–type–specific Prkcg overexpression or loss-of-function in GABAergic neurons,\nfollowed by detailed histological and behavioral analyses, would provide valuable mechanistic\ninsight. However, such studies require the creation and validation of new transgenic or viral\nlines, extensive breeding, and longitudinal phenotyping, which are beyond the scope and\nfeasible timeline of this revision cycle.\nTo address the reviewer’s concern within the current revision, we conducted\ncomplementary cell-based experiments to test whether modulating PRKCG levels influences\nCGG repeat–induced toxicity in a mammalian system. Specifically, mouse Neuro2A cells\nwere transfected with the FMR1 5′UTR (CGG) –EGFP construct or an empty vector control\n99\n(pcDNA3–EGFP). After 24 hours, cells were co-transfected with either a PRKCG\noverexpression plasmid (Fig. a) or a pool of three shRNAs targeting Prkcg (Fig. b). Cell\nviability was measured at 96 hours using CellTiter-Blue. Consistent with our previous\nfindings (Kong et al., 2022), CGG-repeat RNA significantly reduced cell viability. Importantly,\nPRKCG overexpression further exacerbated this reduction (Fig. a; Welch’s t-test, P < 0.01),\nwhereas Prkcg knockdown markedly rescued viability (Fig. b; Welch’s t-test, P < 0.001).\nThese results provide direct functional evidence that Prkcg levels modulate CGG\nRNA–induced toxicity, thereby supporting its role as a biologically relevant modifier of\n7\nytisnetni\nevitaler\nfo\nnoitaR\nP = 0.0446 0.5\n0.4\n0.3\n0.2\n0.1\n0.0\nN\nA(fl/\n+);\nC o\nntr\nols\nR G + +) p oly Cr e( F M R esti n-\nN\nytisnetni\nevitaler\nfo\nnoitaR\na b\nP = 0.9341\nFXTAS phenotypes. While in vivo validation remains a critical future direction, our cell-based\nassays offer a timely and mechanistically relevant demonstration that altering PRKCG\nexpression influences disease-relevant cellular outcomes. We have incorporated these new\nfindings in Figure 7, which directly addresses the reviewer’s request within the scope of the\nOcvuerrexrperensts iosnt (ua)d aynd. shRNA knockdown (b) of PRKCG in Neuro2A\ncells withexpression of expanded CGG repeats\na b\n1.5\n1.0\n0.5\n0.0\nCtrl Ctrl\nC\nG\nh h K p C D N A/s C G G9 / 9 s G9 / 9 s h P R G C\nAdditional analyses:\ni. The behavior data has a worrisome potential confound. First, they will need to compare\nrotarod data in a subset of FXTAS mice with similar weights as WT controls. Also, they need\nto correlate weight with latency to fall in rotarod. If the correlation exists (esp. in WT mice)\nand there is no difference in latency between mice with similar weights, then more studies of\nmotor behavior will be needed that are not sensitive to weight differences (grid walk, beam\nwalking, grip testing); see below for comments on Fig. 4.\nThank you for this constructive suggestion. 1) At 3 and 4 months (when male FXTAS mice\nfirst display the phenotype), the FXTAS mice’s body weight was similar to WT mice (Fig. a &\nb, below). After matching for weight, FXTAS mice showed significantly shorter rotarod\nlatencies than WT controls at both developmental stages (*P < 0.05, **P < 0.01). These results\nindicate that the observed onset of motor impairment should not be explained by differences\nin body weight. We have included this results in the Supplementary Fig. 5. 2) In our current\nrevision, we complemented the behavioral findings with histological evidence: at 3–4 months,\nboth FMRpolyG+RNA(fl/+);Gad2-Cre(+);Tagger(+/–) and FMRpolyG+RNA(fl/+);Nestin-\nCre(+/–) mice showed significantly reduced Purkinje cell density relative to controls (see\nresponse on the next comments). This also supports the idea that the expression of expanded\nCGG repeats primarily drives locomotor deficits. 3) We note that in the original description\nof this FXTAS mouse model (Sellier et al., 2017), multiple behavioral assays were already\nperformed, including string and grip strength tests, in addition to rotarod. These assays\nrevealed reduced hind-limb traction and diminished grip strength, consistent with increased\nfalls from the rotarod. Thus, the published findings demonstrate that the locomotor deficits\nin this model are not assay-specific but represent a robust motor impairment phenotype.\n8\nytilibaiV\nlleC\nevitaleR\n1.5\n✱✱✱\n✱✱✱\n1.0\n0.5\n0.0\nP P P\nF F F G G G A- A- G- N N C D D K pc D N A/ pc C G G9 / 9 pc C G G9 / 9 P R\nytilibaiV\nlleC\nevitaleR\n✱✱\n✱✱\na Body weight(Left) and rotarodtest_(right) bBody weight (Left) and rotarod test_(right)\non 3-month male mice on 4-month male mice\n200\n150\n100\n50\nSellier, C., Buijsen, R.A., He, F., Natla, S., Jung, L., Tropel, P., Gaucherot, A., Jacobs, H., et\nal. 2017. Translation of expanded CGG repeats into FMRpolyG is pathogenic and may\ncontribute to fragile X tremor ataxia syndrome. Neuron, 93(2), pp.331-347.\nii. Quantify PC density for Fig. 4, preferably at multiple ages, in Gad2-Cre and Nestin-Cre,\nbut also in Camk2-Cre mice.\nThank you for this suggestion. To directly address PC abundance, we quantified PC density\nas counts per mm in cerebellar lobules using anti-Calbindin-D28k staining. 1) Fig. a: At 6–\n7 months (Fig. 4 and Supplementary Fig. 6a), PC density was significantly reduced in\nFMRpolyG+RNA(fl/+);Gad2-Cre(+);Tagger(+/-) and FMRpolyG+RNA(fl/+);Nestin-Cre(+/-)\nmice compared with controls (Welch’s t-test, P<0.0001 for both), whereas\nFMRpolyG⁺RNA(fl/⁺);Camk2-Cre(⁺);Tagger(⁺/–) cohorts showed no genotype effect. 2) Fig. b:\nto assess disease-progression effects, we performed the same staining and analysis at 3-4\nmonths (onset stage) in FMRpolyG+CGG_RNA(fl/+);Gad2-Cre(+) and\nFMRpolyG+CGG_RNA(fl/+);Nestin-Cre(+) cohorts. Both lines again showed significant\nreductions in PC density relative to controls (Welch’s t-test, P < 0.005 for each;\nSupplementary Fig. S6b), indicating early and progressive PC loss. Bars show mean ± SD; 3\nmice per genotype (brain sections analyzed) and four fields per section were counted.\niii. In the snRNAseq data, was Prkgc a DEG in Exc or GABAergic neurons, and if so, in which\ndirection (up or down)? (no significant change, confirm direction).\nThanks for your question. In the snRNA-seq data, Prkcg did not meet our DEG threshold\n(adjusted P < 0.05) in either excitatory or GABAergic neurons. However, as shown in the\ntable below, it exhibited a modest trend toward upregulation in both neuron populations,\n9\n)s(\ngnillaf\nerofeb\nemiT\n30 ✱\n20\n10\n)g(\nthgieW\nydoB\n30\n20\n10\n)g(\nthgieW\nydoB\n200\n150\n100\n50\n)s(\ngnillaf\nerofeb\nemiT\n✱✱\nalthough these changes did not survive multiple-testing correction. Specifically, in\nGABAergic (inhibitory) neurons, Prkcg upregulation exhibited p-value < 0.05. This\nobservation suggests that while Prkcg expression changes are subtle and below stringent\nstatistical thresholds in the snRNA-seq dataset, there is a biologically relevant indication of\ndysregulation.\navg_log2FC p_val p_adj\nInhibitory 0.017 5.32E-06 0.17\nExcitatory 0.041 0.75 1\niv. Better statistics throughout:\n- in line 133-4 (Fig. 1b), is the difference in # DEGs between regions significant? If not, then\nremove that statement.\nThank you for this suggestion. In lines 133-134 and Fig. 1b, we originally reported the\nnumber of DEGs per brain region and their overlaps but did not provide statistical evaluation.\nTo address this, we conducted Fisher's exact tests comparing DEG counts between brain\nregions. The tests revealed significant differences in the numbers of DEGs across brain\nregions (cerebellum vs. cortex, P = 0.0353; cerebellum vs. hippocampus, P = 4.5 × 10⁻⁵; cortex\nvs. hippocampus, P = 0.0331). Accordingly, we have revised the text (Line 140–141)\nas: “Pairwise Fisher’s exact tests revealed significant differences (all P < 0.05) in the number\nof DEGs among brain regions (Fig. 1b).” We have also added the detailed statistical values to\nthe Fig. 1 legend for clarity.\n- In line 149-50: how do they know that any of the overlap, particularly the greater overlap\nfor downregulated genes, is not due to chance alone (given more DEGs were downregulated\nin all 3 regions). They should use Odd Ratio tests to assess this. This applies to Fig. 5c, e and\nFig. 7a-b as well.