Long-read sequencing identifies ATXN3 repeat expansions, and transcriptomics reveals disease progression biomarkers and druggable targets for spinocerebellar ataxia type 3

Abstract

Background

Hereditary ataxias (HAs) are neurodegenerative disorders characterized by progressive cerebellar degeneration, with autosomal dominant spinocerebellar ataxias (SCAs) representing the most prevalent subtype. SCA3, the most common form worldwide, is caused by CAG repeat expansions in ATXN3, resulting in pathogenic ataxin-3 aggregation. However, the underlying molecular mechanisms driving disease progression remain incompletely understood.

Methods

We utilized an integrated multi-omics strategy to investigate a five-generation Chinese HA pedigree. Genetic analyses included targeted ataxia panel sequencing (TS), whole-exome sequencing (WES), and long-read whole-genome sequencing (LR-WGS) of blood-derived DNA to identify causal variants and confirm diagnosis. Transcriptomic profiling revealed disease-associated gene expression signatures, followed by functional annotation and cross-species validation. To ensure analytical rigor, we further validated our bioinformatic pipeline using an independent ulcerative colitis (UC) dataset.

Results

Genetic analysis identified pathogenic ATXN3-CAG repeat expansions that co-segregated with clinical symptoms in affected family members. Transcriptomic profiling showed significant enrichment in ECM-receptor interaction and focal adhesion pathways, along with immune dysregulation and RNA splicing defects associated with disease progression. Cross-species analysis discovered conserved blood biomarkers (C3/ALS2/SLC35A2↓ and THBS1/CAMTA1↑), strongly correlated with clinical progression. Protein-protein interaction network emphasized AKT1 as a central regulator, along with other key hubs (e.g., TGFB1, MAPK3, CALM3, APP), while brain-specific analyses highlighted Mobp, Mal, Gja1 and Klk6 as potential therapeutic targets.

Conclusions

This study genetically confirms SCA3 in a Chinese pedigree using LR-WGS, overcoming the diagnostic limitations of short-read sequencing. Comprehensive analyses revealed conserved SCA3 progression signatures with potential biomarkers for future non-invasive monitoring. Mechanistically, this study identified dysregulation in ECM-receptor interaction/focal adhesion, immune response, and RNA splicing as key pathogenic contributors. These findings provide both actionable therapeutic targets and demonstrate the clinical utility of integrated multi-omics approaches for SCA3 diagnosis and patient stratification, with broader implications for repeat expansion disorders.

Trial registration

Not Applicable.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-025-04378-z.

Introduction

Hereditary ataxia (HA) represents an extensively wide group of clinically and genetically heterogeneous neurodegenerative and movement disorders characterized by progressive dysfunction of the cerebellum and degeneration of spinocerebellar tracts and the spinal cord, with spinocerebellar ataxia (SCA) being the most common autosomal dominant subtype [1, 2]. Globally, the prevalence of dominant HA/SCA ranges from 0.0 to 5.6 per 100,000 individuals (0.0-5.6/10⁵), with an average estimate of 2.7/10⁵ (1.5-4.0/10⁵) and higher rates observed in isolated populations [3, 4]. SCA’s epidemiology varies greatly across regions, depending on factors such as historical ancestries, population migration, founder effects, and regional diagnostic capabilities [5–8]. SCA3, or Machado-Joseph disease (MJD), stands out as the most prevalent SCA worldwide, with a striking geographical distribution driven by founder effects [5–8]. SCA3 is very frequent in Portugal, particularly in the Azores Islands/Flores Island [4, 9], and also high frequent in Brazil [7, 10, 11], China [12, 13], Germany, and the Netherlands [9]. By contrast, SCA3 is less frequent in Mexico, Peru, Australia, India, South Africa, and Italy [5, 7, 9, 11, 13]. The regional clustering of population prevalence underscores the impact of historical migration and genetic drift on SCA3 dissemination. The uneven global distribution emphasizes the importance of targeted genetic screening in high-risk areas, such as Portugal and East Asia, to enable early diagnosis and intervention.