\nThank you for this suggestion. To evaluate whether the observed overlaps, particularly the\ngreater overlap of downregulated genes, are significantly higher than expected by chance, we\nre-analyzed all overlaps in Fig. 1e (bulk RNA-Seq DEGs across regions), Fig. 5c,e (TRAP-Seq\nDEGs vs. hnRNPA2/B1 targets), and Fig. 7a–b (DEGs vs. TWAS candidates) using odds-ratio\ntesting with Fisher’s exact tests. In detail, we constructed contingency tables for each overlap\ncomparison, followed by Fisher's exact tests to derive odds ratios and their\nassociated P values. These analyses quantitatively confirm whether the observed overlaps\nsignificantly exceed expectations due to chance. The detailed results are listed in a table\nbelow and we have included this in Supplementary Table 5:\nSource Comparison Odds P-value\nRatio\nUp: Cortex & Cerebellum 14.89 5.95e-54\nUp: Cortex & Hippocampus 88.12 1.46e-64\nUp: Cerebellum & Hippocampus 48.67 3.52e-54\nFig. 1e\nDown: Cortex & Cerebellum 21.11 1.61e-97\nDown: Cortex & Hippocampus 55.95 1.48e-59\nDown: Cerebellum & Hippocampus 12.31 6.56e-20\nCortex 3-4m & hnRNPA2B1 targets 2.23 1.31e-23\nCortex 6-7m & hnRNPA2B1 targets 2.18 2.37e-20\nCerebellum 6-7m & hnRNPA2B1 targets 0.58 7.98e-06\nTWAS & Cortex DEGs 3–4 m 0.00 1.00\nTWAS & Cortex DEGs 6–7 m 0.33 0.077\nTWAS & Cortex GABA DEGs 3–4 m 0.35 0.10\nFig. 7a-b\nTWAS & Cortex GABA DEGs 6–7 m 0.29 0.14\nTWAS & Cerebellum GABA DEGs 3–4 m 1.23 0.67\nTWAS & Cerebellum GABA DEGs 6–7 m 2.37 0.018\n1) Fig. 1e: Fisher's exact tests confirmed that the overlaps of both up- and down-regulated\ngenes across brain regions (Fig. 1e) were significant, greatly exceeding expectations by chance\nalone (P-values < 1e-19). Overlaps among downregulated genes were stronger (higher ORs,\nsmaller P-values). Such overlaps could suggest a conserved core of dysregulation across brain\nregions. We have added this description in the Line 153-154.\n2) Fig. 5c, e: Fisher’s exact confirmed significant enrichment of overlaps between DEGs and\nhnRNPA2/B1 targets at both developmental stages in both brain regions. The late-stage (6-7\nmonths) cerebellum showed lower significant overlap than expected by chance (P-\nvalue=7.98e-06) compared with other comparisons. We have added this description in the\nLine 338-339.\n3) Fig. 7a-b: None of the overlaps between TWAS-identified candidates and cortex DEGs at\n3–4m reached significance (overlap = 0, P = 1). Similarly, most overlaps between TWAS genes\nand GABA DEGs are not significant (P > 0.05), although the Cortex GABA with TWAS\noverlap at 6–7 m trends toward significance (P = 0.14) and the cerebellum GABA DEGs\noverlap with TWAS genes at 6–7 is significant (P-value<0.05). We have added this description\nin the Line 388-390.\n- In Fig. 4c-f the stats should be ANOVAs (if data is normally distributed) and they should\nstate whether there is an age effect (it looks like both latency-to-fall and weight increase with\nage)\nThanks for your question. The rotarod latency-to-fall (Fig. 4c–d) and body weight (Fig. 4e–f)\ncomparisons were analyzed by two-way ANOVA with factors Genotype (Gad2-Tagger or\nCaMKIIα-Tagger with FMRpolyG+CGG_RNA mice vs Control animals) and Age (3, 4, 5, 6\nmonths), followed by Šídák’s multiple correction (Midway et al., 2020). Data are presented as\nmean ± SD, and significance was defined as adjusted P-value <0.05. The results are\nsummarized below, and we have included the description for Fig. 4c-f in the Results Line 272-\n276, and 282-284.\n1) Fig. 4c (rotarod in Gad2-Cre model): We observed significant main effects of genotype\n(F(1,11) = 78.57, P < 0.0001) and age (F(3,33) = 4.15, P = 0.0133), as well as a significant\ngenotype × age interaction (F(3,33) = 12.48, P < 0.0001), indicating progressive motor deficits\nin the FXTAS model with age.\nANOVA table F(DFn, DFd) P value\nAge x Genotype F (3, 33) = 12.48 P<0.0001\nAge F (3, 33) = 4.154 P=0.0133\nGenotype F (1, 11) = 78.57 P<0.0001\n2) Fig. 4d (rotarod in CamkII-Cre model): No significant effects were found for genotype, age,\nor interaction, suggesting minimal motor impairment in this model.\nAge x Genotype F (3, 149) = 1.166 P=0.3247\nAge F (3, 149) = 1.743 P=0.1607\nGenotype F (1, 149) = 2.338 P=0.1284\n3) Fig. 4e (body weight in Gad2-Cre model): There were significant main effects of genotype\n(F(1,25) = 15.83, P < 0.0001), age (F(1.74,43.56) = 308.8, P < 0.0001), and genotype × age\ninteraction (F(4,100) = 99.53, P < 0.0001), indicating genotype-specific weight gain over time.\nANOVA table F(DFn, DFd) P value\nAge x Genotype F (4, 100) = 99.53 P<0.0001\nAge F (1.742, 43.56) = P<0.0001\n308.8\nGenotype F (1, 25) = 15.83 P<0.0001\n4) Fig. 4f (body weight in CamkII-Cre model): A significant effect of age was observed (F(4,211)\n= 11.57, P < 0.0001), but there were no effects of genotype or interaction, suggesting that\nchanges reflect normal growth rather than a disease phenotype.\nANOVA table F(DFn, DFd) P value\nAge x Genotype F (4, 211) = 0.1853 P=0.9459\nAge F (4, 211) = 11.57 P<0.0001\nGenotype F (1, 211) = 0.6088 P=0.4361\nMidway, S., Robertson, M., Flinn, S. and Kaller, M., 2020. Comparing multiple comparisons:\npractical guidance for choosing the best multiple comparisons test. PeerJ, 8, p.e10387.\n- State which statistical test was used for each graph in the figure legends.\nThank you for this helpful suggestion. We have included statistical tests used for each graph\nin the figure legends.\nAdditional comments for every figure:\nFigure 1:\n- Fig. 1b-e: Would also be nice to show correlations of fold change for all DEGs in CX vs. CBM\nThank you for this helpful suggestion. We\n1 analyzed the correlations of\nLog2FoldChange for shared DEGs between\nCTX and CB. As shown below, these shared\n-3 -2 -1 1 genes displayed a significant positive\ncorrelation in their expression changes\n-1\nacross the two regions, indicating that they\nmay undergo coordinated regulation in the\n-2 FXTAS mouse model.\nLog2FoldChange_CB\n- Why not show volcano plots for bulk RNAseq?\nThanks for your question. We chose heatmaps because they best illustrate the transcriptional\nsignatures across brain regions and allow direct comparison of expression patterns across\nindividual samples. This format highlights regional clustering and relationships between up-\nand downregulated genes, which are central to our study design and interpretation. While\n6-7 months\nR2=0.99\nP<0.0001 2\n1\n-2 -1 1 2\n-1\nLog2FoldChange_CB\nXTC_egnahCdloF2goL\n3-4 months\nR2=0.73\nP<0.0001\nvolcano plots are useful for highlighting individual genes with large fold changes and strong\nstatistical support, they do not capture sample-level variation or inter-regional\ntranscriptional profiles as effectively as heatmaps. For these reasons, we believe heatmaps\nprovide the most informative representation for our datasets.\n- In 1f-g, is the analysis done for both ages or just oldest mice?\nWe appreciate the opportunity to clarify this point. In Fig. 1f-g, the GO analyses were\nspecifically performed on the DEGs identified at the late disease progression stage (oldest\nmice, 6-7 months old), as described in lines 156–158 and 168-169 of the manuscript.\n- Fig. S1a, c: Would it be possible to expand the heat maps in y-axis and name at least the\ntop 20 up and down DEGs? Also point out some interneuron-specific genes\nThanks for your\nsuggestion. We have\nexpanded the heatmaps\nin Supplementary Fig.\nS1a and S1c to display\ngene names for the top\n20 upregulated and top\n20 downregulated DEGs\nfrom each bulk RNA-seq\ndataset (please see\nbelow). In addition, we\nhighlighted\ninterneuron-related\ngenes among these\nDEGs: Pvalb expression\nwas increased in the\ncortex of 3–4-month-old\nFXTAS mice compared\nwith controls,\nwhile Sst was decreased\nin the hippocampus at\n6–7 months.\nFigure 2:\n- The rationale for doing snRNAeq in PFC but not CBM is not explained\nThank you for raising this point. We prioritized snRNA-seq in the PFC for several reasons.\nFirst, the PFC contains a broad diversity of neuronal and glial populations, making it well\nsuited to detect early, cell–type–specific molecular alterations in FXTAS. Second, the PFC is\ninvolved in functions highly relevant to FXTAS phenotypes—including executive control,\nbehavioral regulation, emotional processing, and preparatory motor activity (Le Merre et al.,\n2021). Although the cerebellum plays a central role in motor coordination, its cellular\ncomposition is dominated by granule cells, with vulnerable Purkinje cells accounting for only\na small fraction. In addition, FXTAS pathology is not confined to the cerebellum: intranuclear\ninclusions have been reported throughout the cortex and other brain regions (Greco et al.,\n2006; Sellier et al., 2017), further supporting the use of cortical profiling to capture disease-\n13\nrelevant signals. In this study, we complemented our PFC analysis with histological\nquantification of Purkinje cell density in the cerebellum, and in future work, we would like\nto extend snRNA-seq to the cerebellum to delineate cell–type–specific mechanisms\nunderlying motor impairments. We have added such a rationale for doing snRNA-Seq in PFC\nin the Discussion Line 457-463.\nLe Merre, P., Ährlund-Richter, S. and Carlén, M., 2021. The mouse prefrontal cortex: Unity\nin diversity. Neuron, 109(12), pp.1925-1944.\nSellier, C., Buijsen, R.A., He, F., Natla, S., Jung, L., Tropel, P., Gaucherot, A., Jacobs, H.,\nMeziane, H., Vincent, A. and Champy, M.F., 2017. Translation of expanded CGG repeats\ninto FMRpolyG is pathogenic and may contribute to fragile X tremor ataxia syndrome.\nNeuron, 93(2), pp.331-347.\nGreco, C.M., Berman, R.F., Martin, R.M., Tassone, F., Schwartz, P.H., Chang, A., Trapp, B.D.,\nIwahashi, C., Brunberg, J., Grigsby, J. and Hessl, D., 2006. Neuropathology of fragile X-\nassociated tremor/ataxia syndrome (FXTAS). Brain, 129(1), pp.243-255.\n- What does Inh_2 cluster in PFC correspond to – does it express Pvalb, Sst, or Vip? Consider\nsplitting “Inh” in Fig. 2d according to the 2 inh categories.\nThank you for this suggestion. To address it, we generated a heatmap showing the expression\nof marker genes (including Pvalb, Sst, and Vip) across the identified cell type clusters. Using\nour current pipeline and clustering resolution, we did not detect clear expression of Pvalb.\nHowever, both Sst and Vip were expressed and showed enrichment within the Inh_1 cluster.\nThe apparent overlap between Sst and Vip expression likely reflects limitations in sequencing\ndepth and the number of nuclei captured during library preparation. In future work, we plan\nto increase sequencing depth and apply sub-clustering approaches specifically to inhibitory\nneurons to resolve these sub-populations with greater precision.\n14\nyet the authors refer to it multiple times in Results and Discussion). It’s particularly\nperplexing given that, in the CBM, the main GABAergic cells (Purkinje neurons) have died.\nThank you for this helpful suggestion. We performed immunofluorescent (IF) staining in the\nmouse PFC using a validated anti-GABA antibody (Thermo Fisher, Cat# PA5-32241)\ntogether with the neuronal marker NeuN. As shown in below Fig. a-b (included in\nSupplementary Fig. 3a–b), FXTAS mice exhibit a significant increase in GABA⁺ neuronal\ndensity versus controls (p < 0.05, Student’s t-test), in agreement with our scRNA-seq results.\nWe have included these results in the Supplementary Fig. S3. In the cerebellum, the same\nantibody primarily labeled Purkinje cells and their processes (Fig. c). Because FXTAS cohorts\nexhibited marked PCs loss (Fig. 4), the reduced GABA signal should be interpreted as a\nconsequence of Purkinje cell loss rather than interneuron fate changes. We have revised the\ntext to avoid over-interpretation across regions.\nFigure 3:\n- In Fig. 3f aren’t most GO terms shared by Exc and Inhib neurons? this is not mentioned in\nthe Results.\nThank you for this suggestion. Several GO terms identified in the top 200 DEGs for excitatory\nand inhibitory neurons overlap, reflecting shared biological processes affected in both\nneuronal populations. However, our analyses also highlighted distinct functional\n15\n;)-/xolf(ANR+GylopRMF\n)+(erC-nitseN\nTW\nDAPI GABA NeuN Merge\n0.3\n0.2\n0.1\n0.0\nDAPI GABA NeuN Merge\noly\nG +\nN\nR\ne\nN st A i n ( - f C l/\n+\nr e\n);\n( +) C o ntr\nols\np\nR\nM\nF\n)%(\nsnoruen\ndlebbal-ABAG\naa\nb\nP = 0.0051\nDAPI GABA NeuN Merge\nDAPI GABA NeuN Merge\n;)+/lf(ANR+GylopRMF\n)+(erC-nitseN\nTW\nc\nenrichments specific to inhibitory neurons, such as \"locomotor behavior\" and \"balance\ncontrol,\" aligning closely with the motor deficits observed in FXTAS. This specificity\nunderscores the pronounced transcriptional dysregulation in inhibitory neurons at the\ndisease onset stage. We have revised Line 231-233 in the Results section to explicitly\nacknowledge the overlap and clarify the neuron-type-specific differences as: \"Although\nseveral GO terms were common to both excitatory and inhibitory neurons, inhibitory neurons\nuniquely exhibited enrichment in categories related to locomotor behavior and balance\ncontrol (Fig. 3f), suggesting their specific contribution to FXTAS-associated motor\ndysfunction.\"\n- In Fig 3b why are there DEGs near zero (minimal change) for Inh neurons? (shouldn’t it\nlook like Exc neurons because of some statistical threshold?)\nThank you for this observation. The presence of DEGs near zero log₂ fold change in inhibitory\nneurons reflects our statistical criteria, which defined DEGs as those with adjusted P < 0.05\nwithout applying an additional fold-change cutoff. This approach allowed us to identify genes\nwith statistically robust differences, even if the effect sizes are modest, particularly for genes\nwith low within-group variability. Inhibitory neurons, which contained a larger number of\nDEGs overall, therefore include some genes with small fold changes that still meet statistical\nsignificance. By contrast, excitatory neurons produced fewer DEGs, resulting in a narrower\ndistribution around higher fold-change values.\n- Data in Fig 3d seems neither here nor there given extremely low correlations. I would\nrecommend moving this to suppl and merging Figs 2 & 3 into a single figure for snRNAseq.\nWe appreciate the reviewer’s thoughtful suggestion. The relatively modest correlations\nobserved in Fig. 3d between the bulk RNA-seq and snRNA-seq datasets can be explained by\ninherent differences between these two methods. Bulk RNA-seq measures the average gene\nexpression across all cell types present in the sampled tissue, potentially diluting cell-specific\nsignals. Conversely, snRNA-seq captures gene expression profiles at the single-cell level,\nproviding higher resolution but often yielding greater variability due to increased sparsity,\ntechnical noise, and sampling differences. As a result, modest overall correlations are\nexpected when comparing these approaches. Similar observations have been reported in\nother studies, such as von Ziegler et al. (2022), which found modest correlations between bulk\nRNA-seq and snRNA-seq in mouse PFC following acute stress. Despite these modest global\ncorrelations, we observed significant associations in excitatory and inhibitory neurons,\nparticularly for genes with larger fold changes. We believe these cell-type-specific correlations\nremain valuable to readers and support our snRNA-Seq findings. Therefore, we prefer to\nretain Fig. 3d in the main figure. To avoid overrepresentation, we have revised the\nmanuscript text in Line 226-228: “Despite modest overall correlations, the inhibitory and\nexcitatory neurons exhibited significant association with bulk gene expression (Fig. 3d;\nP<0.001), particularly for DEGs with substantial fold changes.\"\nvon Ziegler, L.M., Floriou-Servou, A., Waag, R., Das Gupta, R.R., Sturman, O., Gapp, K.,\nMaat, C.A., Kockmann, T., Lin, H.Y., Duss, S.N. and Privitera, M., 2022. Multiomic\nprofiling of the acute stress response in the mouse hippocampus. Nature\nCommunications, 13(1), p.1824.\nFigure 4:\n16\n- The main result is that as the Gad2-Cre mice get older and gain a lot of weight, their latency\nto fall shortens. This is not mentioned by the authors and yet greater weight alone could be\nthe reason why they fall sooner (the Camk2-Cre cohort is not impaired in rotarod because\nthey don’t get fat). They should show correlation graphs between weight and latency for all\ngroups and ages, they also compare Gad2-Cre mice with control mice of the same weight. If\nthis phenotype is indeed due to weight, then a lot of the excitement for this paper goes away\nand they’ll need to find other ataxia phenotypes.\nWe appreciate the reviewer’s thoughtful suggestion. We agree that the relationship between\nbody weight and motor performance in the Gad2-Cre cohort warrants further clarification.