The diagnosis of SCA integrates regional epidemiology, clinical evaluation (family history), laboratory assessment, and molecular genetic testing. Clinicians assess characteristic ataxia symptoms such as gait disturbances, poor coordination, and dysarthria, and autosomal dominant inheritance patterns [14], with definitive confirmation through the detection of pathological variants [14, 15]. According to the OMIM database (Phenotypic Series - PS164400), there are up to 40 genes and 45 loci associated with 48 SCAs, most of them belonging to nucleotide repeat expansion disorders (REDs). Next-generation sequencing (NGS) has improved diagnosis of inherited ataxia, but its short-read lengths usually fail to accurately detect large repeat expansions, leading to underestimation and diagnostic uncertainty [16]. Long-read whole-genome sequencing (LR-WGS) could overcome these limitations by precisely measuring repeat expansion length and identifying complex structural variants (SVs) and copy number variants (CNVs) undetectable by conventional NGS, significantly enhancing diagnostic accuracy for SCAs [17–19].

The pathophysiology of SCA3 stems from abnormal CAG repeat expansion in the ATXN3 gene, leading to production of ataxin-3 protein with an extended polyglutamine (polyQ) tract that misfolds and then forms neuronal aggregates [5, 20]. These toxic inclusions predominantly accumulate in the cerebellum, brainstem, and spinal cord, disrupting neuronal function and causing widespread neurodegeneration [5, 20]. The nucleotide repeat disorders like SCA3 exhibit genetic anticipation, with longer tandem repeats correlating with earlier onset and increased severity across generations [5]. Normally, ataxin-3 functions as a deubiquitinating enzyme that maintains protein homeostasis by cleaving ubiquitin chains and aiding misfolded proteins for proteasomal degradation. However, the expanded polyQ of ataxin-3 impairs this function, resulting in accumulation of toxic protein aggregates [21]. These aggregates disrupt multiple cellular processes like protein degradation, mitochondrial function, and neuronal pathways, ultimately leading to neuronal death and progressive neurodegeneration [5, 20, 22].

Transcriptomic profiling through RNA sequencing (RNA-seq) have advanced our understanding of the molecular mechanisms of disease and identified potential molecular biomarkers for monitoring SCA3 disease progression. Studies have revealed dysregulated genes in the blood of SCA3 patients, with their expression correlating to disease severity [23–25]. These findings highlight the utility of less-invasive and transcriptional blood biomarkers for tracking brain disease progression and evaluating therapeutic efficacy, offering valuable tools for basic research and clinical management.

In this study, we conducted comprehensive clinical and molecular genetic investigations on a Chinese pedigree presenting with suspected hereditary cerebellar ataxia. By employing multi-omic approaches including advanced sequencing technologies and transcriptome profiling, we intended to: (1) elucidate the molecular etiology of this family, (2) discover novel blood-based biomarkers for disease progression monitoring, and (3) explore key hub genes and potential therapeutic targets. Our integrated approach aims to improve diagnostic accuracy, facilitate early intervention, and contribute to the development of targeted therapies for this ataxia through cutting-edge genomic and transcriptomic analyses.

Materials and methods

Hereditary ataxia pedigree

We recruited a five-generation family with suspected hereditary ataxia from the Neurology Department of the First Traditional Chinese Medicine Hospital in Changde City, Hunan Province, China, comprising fourteen family members who underwent genetic testing (Fig. 1A). Symptomatic individuals received a standardized neurological workup like clinical examination, routine laboratory tests, brain magnetic resonance imaging (MRI), and electronystagmography (ENG). Additionally, three neuropsychological assessments — Zung’s Self-Rating Anxiety Scale (SAS) [26], Self-Rating Depression Scale (SDS) [27], and the Symptom Checklist-90-Revised (SCL-90-R) [28] were administered. All participants provided informed consent for the use of clinical data, peripheral blood samples, and genetic analysis; and this study was approved by the ethics committees of the First Traditional Chinese Medicine Hospital and Harbin Medical University.

Fig. 1.

The pedigree and MRI findings of the SCA3 cohort. (A) The pedigree identifies 14 tested members (asterisks) with color-coding: black (severe symptoms), dark gray (mild symptoms), and light gray (asymptomatic carriers). Age data (onset in red/current in black) and CAG repeat numbers (normal in black and expanded in red, formatted as Allele1/Allele2) are displayed. Fourteen members underwent molecular testing: IV-4, IV-11, IV-13, V-2 completed PCR-capillary electrophoresis and T-A cloning sequencing; ten others additionally had WES; the proband II-6 also received TS and LR-WGS. All 14 individuals contributed to RNA-seq. (B-E) MRI scans of severe individuals show: left panels — WM hyperintensities in the periventricular (B,C,E; arrows), subcortical (C; arrowhead), and left ventricular posterior horn (D) regions; right panels — atrophy in the cerebellar vermis/peduncles (B, circles) and posterior corpus callosum (C-E, arrows).