\nWhile correlation plots between body weight and rotarod latency would show that heavier\nmice tend to perform worse, such analyses cannot distinguish whether reduced performance\nis a consequence of obesity or an independent disease phenotype. To address this issue more\ndirectly, we instead performed weight-matching analyses at 3 and 4 months of age, when\nmale Gad2-Cre mice first begin to show deficits. At these ages, body weight differences were\nminimal; nonetheless, Gad2-Cre mice exhibited significantly shorter rotarod latencies than\nweight-matched controls (Fig. a & b below). These findings indicate that the onset of motor\nimpairment cannot be explained solely by weight gain, but rather reflects a disease-related\nphenotype driven by the expression of expanded CGG repeats. In contrast, Camk2-Cre mice\ndid not show significant motor impairment, even when their weights were similar to those of\ncontrols at all tested ages (Fig. 4d&f), supporting the idea that the motor phenotype is\nunrelated to obesity. Consistent with this conclusion, our two-way ANOVA revealed\nsignificant main effects of genotype (F(1,11) = 78.57, P < 0.0001) and age (F(3,33) = 4.15, P =\n0.0133), as well as a strong genotype × age interaction (F(3,33) = 12.48, P < 0.0001) on\nlocomotor performance in Gad2-Cre mice. Body weight analyses also confirmed significant\neffects of genotype (F(1,25) = 15.83, P < 0.0001), age (F(1.74,43.56) = 308.8, P < 0.0001), and\ngenotype × age interaction (F(4,100) = 99.53, P < 0.0001), consistent with genotype-specific\nweight gain over time. Taken together, these analyses demonstrate that the shortened\nrotarod latency in Gad2-Cre mice reflects a disease-related motor impairment rather than an\nobesity-driven effect.\na Body weight(Left) and rotarodtest_(right) bBody weight (Left) and rotarod test_(right)\non 3-month male mice on 4-month male mice\n200\n150\n100\n50\n- They should make the y-axes of graphs in Figs. 4c & 4d the same (up to 300 sec in both, and\nwithout cutting the axis; no reason to do that). It seems that controls had longer latencies to\nfall in 4d for some reason. What’s the p value for 5 months in 4d?\nThank you for pointing this out. We have modified Figs. 4c&d to the same Y-limit. In Fig. 4d,\nthe adjusted P-value for 5 months is 0.13. Regarding the control group showing somewhat\n17\n)s(\ngnillaf\nerofeb\nemiT\n30 ✱\n20\n10\n)g(\nthgieW\nydoB\n30\n20\n10\n)g(\nthgieW\nydoB\n200\n150\n100\n50\n)s(\ngnillaf\nerofeb\nemiT\n✱✱\nlonger latencies in Fig. 4d, we acknowledge natural variability across cohorts. Importantly,\nthose FMRpolyG+RNA(fl/+); CaMKIIα-Tagger mice showed similar locomotor activity across\nall developmental stages that we tested, and statistical analyses confirmed no significant\ndifferences between CaMKIIα-Tagger mice and controls at 5 months.\n- Same issue with y-axis matching applies to Figs 4e & 4f and Figs S3a-b and S3c-d\nThank you for this suggestion. We have modified and aligned the Y-axis scales in Fig. 4e-f,\nFigs. S3a-b and S3c-d to ensure consistency across panels.\n- Fig. 4g: The images show dramatic loss of Purkinje cell in Gad2-Cre mice but there is no\nquantification of PC density to get a sense of how extensive this is and whether it indeed\nmatches loss seen in Nestin-Cre mice. Also, they need to show data for Camk2-Cre mice;\notherwise, we cannot conclude that overexpression in GABAergic neurons is unique in\nreproducing key phenotypes of FXTAS mice.\nWe appreciate this helpful comment. The IHC images in Fig. 4 were acquired from 6–7-\nmonth-old mice using anti-Calbindin-D28k staining. To directly examine PC abundance, we\nquantified PC density as PC counts per mm in cerebellar lobules. At 6–7 months, PC density\nwas significantly reduced in FMRpolyG+RNA(fl/+);Gad2-Cre(+);Tagger(+/-) and\nFMRpolyG+RNA(fl/+);Nestin-Cre(+/-) mice compared with controls (P<0.0001 for both; Fig. a\nbelow). In comparison, Camk2-Cre cohorts (FMRpolyG+RNA(fl/+);Camk2-Cre(+);Tagger(+/-))\nshowed no genotype effect (all P > 0.05). We have included these results in Supplementary\nFig. 6.\nFigure 5:\n- Can they confirm cell type-specificity of TRAP-seq by showing expression of genes that are\nuniquely expressed in each, like what they did for snRNA-seq (Fig. 2d).\nWe thank the reviewer for this helpful suggestion. Our TRAP-seq was performed\nin FMRpolyG+RNA(fl/+); Gad2-Cre;Tagger mice, in which HA-tagged ribosomes are\nrestricted to GABAergic neurons; thus, the IP specifically captures the translating mRNA\npool from inhibitory neurons. To document this specificity, we quantified normalized read\ncounts of marker genes in the TRAP (IP) across at 6m cortex. As demonstrated below,\ninhibitory neuron markers (Gad1, Gad2, Slc32a1) were robustly enriched, whereas excitatory\nmarkers (Neurod6, Slc17a) and non-neuronal markers (astrocyte: Aldh1l1, GfAqp4;\noligodendrocyte: Mobp, Mog; microglia: Ctss and Csf1r; OPC: Pdgfra) were depleted. These\nresults confirm that the TRAP-seq signal is derived predominantly from GABAergic neurons.\n18\n8000\n6000\n4000\n2000\n- There are important differences between these data and the ones from bulk sequencing: Is\nit unusual that they saw more DEGs in cortex this time (compared to Fig 1)? In TRAP-seq,\nwhy do they no longer see an increase in # DEGs with age, as degeneration progresses? (with\nbulk sequencing they did)\nWe appreciate the reviewer highlighting this observation. 1) The differences in DEG patterns\nbetween TRAP-seq and bulk RNA-seq reflect inherent distinctions in methodology and\nsampled cell populations. TRAP-Seq selectively captures mRNAs from actively translating\nribosomes in genetically defined neuronal populations, GABAergic neurons in our case.\nTherefore, this method provides a more cell-type-specific snapshot of gene expression. In\ncontrast, bulk RNA-seq measures average transcript abundance across all cell types in the\ntissue, which can dilute or obscure changes occurring in specific subpopulations. Accordingly,\nit is expected that a greater number of DEGs would be detected in cortical GABAergic\nneurons by TRAP-seq than by bulk RNA-seq, as cell–type–specific changes are more readily\nuncovered once cellular averaging is removed. 2) The progressive increase in DEGs with age\nobserved in bulk RNA-seq likely reflects the cumulative transcriptional impact of\ndegeneration across multiple cell types, including glia and vulnerable neuronal populations,\nas pathology advances. By contrast, the relatively stable number of DEGs detected by TRAP-\nseq in the cortex may suggest that cortical GABAergic neurons undergo robust\ntranscriptional and translational reprogramming during disease progression, with\nsubsequent changes manifesting in stage-specific expression patterns rather than in the\noverall DEG count. Our cross-comparison of DEGs between stages and GO analyses indeed\nrevealed age-dependent expression patterns within this neuronal population (Supplementary\nFig. 8). Together, these complementary findings highlight how bulk RNA-seq and TRAP-seq\ncould provide distinct but informative perspectives: bulk captures the expanding tissue-level\nconsequences of neurodegeneration, while TRAP-seq pinpoints early and late-stage cell type–\nspecific alterations within inhibitory neurons.\n- It’s generally accepted to interpret RNAseq results in the context of phenotypes, but the\nauthors seem to stretch this a bit too much; in L299-300 it’s an exaggeration to cherry-pick a\ncouple of GO terms (locomotor behavior or skeletal muscle contraction) from a longer list to\nargue that this explains the rotarod deficits of the mice (even though they are due to obesity).\nThey should tone this down a bit.\nWe appreciate this helpful suggestion. To avoid overstating the link between selected GO\nterms and behavioral phenotypes, we have revised the Results text to interpret the\nenrichment analysis more cautiously. Rather than directly linking specific GO terms to the\nrotarod phenotype, we now present them as examples of neuronal and motor-related\n19\nstnuoc\ndaer\ndezilamroN\nprocesses, while emphasizing that GO enrichment highlights broad biological associations\nrather than direct causal explanations. We have changed the wording in Line 326-329 as: “In\nthe cerebellum, DEGs are associated with neuronal and motor-related processes, such as\nlocomotor behavior (3-4m) and skeletal muscle contraction (6-7m), among a broader set of\ncategories. These molecular alterations indicate that pathways linked to motor function are\naffected, which could be relevant to the locomotor dysfunction observed in the behavioral\nassays.”\nFigure 6:\n- It’s a nice screen, but none of the candidates are pursued further\nWe appreciate this comment. Our TWAS analysis, combined with a Drosophila RNAi screen,\nidentified several high-confidence fly orthologs of human candidates that modify CGG repeat-\nassociated neurotoxicity, providing valuable targets for future investigation. Because PRKCG\nlacks a high-similarity ortholog in Drosophila, it was not included in this RNAi screen. While\nsystematic follow-up of the identified modifiers is ongoing and will be reported separately,\nthe primary goal of this study was to establish convergent evidence across mouse, human,\nand fly datasets to highlight the most consistently implicated genes in FXTAS. Among these,\nPRKCG emerged as a particularly compelling candidate based on its biological relevance and\nstrong support across multiple model systems. Accordingly, we prioritized additional\nvalidation of PRKCG here, while noting that the additional modifiers remain important for\nfuture investigation.\n- Prosbeta5 should be removed since the RNAi is toxic to control flies\nWe appreciate this comment. Prosbeta6 is the high-similarity fly ortholog of human PSMB1.\nWe acknowledge that Prosbeta6 RNAi is toxic in control flies, consistent with its essential\nrole in proteasome function (Ansar et al., 2020). However, we believe it is important to retain\nthe data, as it provides mechanistic insight into the interaction between proteasome\nimpairment and CGG-repeat toxicity. The lethality observed with Prosbeta6 knockdown in\nFXTAS flies reflects its critical role in cellular homeostasis, and the further exacerbation of\nthis phenotype in the presence of CGG repeats supports a functional interaction. Therefore,\nwe have kept Prosbeta6 in the Fig. 6e.\nAnsar, M., Ebstein, F., Özkoç, H., Paracha, S.A., Iwaszkiewicz, J., Gesemann, M., Zoete, V.,\net al. 2020. Biallelic variants in PSMB1 encoding the proteasome subunit β6 cause\nimpairment of proteasome function, microcephaly, intellectual disability, developmental\ndelay and short stature. Human molecular genetics, 29(7), pp.1132-1143.\nFigure 7:\n- The levels of Prkcg are not changed in FXTAS mice vs. controls. Elevated Prkcg mRNA in\nhuman NPCs is from only n=3 mice\nWe respectfully clarify that our analyses in Fig. 7a–c consistently show\nincreased Prkcg expression in FXTAS mice compared with controls. Specifically, bulk RNA-\nseq of cortical tissue (Fig. 7a), TRAP-seq of cerebellar GABAergic neurons (Fig. 7b), and qRT-\nPCR validation in mouse tissues (Fig. 7c) all demonstrated elevated Prkcg levels in FXTAS\nmice. We acknowledge that the human NPC data were derived from a limited number of\nsamples (n=3 per group), but their expression is significantly increased. It’s worth validating\nin larger cohorts in future studies.\n20\n- They should mention adjusted p values for the Prkcg in legend of Fig 7a-b\nThank you for this suggestion. We have added the significance value of Prkcg and other DEGs\nin the legend of Fig. 7a-b.\n- Isn’t it strange that Prkcg is down in the CBM in the bulk RNAseq, but in TRAPseq it is up\nin GABAergic neurons?\nWe appreciate this important point. The apparent discrepancy in Prkcg expression between\nbulk RNA-Seq and TRAP-seq reflects differences in methodology and in the populations\nsampled. Bulk RNA-seq measures average gene expression across all cerebellar cell\npopulations, including multiple cell types, such as Purkinje cells, granule cells, Golgi cells,\nstellate cells, and basket cells. Because Purkinje cell loss is a key pathological feature of\nFXTAS, and Prkcg is highly enriched in Purkinje cells, reduced expression in bulk tissue is\nlikely caused by the reduced Purkinje cell numbers relative to controls. In contrast, TRAP-\nseq captures ribosome-bound mRNAs specifically from genetically defined cerebellar\nGABAergic neurons. The increased Prkcg expression detected by TRAP-seq therefore\nrepresents cell–type–specific dysregulation within surviving GABAergic neurons, a signal\nthat is masked in bulk analyses due to averaging across heterogeneous cell populations.\n- The counts for CTX at 3-4 months in Fig 7A do not seem to match with those of Fig 1B\n(Fig1B CTX 3-4m is 127, Fig7A is 496); same for CB (Fig 1b 162, Fig 7A is 576)\nThank you for pointing this out. In our original analysis for Fig. 7a, the genes used for\noverlapping with TWAS candidates in 3–4 months CTX and CB were selected using a relaxed\nthreshold of P < 0.05, since relatively few DEGs were identified by bulk RNA-seq at this stage.\nThis resulted in higher counts than those shown in Fig. 1b. However, we realize that the\nshared genes identified in this analysis also met the cutoff P < 0.01 and correspond to the\nDEGs presented in Fig. 1b. To ensure consistency, we have now updated the gene counts Fig.\n7a so that the DEG counts match those reported in Fig. 1b.\n- Fig 7b. The counts for 3-4 months CB Fig.5e 328; Fig 7b 523; CTX 3-4 months Fig 5e 728 fig\n7b 811. the results match for 6-7 month\nThank you for pointing this out. We have carefully re-checked each number in Fig. 5e and\nFig. 7b. For the 3-4 months CB, the DEG count in Fig.5e is 523, which matches with the count\nshown in Fig. 7b. For the 3-4 months CTX, the DEG count in Fig. 5c (not Fig. 5e) is 811, which\nalso matches with the count in Fig. 7b. The results for the 6-7 months groups are consistent\nacross both figures.\nMinor:\n- L151: “widespread neuronal death” needs a reference\nThank you for this suggestion. In line with feedback from another reviewer, we have revised\nthe wording to avoid overstatement in Lines 150-153 of the revised manuscript. The sentence\nnow reads: “Cross-region comparisons (Fig. 1e) showed that downregulated genes exhibited\ngreater overlap than upregulated genes, likely reflecting conserved pathways involved in\nneuronal dysfunction or cell loss across regions (Fröhlich et al., 2024).”\nFröhlich, A.S., Gerstner, N., Gagliardi, M., Ködel, M., Yusupov, N., Matosin, N., Czamara, D.,\net al. 2024. Single-nucleus transcriptomic profiling of human orbitofrontal cortex reveals\n21\nconvergent effects of aging and psychiatric disease. Nature neuroscience, 27(10), pp.2021-\n2032.\n- Fig. S1: ‘z-score’ misspelled in color scale bar in a-d\nThank you for pointing this out. We have corrected the wording for “z-score” in Fig. S1\n- L166 “Overpenetration” – do they mean “overrepresentation”? also is it “ion transport” or\n“iron transport” (different things in figure vs. text)\nWe appreciate this careful reading. We have corrected “overpenetration” to\n“overrepresentation”, and “iron transport” to “ion transport” in Line 170-171 of the revised\nmanuscript.\n- L192: they should introduce the clustering method for Fig 2f\nThank you for pointing this out. The description of the clustering methods used for the\nsnRNA-seq analysis, including those shown in Fig. 2f, is provided in the Methods section\nunder “Data pre-processing and analyses of snRNA-seq.”\n- Fig. S5d: “adult” is misspelled under adult locomotory behavior; “Negative” is misspellled.\nThank you for pointing this out. We have corrected the misspellings in Supplementary Fig.\nS5d.\n- Fig. 4f-h: what age were immunos done?\nThanks for your question. The brain sections used for IHC in Fig. 4f-h were obtained from\nanimals at 6-7 months of age.\n- How many mice were used for TRAP-seq?\nThanks for your question. We have listed the number of animals used for each genotype in\nTRAP-Seq as below. We have included the description for the sample size in the Figure legend\nfor TRAP-Seq.\nCortex Cerebellum\nFMRpolyG+RNA; Control FMRpolyG+RNA; Control\nGad2-Tagger CaMKIIα-Tagger\n3-4 4 4 2 3\nmonths\n6-7 3 3 3 3\nmonths\n- Fig 5: the labels for DEGs in volcano plots are a bit messy: the lines don’t always reach a\ndot, and the words are on top of each another\nWe appreciate the reviewer’s suggestion. The volcano plots in Fig. 5 have been updated for\nbetter readability.\n- Fig 7B error bar is off center for Ttyh1.\nThank you for pointing this out. We have corrected the error bar alignment for Ttyh1 in Fig.\n7b.\n- Line 349: I count 6 TWAS gene that overlap with CTX not 5\nthe expression data of bulk brain tissues from FXTAS mice, we identified differential\nexpression of 5 TWAS genes in the cortex or cerebellum (Fig. 7a).” After re-checking the data\nin Fig. 7a, we confirm that there are indeed 5 overlapping genes, including Prkcg, Tlk1, Txnip,\nTtyh1, and Atp6v1g2.