Magnetic resonance imaging

MRI examinations were conducted on a 1.5 T Siemens MAGNETOM Avanto scanner using a dedicated head coil, following patient safety screening and standardized supine positioning with eyes closed. The imaging process commenced with a three-plane localizer (TR/TE = 20/5 ms, 20 s) for spatial planning, followed by diagnostic sequences: T1-weighted imaging (TR/TE = 500/15 ms, 2–5 min) for anatomical reference, T2-weighted (TR/TE = 4000/100 ms, 3–6 min) for pathological evaluation, fluid attenuated inversion recovery (FLAIR, TR/TI = 9000/2500 ms, 4–7 min) with cerebrospinal fluid suppression to enhance lesion conspicuity, diffusion-weighted imaging (DWI, b = 1000 s/mm², 1–2 min) for acute ischemic detection, and susceptibility-weighted imaging (SWI, Three-dimensional Gradient Recalled Echo, 3–5 min) for microhemorrhage and calcification assessment, with BLADE/PROPELLER motion correction applied throughout. Post-processing methods included multiplanar reconstruction (MPR) for anatomical correlation and maximum intensity projection (MIP) for vascular visualization, ensuring comprehensive diagnostic interpretation while maintaining optimal image quality.

High-throughput sequencing

Targeted, whole-exome, and long-read whole-genome sequencing

Genomic DNA was extracted from peripheral blood using the FlexiGene DNA Kit (QIAGEN, 51206). To identify causal variants, short-read ataxia targeted sequencing (TS, 1954 neurological disease-related genes) were performed on the proband II-6 and whole-exome sequencing (WES) were conducted on ten family members (II-6, III-2, III-6, III-8, III-10, III-12, III-14, III-16, III-19, and IV-10) using the Illumina NovaSeq 6000 System (Novogene, Beijing). Raw data were processed with Illumina bcl2fastq, aligned to the GRCh37/hs37d5 reference genome using Burrows-Wheeler Aligner (v0.7.8-r455) [29], and deduplicated using Sambamba (v0.6.6) [30]. The proband II-6 also underwent Oxford Nanopore LR-WGS (Biomarker, Beijing); raw signals were base-called with Guppy, quality-filtered, and aligned using Minimap2 [31, 32]. STR analysis was performed using RepeatHMM [33, 34], which employs a hidden Markov model and Gaussian mixture model-based peak detection for repeat count inference.

Transcriptome sequencing

Total RNA was extracted from peripheral blood samples, with integrity and quantity verified using an Agilent 2100 Bioanalyzer to ensure sequencing suitability. Sequencing libraries were prepared with the RNA Library Preparation Kit (NEB, E7770L) and subjected to paired-end sequencing (2 × 150 bp) on the Illumina NovaSeq 6000 System (Novogene, Beijing). Raw FASTQ were quality-filtered to remove low-quality reads, and all subsequent analyses were performed using the high-quality clean data.

Length of CAG repeat expansion

To precisely quantify ATXN3-CAG repeat expansions, we utilized a multi-method approach combining direct PCR-Sanger sequencing, PCR-capillary electrophoresis, and T-A cloning followed by sequencing. PCR was performed using a FAM-labeled forward (5′-CCAGTGACTACTTTGATTCGTGA-3′) and reverse (5′-GAATGGTGAGCAGGCCTTAC-3′) primers. Direct PCR-Sanger sequencing determined the short CAG repeats; while long repeat quantification was performed by capillary electrophoresis data, with expansion sizes calculated as: [flanking sequences + (CAG count × 3)]. Using the proband II-6’s established flanking sequences (182 bp for expanded, 174 bp for normal alleles), we determined repeat lengths for all affected individuals, enabling consistent measurement of both normal and pathogenic expansions throughout the pedigree. T-A cloning followed by sequencing validated the previous results.

Data acquisition

To address the scarcity of blood transcriptome data from SCA3 patients across different disease stages, we incorporated mouse models for cross-species validation, comparing blood differentially expressed genes (DEGs), expression patterns, functional clusters, and protein-protein interaction (PPI) hub nodes associated with disease severity both in SCA3 family members and mouse models. This comparative approach significantly strengthened our findings, as human and mouse blood transcriptomes showed remarkable consistency in gene expression patterns. Mouse model selection and dataset utilization were guided by four criteria: (A) Pathological severity alignment based on age, genotype, and CAG repeat length; (B) Cross-species pathological comparability; (C) Availability of high-throughput sequencing (HTS) data for both blood and relevant brain regions in SCA3 and control mice; and (D) Inclusion of ≥ five matched SCA3-control pairs in HTS analyses. Mouse datasets were publicly available at the NCBI GEO (https://www.ncbi.nlm.nih.gov/geo/; GSE145613, GSE107958, GSE108069; Table S2–Sheet1) [35, 36], while human blood RNA-seq data are provided in the appendix. All analyses were conducted using R (v4.3.1).