\nReviewer #3:\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\nReviewer #4:\nIn this study, Yulin Jin et al. have made great effort to conduct multi-omics strategy combined\nwith TWAS to decode the molecular mechanisms of FXTAS, and identified a novel prominent\ndriver gene of FXTAS pathogenesis, which is of significance for guiding its clinical practice.\nHowever, there are also a number of issues which should be addressed.\n1. The authors employed different criterion for the identification of DEGs at the onset of\nphenotype (p<0.01), late progression stage (FDR<0.05) and for specific cell types (adjusted p-\nvalue<0.05), is it reasonable? Are these identified DEGs comparable?\nThanks for raising this consideration. We acknowledge that our choice of thresholds differed\nbetween analyses, reflecting specific experimental contexts and goals. At the early onset\nstage, we used a nominal p-value threshold (p < 0.01) due to subtle transcriptional changes,\nlower statistical power, and fewer DEGs at this stage. This criterion allowed us to detect\npotential early gene expression changes that may be relevant to disease initiation. For\nthe late progression stage (bulk RNA-seq) and cell-type-specific analyses (snRNA-seq), we\napplied a more stringent value (FDR < 0.05). At these stages, the number of DEGs was larger\nand statistical power was greater, making it appropriate to use a conservative multiple-\ntesting correction to reduce false positives and focus on the most robustly altered genes.\nAlthough the DEG sets were defined under slightly different thresholds, the comparisons we\nhighlight in the manuscript are made within each dataset rather than directly across\nthresholds, and key findings (e.g., enrichment of neuronal pathways, overlap with TWAS\ncandidates) are consistent across approaches. Thus, while the DEG lists are not directly\ninterchangeable, the biological conclusions remain comparable and robust.\n2. I suggest the authors to briefly describe the sample size used in the omics data analysis in\nthe Results section, such as the transcriptomic profiling and snRNA-seq data analysis.\nWe appreciate this helpful suggestion. We have now included descriptions of the sample sizes\nused for each omics dataset (bulk RNA-seq, snRNA-seq, and TRAP-seq) in the corresponding\nfigure legends and Methods.\n3. The prior marker genes for cell-type annotation that the study used should be listed in the\nMethod section so that other researchers can repeat these relative analyses.\nThanks for your suggestion. We have included prior marker genes for cell-type annotation in\nthe Method section of the revised manuscript, Line 676-677.\n23\n4. Which statistical test method does the authors use to compare cell proportions between\nFXTAS and control animals, it seems that the difference of inhibitory neurons is not that\nremarkable. The method should be described before the p-value.\nWe appreciate this comment. We used Welch’s t-test to compare cell proportions between\nFXTAS and control animals. We have included the t-test method in the results for Fig. 2e&f.\n5. Which method was used for the CGG repeat toxicity evaluation (fly eye)?\nThank you for this question. To evaluate CGG repeat toxicity in fly, progeny from the genetic\ncrosses were collected and aged to 7 days. The screen was performed by scoring rough eye\nphenotype under a microscope. For quantification, we have added panels to Fig.6e and Fig.7f\n(shown in panels a and b below, respectively), where genotypes are indicated beneath each\ncolumn. The numbers above each column represent the percentage of flies exhibiting an\nenhanced rough eye phenotype for each cross. These quantification data have also been\nincluded in Supplementary Fig. S11.\n6. Why does the authors select TIGAR-V2 to conduct TWAS analysis, to my knowledge, several\ntools such as PrediXcan and FUSION are more popular.\nWe thank the reviewer for raising this question. While PrediXcan (Gamazon et al., 2015) and\nFUSION (Gusev et al., 2016) are widely used TWAS tools, we note that TIGAR and the\nrelated TIGAR-V2 tool are also quite popular for TWAS (with the original TIGAR paper\nhaving over 100 citations in Google Scholar). We chose TIGAR-V2 (Parrish et al., 2022) over\nPrediXcan and FUSION as the former method has methodological flexibility and improved\nperformance over the latter two methods. PrediXcan and FUSION assume parametric priors\nfor modeling cis-eQTL effect sizes, which restrict their capability for handling complex\ngenetic architecture of transcriptomes. In contrast, TIGAR (Nagpal et al., 2019) employs a\nnonparametric Bayesian method that provides a more flexible and general framework,\nencompassing the parametric models used by PrediXcan and FUSION as special cases. Both\nsimulation studies and real data applications have demonstrated that TIGAR achieves higher\nimputation accuracy of genetically regulated gene expression and greater statistical power to\ndetect associations with complex traits compared to PrediXcan and FUSION in TWAS\nanalyses. TIGAR-V2 extends the original TIGAR framework by incorporating parallel\ncomputing to enhance computational efficiency. It can also accommodate both individual-\nlevel and summary-level GWAS data, making it highly versatile for large-scale studies. Given\n24\nthese advantages in modeling generalizability, imputation accuracy, and computational\nefficiency, we chose TIGAR-V2 for our TWAS analyses.\nGamazon, E.R., Wheeler, H.E., Shah, K.P., Mozaffari, S.V., Aquino-Michaels, K., Carroll, R.J.,\net al. 2015. A gene-based association method for mapping traits using reference\ntranscriptome data. Nature genetics, 47(9), pp.1091-1098.\nGusev, A., Ko, A., Shi, H., Bhatia, G., Chung, W., Penninx, B.W., Jansen, R., De Geus, E.J.,\nBoomsma, D.I., Wright, F.A. and Sullivan, P.F., 2016. Integrative approaches for large-\nscale transcriptome-wide association studies. Nature genetics, 48(3), pp.245-252.\nParrish, R.L., Gibson, G.C., Epstein, M.P. and Yang, J., 2022. TIGAR-V2: Efficient TWAS\ntool with nonparametric Bayesian eQTL weights of 49 tissue types from GTEx V8. Human\nGenetics and Genomics Advances, 3(1).\nNagpal, S., Meng, X., Epstein, M.P., Tsoi, L.C., Patrick, M., Gibson, G., De Jager, P.L.,\nBennett, D.A., Wingo, A.P., Wingo, T.S. and Yang, J., 2019. TIGAR: an improved Bayesian\ntool for transcriptomic data imputation enhances gene mapping of complex traits. The\nAmerican Journal of Human Genetics, 105(2), pp.258-266.\n7. The colors for distinguishing the FXTAS and Control groups in Figure 3e are suggested to\nbe changed, which are easily confused with the heatmap gradient colors. Similarly, the white\nboxes in Figure 4c are not ideal.\nThanks for your suggestion. The color label for the FXTAS and Control groups has been\nchanged. We also changed the white boxes in Fig. 4c to a grey color.\n8. Is it necessary to list those genes with p-value or adjust larger than 0.05 in supplementary\ntables.\nWe have listed all the genes detectable in bulk RNA-Seq, snRNA-Seq, and TRAP-Seq,\nincluding their P-value, adjusted P-value, and Log2Fold-change.\n9. There are some syntax errors in the manuscript, such as ‘We used TIGAR-V2 69was used to’\nin line 653 on page 23.\nThe syntax errors have been corrected in Line 728 of the revised manuscript.\nReviewer #5:\nThis paper “Integrative transcriptome-wide association reveals PRKCG-linked GABAergic\ndysfunction in fragile-X associated tremor/ataxia syndrome”.(FXTAS) by Jin and colleagues\ndescribes the detailed molecular analysis of mouse models for this late-onset\nneurodegenerative disease that is caused by CGG repeat expansion. `Both toxic expanded-\nrepeat RNA-gain of function as well as RAN translation have been suggested to contribute to\npathogenesis, though the relative importance of each of these phenomena is under intense\ndebate. The authors here provide multi-omics data suggesting both mechanisms are important\nfor pathogenesis. Moreover, using coitional knockouts, the authors find that expression of the\nrepeat in GABAergic neurons alone is sufficient to drive FXTAS pathogenesis. Finally, PRKCG\nis identified as a novel modifier of the disease as confirmed in fly studies.\nThe study represents an enormous amount of work and was well designed and executed and\nso contributes to our current understanding of FXTAS pathogenesis.\n1. The authors show that increased expression of PRKCG leads to a robust detoriation of the\nrough eye phenotype in flies and conclude this gene is a modifier of\n1.5\nFXTAS. However, in order to prove this is a true modifier, an elegant\nexperiment would be to show that a deletion of this (or related) gene\n1.0 results in an amelioration of the FXTAS phenotype.\nThanks for your question. 1) aPKC (gene PKC98E) is the closest PKC-\n0.5 family homolog of mammalian PRKCG in Drosophila based on\nsequence/domain similarity; however, it is not a one-to-one, high-\n0.0 confidence ortholog. Accordingly, we revised the Abstract to replace “its\nCtrl Ctrl\nk\nc g fly ortholog” with “the fly PKC homolog, aPKC.” Consistent with\nN\nA/ s\nh\nG9\n/\n9\ns\nh\n/ s\nh\nPr\nFlyBase ortholog mapping, which does not assign a high-confidence\np C D C G G G99 Drosophila ortholog for human PRKCG. Therefore, we did not perform\nC\naPKC knockdown in flies in this study. 2) To complement the fly work,\nwe performed shRNA-mediated knockdown of Prkcg in mouse Neuro2A (N2A) cells\nexpressing CGG-repeat RNA. Cells were transfected with 5′UTR-CGG99-FMR1-EGFP (99\nCGG repeats in the human FMR1 5′UTR) or a control vector. As reported previously (Kong\net al., 2022), CGG-repeat RNA significantly reduced cell viability. Here, we found that Prkcg\nknockdown significantly rescued this CGG-induced toxicity at 96 h post-transfection (Right\nfigure), supporting a functional role for PRKCG in modulating repeat-associated\nneurotoxicity. (Exact n and statistics are provided with the figure).\n2. The study could mention the level of expression of the toxic RNA in their model, as this has\nan important effect on the clinical presentation. In addition, while a repeat construct of 90\nrepeat units is used, it may be worthwhile to measure at least in some of the experiments the\nrepeat length in the tissues under investigation, as the repeat may inherit unstably, and such\nrepeat-length differences may influence the outcome of the analysis.\nWe appreciate this thoughtful suggestion. In our model systems, the human FMR1 5′UTR-\nCGG transgene is expressed at readily detectable levels in mouse brain tissues, as shown by\nqRT-PCR and GFP-based snRNA-seq analyses (Fig. 4b; Supplementary Fig. 2e, f). Expression\nwas observed in both excitatory and inhibitory neurons, consistent with induction by the\nstrong CAG promoter. These data suggest that the transgene could produce sufficient CGG\nrepeat RNA to drive reproducible behavioral and neuropathological phenotypes. In parallel,\nimmunohistochemistry and western blot analyses confirmed accumulation of FMRpolyG\nprotein aggregates in the transgenic mouse brain (Fig. 4g,h), providing evidence that both\nrepeat RNA and its RAN-translated product are expressed at biologically relevant levels in\nthis model. We acknowledge, however, that the transgene RNA expression level represents\nonly a proxy for toxic RNA burden. The FMR1 5′UTR transgene produces both expanded\nrepeats RNA and FMRpolyG, and several molecular features, including secondary structures\n(Broniarek et al., 2024), R-loop formation (Loomis et al., 2014), nuclear foci accumulation\n(Hagerman et al., 2013), and RNA–protein interactions (Jin et al., 2007; Sofola et al., 2007;\nSellier et al., 2010), can affect the localization, stability, and function of repeat RNA. These\nfactors mean that measurable RNA abundance may not directly equate to pathogenic activity.\nA limitation of this study is that our current design does not allow us to dissect the relative\ncontributions of CGG repeats RNA versus FMRpolyG protein to the observed phenotypes.\nFuture studies employing strategies that selectively inhibit RAN translation or RNA\n26\nytilibaiV\nlleC\nevitaleR\n✱✱✱\n✱✱✱\naccumulation, together with cell-type–resolved quantification approaches (e.g., targeted or\nlong-read RNA sequencing, RNA spatial imaging), will be important to clarify contributions\nof toxic RNA and FMRpolyG in FXTAS.\nWe also agree that repeat-length instability could influence phenotypic variability. The\nconstruct used in this study carries 99 CGG repeats within the human FMR1 5′UTR and has\npreviously been reported to stably integrate into the mouse genome (Sellier et al., 2017). To\nfurther confirm this, we performed capillary electrophoresis assays using PCR primers\nspanning the FMR1 5′UTR repeat region (F: /56-\nFAM/TCAGGCGCTCAGCTCCGTTTCGGTTTCA; R: AGCCCCGCACTTCCACCACCAGCTC)\nin cortex, cerebellum, hippocampus, and control samples. We observed a consistent size of\nCGG repeats across all three brain regions in FXTAS mice (FMRpolyG+CGG_RNA; Nestin-\nCre(+)), suggesting that the CGG repeats in our transgene are stable in these brain regions.\nBroniarek, I., Niewiadomska, D. and Sobczak, K., 2024. Contribution of DNA/RNA Structures\nFormed by Expanded CGG/CCG Repeats Within the FMR1 Locus in the Pathogenesis of\nFragile X-Associated Disorders. Wiley Interdisciplinary Reviews: RNA, 15(6), p.e1874.\nLoomis, E.W., Sanz, L.A., Chédin, F. and Hagerman, P.J., 2014. Transcription-associated R-\nloop formation across the human FMR1 CGG-repeat region. PLoS genetics, 10(4),\np.e1004294.\nJin, P., Duan, R., Qurashi, A., Qin, Y., Tian, D., Rosser, T.C., Liu, H., Feng, Y. and Warren,\nS.T., 2007. Pur α binds to rCGG repeats and modulates repeat-mediated\nneurodegeneration in a Drosophila model of fragile X tremor/ataxia\nsyndrome. Neuron, 55(4), pp.556-564.\nHagerman, P., 2013. Fragile X-associated tremor/ataxia syndrome (FXTAS): pathology and\nmechanisms. Acta neuropathologica, 126(1), pp.1-19.\nSofola, O.A., Jin, P., Qin, Y., Duan, R., Liu, H., de Haro, M., Nelson, D.L. and Botas, J., 2007.\nRNA-binding proteins hnRNP A2/B1 and CUGBP1 suppress fragile X CGG premutation\nrepeat-induced neurodegeneration in a Drosophila model of FXTAS. Neuron, 55(4),\npp.565-571.\nSellier, C., Rau, F., Liu, Y., Tassone, F., Hukema, R.K., Gattoni, R., Schneider, A., Richard,\nS., Willemsen, R., Elliott, D.J. and Hagerman, P.J., 2010. Sam68 sequestration and\npartial loss of function are associated with splicing alterations in FXTAS patients. The\nEMBO journal, 29(7), pp.1248-1261.\nMinor comments:\n1. L 67 “or a slight reduction in FMRP protein levels”. Add this is propotional/related to the\namount of length increase of the CGG repeat\nThanks for your suggestion. We have revised the description in Lines 63-65, as “FXTAS\ncarriers exhibited increased mRNA levels of FMR1 but displayed either no detectable or a\nslight reduction in FMRP protein levels, which is proportional to the length of the expanded\nCGG repeats.”\n2. L74 “This leads to the …….FMRployG.”. Addional RAN translation products are also made\nWe thank the reviewer for this helpful suggestion. We have revised Lines 72-74 of the revised\nmanuscript to acknowledge other RAN translation products: \"This leads to the generation of\npolyglycine-containing protein, FMRpolyG, along with additional repeat-associated non-AUG\n(RAN) translation products.\"\n27\n3. L134 “…with the cortex exhibiting a subsequent level of observation”. Meaning of this\nsentence not clear\nThank you for highlighting this unclear wording. We have revised this sentence (Line 135-\n136) to improve clarity:\"...with the cortex showing an intermediate number of DEGs relative\nto the cerebellum and hippocampus.\"\n4. L142 “Hist1he1”. Perhaps relevant to mention this is an established ID gene\nWe thank the reviewer for this suggestion. We have mentioned this is an established ID gene\nin Line 145-146 of the revised manuscript as: \"Hist1h1e, an established intellectual disability\n(ID)-associated gene (Duffney et al., 2018), was the only shared DEG identified across all\nthree brain regions.\"\nDuffney, L.J., Valdez, P., Tremblay, M.W., Cao, X., Montgomery, S., McConkie-Rosell, A. and\nJiang, Y.H., 2018. Epigenetics and autism spectrum disorder: A report of an autism case\nwith mutation in H1 linker histone HIST1H1E and literature review. American Journal\nof Medical Genetics Part B: Neuropsychiatric Genetics, 177(4), pp.426-433.\n5. L151 “the widespread overlap … in upregulated genes”. Explain to the reader l255(in half\na sensence or so) why this is the case\nWe appreciate the reviewer’s suggestion. We have revised the manuscript at Line 150-153 as\nfollows: \" Cross-region comparisons (Fig. 1e) showed that downregulated genes exhibited\ngreater overlap than upregulated genes, likely reflecting conserved pathways involved in\nneuronal dysfunction or cell loss across regions (Fröhlich et al., 2024).