Differentially expressed genes analysis

Family individuals were stratified into three severity groups: Severe [II-6, III-2, III-6, and III-14; Mean (± SD) current age was 64.25 ± 14.38], Mild-Asymptomatic [III-16, IV-10, IV-4, IV-11, and IV-13; Mean (± SD) current age was 32.60 ± 10.50], and Normal [III-8, III-10, III-12, and III-19; Mean (± SD) current age was 53.50 ± 5.32]. Mouse severity classifications followed the original studies [35, 36]. Differential expression analysis was performed using DESeq2 (v1.40.2) [37] with significance thresholds set at p < 0.05 and |log₂FC| ≥ 0.5 for both human and mouse datasets.

Cluster and enrichment analysis

Raw data were normalized as fragments per kilobase per million mapped reads (FPKM) for differential expression analysis. Following DEG identification, we performed mRNA expression trend clustering using ClusterGVis (v0.1.1) (https://github.com/junjunlab/ClusterGVis) and then conducted functional enrichment analyses (GO/KEGG) for each cluster. All analyses employed clusterProfiler (v4.8.2) [38] for enrichment and Gene Set Enrichment Analysis (GSEA), with gene ID conversion performed using org.Hs.eg.db (v3.17.0) [39] and org.Mm.eg.db (v3.17.0) [40] for human and mouse respectively, along with biomaRt (v2.56.1) [41] for cross-species gene mapping. The results were visualized using GseaVis (v0.0.9) (https://github.com/junjunlab/GseaVis).

Molecular hub and draggability analysis

PPI networks were constructed using the STRING (confidence threshold: 0.4) (https://cn.string-db.org/) and visualized in Cytoscape (v3.10.2). Hub nodes were identified via cytoHubba’s Degree algorithm, with the top ten genes classified as molecular hubs. Potential therapeutic targets were further screened using the Open Targets Platform (https://platform.opentargets.org/) and Therapeutic Target Database (https://db.idrblab.net/ttd/), prioritizing genes with existing clinical applications or trial evidence.

Results

Neurological, imaging, and neuropsychiatric abnormalities in the HA pedigree

The proband II-6 presented with a 7-year history of urinary urgency/frequency (recently worsened), progressive lower limb weakness, gait ataxia, dysarthria, and functional dependence, with a background of grade 3 hypertension (systolic blood pressure > 180 mmHg, long-term use of amlodipine besylate) and posterior circulatory ischemia but no history of trauma, stroke, or psychiatric signs. This pedigree revealed consanguinity (II-6 and husband II-5 were first cousins) and multi-generational involvement (I-1; II-2, II-6; III-2, III-3, III-6, III-14, III-16; IV-10) with similar motor symptoms. Evaluation of six affected individuals (II-6, III-2, III-6, III-14, III-16, IV-10; Table S1–Sheet1) demonstrated cerebellar dysfunction, oculomotor abnormalities, and extrapyramidal signs, with the II-6, III-2, III-6, and III-14 exhibiting more severe manifestations than III-16 and IV-10. Earlier onset in successive generations (particularly on the right branch) suggested genetic anticipation (Fig. 1A). Routine lab tests (hematology, hepatic and renal function, and metabolic panels; Table S1–Sheet2) were unremarkable. These findings roughly correspond to SCA presentation.

Cranial MRI in the proband indicated cerebral small vessel disease (CSVD) and demyelinating lesions, characterized by punctate long T1/T2 signals and T2-FLAIR hyperintensities in periventricular white matter (WM), accompanied by mild posterior corpus callosum atrophy and severe cerebellar vermis/peduncle atrophy (Fig. 1B). These neuroimaging abnormalities were consistently observed across all five symptomatic patients (III-2, III-6, III-14, III-16, IV-10), with myelinopathy and varying degrees of cerebellar/callosal atrophy present in all cases, while CSVD features were absent only in III-6 (Fig. 1C-E, S1A; Table S1–Sheet1). Complementary ENG test revealed vestibular dysfunction and spontaneous nystagmus in four patients (III-2, III-6, III-14, III-16; Table S1–Sheet1). These findings further support central neurological involvement, despite the relative rarity of corpus callosum atrophy and CSVD in SCA spectrum disorders.