\"\nFröhlich, A.S., Gerstner, N., Gagliardi, M., Ködel, M., Yusupov, N., Matosin, N., Czamara, D.,\nSauer, S., Roeh, S., Murek, V. and Chatzinakos, C., 2024. Single-nucleus transcriptomic\nprofiling of human orbitofrontal cortex reveals convergent effects of aging and psychiatric\ndisease. Nature neuroscience, 27(10), pp.2021-\n2032.\n6. L255-259 “on the difference in phenotypic presentation between males/females”. These\nfindings to some extend parallel the gender differences in FXTAS patients. However, in man,\nthe locus is on the X, whereas here the expansion is autosomal perhaps suggesting this\ndifference may not solely be due to the presence of the expansion on the X chromosome. This\ncould be mentioned in this paper (here or in the discussion)\nWe thank the reviewer for this suggestion. In our study, we observed sex-specific differences\nin disease onset and progression: male FXTAS mice exhibited earlier locomotor deficits,\nwhereas female mice showed delayed but progressive changes, including earlier onset of\nweight gain. These findings parallel clinical observations in FXTAS patients, in which\npenetrance, age of onset, and severity are generally higher in males than in females\n(Hagerman & Hagerman, 2016). In humans, this sex difference is often attributed to the X-\nlinked nature of the FMR1 locus, with protective effects from the second X chromosome in\nfemales. However, as the reviewer notes, our model employs an autosomal integrated\ntransgene under the control of a CAG promoter. Therefore, the observed sex-specific\nphenotypes in mice cannot be explained solely by X-linked dosage compensation but may\ninstead reflect additional biological factors such as hormonal influences, metabolic\n28\ndifferences, or intrinsic sex-specific neuronal vulnerabilities. We have now incorporated this\npoint into the Discussion (Line 494-502).\nHagerman, R.J. and Hagerman, P., 2016. Fragile X-associated tremor/ataxia syndrome—\nfeatures, mechanisms and management. Nature Reviews Neurology, 12(7), pp.403-412.\nWe sincerely thank the reviewers for their time and thoughtful comments. We are pleased\nthat all reviewers now consider the manuscript ready for publication, and we truly appreciate\nthe constructive feedback that has helped strengthen our work.\nReviewer #1:\nThe authors have adequately addressed all my concerns. Thanks.\nReviewer #2:\nThe authors have addressed my review point by point. They conducted additional\nexperiments, for example to quantify the density of GABAergic interneurons in the PFC of\nFXTAs mice, doing Westerns to quantify PRKCG expression (though not immunos), in vitro\nstudies to overexpress/knockdown Prkcg in cultured neurons to look for effects on cell\nviability (not quite in vivo, but understandable)\nThe also completed several new analyses, especially to address the potential confound\nof weight gain in the rotarod deficits of the mutants, and answered my questions about\nstatistics, or clarified points when I was confused.\nOverall, this paper represents an important contribution to the field. I sincerely hope\nmy feedback helped to strengthen the paper.\nI have no further concerns or comments.\nReviewer #3:\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\nReviewer #4:\nThe author has addressed and resolved the questions and suggestions I raised in detail, and\nI have no further questions. However, I still believe that a uniform standard for identifying\ndifferentially expressed genes is preferable, as it allows for a more objective assessment of\nheterogeneity across different stages.\nWe thank the reviewer for this thoughtful comment and fully appreciate the importance of\nusing a uniform standard for identifying differentially expressed genes to facilitate objective\ncomparisons across stages. In our revision process, we carefully considered this\nrecommendation. As noted in the manuscript, differences in data structure, sample size,\nand platform-specific characteristics across datasets required us to adapt analytical\nthresholds to ensure robust detection of meaningful signals within each cohort.\nNevertheless, we agree that standardization is ideal, and we have highlighted this issue as\nan important area for future methodological refinement. We appreciate the reviewer’s\ninsight, which has helped us further improve the clarity and transparency of our analysis.\nReviewer #5:\nThe authors have carefully incorporated the suggestions in the manuscript. I have no further\ncomments."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have adequately addressed all my concerns. Thanks.", "reviewer_2": "The authors have addressed my review point by point. They conducted additional experiments, for example to quantify the density of GABAergic interneurons in the PFC of FXTAs mice, doing Westerns to quantify PRKCG expression (though not immunos), in vitro studies to overexpress/knockdown Prkcg in cultured neurons to look for effects on cell viability (not quite in vivo, but understandable)\nThe also completed several new analyses, especially to address the potential confound of weight gain in the rotarod deficits of the mutants, and answered my questions about statistics, or clarified points when I was confused.\nOverall, this paper represents an important contribution to the field. I sincerely hope my feedback helped to strengthen the paper.\nI have no further concerns or comments.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "The author has addressed and resolved the questions and suggestions I raised in detail, and I have no further questions.\nHowever, I still believe that a uniform standard for identifying differentially expressed genes is preferable, as it allows for a more objective assessment of heterogeneity across different stages.", "reviewer_5": "The authors have carefully incorporated the suggestions in the manuscript. I have no further comments."}, "author_response": "Response to Reviewers’ Comments\nWe sincerely thank the reviewers for their time and thoughtful comments. We are pleased\nthat all reviewers now consider the manuscript ready for publication, and we truly appreciate\nthe constructive feedback that has helped strengthen our work.\nReviewer #1:\nThe authors have adequately addressed all my concerns. Thanks.\nReviewer #2:\nThe authors have addressed my review point by point. They conducted additional\nexperiments, for example to quantify the density of GABAergic interneurons in the PFC of\nFXTAs mice, doing Westerns to quantify PRKCG expression (though not immunos), in vitro\nstudies to overexpress/knockdown Prkcg in cultured neurons to look for effects on cell\nviability (not quite in vivo, but understandable)\nThe also completed several new analyses, especially to address the potential confound\nof weight gain in the rotarod deficits of the mutants, and answered my questions about\nstatistics, or clarified points when I was confused.\nOverall, this paper represents an important contribution to the field. I sincerely hope\nmy feedback helped to strengthen the paper.\nI have no further concerns or comments.\nReviewer #3:\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\nReviewer #4:\nThe author has addressed and resolved the questions and suggestions I raised in detail, and\nI have no further questions. However, I still believe that a uniform standard for identifying\ndifferentially expressed genes is preferable, as it allows for a more objective assessment of\nheterogeneity across different stages.\nWe thank the reviewer for this thoughtful comment and fully appreciate the importance of\nusing a uniform standard for identifying differentially expressed genes to facilitate objective\ncomparisons across stages. In our revision process, we carefully considered this\nrecommendation. As noted in the manuscript, differences in data structure, sample size,\nand platform-specific characteristics across datasets required us to adapt analytical\nthresholds to ensure robust detection of meaningful signals within each cohort.\nNevertheless, we agree that standardization is ideal, and we have highlighted this issue as\nan important area for future methodological refinement. We appreciate the reviewer’s\ninsight, which has helped us further improve the clarity and transparency of our analysis.\nReviewer #5:\nThe authors have carefully incorporated the suggestions in the manuscript. I have no further\ncomments."}]

Decision letter