Five symptomatic patients subsequently underwent comprehensive neuropsychiatric evaluation using three standardized scales. The Zung’s SAS and SDS scales identified mild anxiety and depression in III-2 and mild depression in III-6, while the SCL-90-R scale revealed suboptimal physical and mental health states in III-2, III-6, and III-16 (Table S1–Sheet1). In addition, the clinical mental state score indicated moderate cognitive impairment in III-2, while mild cognitive impairment in III-6 and IV-10 (data not shown).

Identification and validation of the abnormal ATXN3 CAG repeat expansion

We implemented a sequential genetic testing strategy, beginning with targeted HA panel (having 1954 neurological disease-related genes and all known SCA genes through 2020) sequencing, which failed to discover pathogenic variants. Given the presence of unusual clinical features (e.g., corpus callosum atrophy and CSVD), incomplete genetic anticipation, and the absence of pathogenic findings on TS, we hypothesized a novel genetic basis for this familial ataxia. However, WES and linkage analysis across ten family members did not discover disease-causing variants or suggestive loci. LR-WGS ultimately identified a pathogenic 69-CAG repeat expansion in ATXN3 exon of the proband II-6. This variant was missed by WES and TS probably due to limitations in exome capture and short-read variant calling (Fig. 2A-B). PCR-capillary electrophoresis confirmed ATXN3-CAG repeat expansions (68–74 repeats) co-segregating with ataxia phenotypes across affected members (Fig. 2C, S1B). Notably, asymptomatic carriers (IV-4, IV-11, IV-13) harbored 71–74 CAG repeats but had not reached disease onset age, while unaffected individuals (III-8, III-10, III-12, III-19, V-2) showed normal repeat ranges (14–28 repeats; Table S1–Sheet3). T-A cloning and subsequent sequencing validated the expansions in nine patients (II-6, III-2, III-6, III-14, III-16, IV-10, IV-4, IV-11, IV-13; Fig. S1C). SCA3/MJD exhibits two distinct ancestral backgrounds: the Machado lineage, originally identified in the Azores Islands (Portugal) and predominantly found in Portuguese descendants; and the Joseph lineage, potentially of Asian origin with global distribution [42]. To determine the ancestral origin of ATXN3-CAG repeat expansion in this family, we integrated LR-WGS for comprehensive genome-wide coverage with WES for precise exon-adjacent region analysis. Haplotype phasing of ten family members was performed using a panel of 32 SNPs (6 core, 14 extended core, and 12 supplementary) [42–44]. The proband II-6 demonstrated 96.88% (31/32) genetic similarity to the Joseph lineage when considering all 32 SNPs, while analysis of the 6 core SNPs indicated that the five additional affected members also exhibited high genetic concordance with the Joseph lineage (Table S1–Sheet4). These findings strongly implicate a Joseph lineage origin for the pathogenic expansion in this family.

Fig. 2.

Identification and validation of ATXN3 CAG repeat expansion. (A) The workflow outlines the LR-WGS pipeline. (B) LR-WGS and WES visualization reveal ATXN3 CAG repeat expansion and rs12895357 heterozygous variant, with a schematic illustrating the regional exon structure of ATXN3. Color coding: green (normal repeats) and yellow (expanded repeats). (C) PCR-capillary electrophoresis results validate the repeat expansions across affected individuals, confirming the genetic cosegregation of the pathogenic variant.

Using the model proposed by Tezenas du Montcel [45], we estimated the age of onset for SCA3 patients (Table S1–Sheet3) based on the published parameters (α₀ = 7.4908, αₑ = -0.0564, γ_G = 0.2167). The model predicted an earlier onset age for all patients except IV-10, whose prediction was later than the actual onset age, and IV-11 and IV-13 had not yet occurred (Fig. S1D). This may be due to differences in genetic background between races that may have an impact on prediction of disease onset age and the interactions between environment and genes. In addition, there may be certain errors in collecting data on patient age of onset like recall bias. Despite the above differences, this prediction is still of great value in early diagnosis of SCA3. This predictive model can provide clinicians with a reference for early risk assessment and help carry out disease monitoring and intervention in advance.

Upon re-examination of WES BAM files, we identified a heterozygous variant rs12895357 (Hg38, chr14:92071010, C > G; NM_004993.6: c.916G > C, p.Gly306Arg; Fig. 2B) in the proband II-6, with a minor allele frequency of 10.62% for the G allele (reads ratio C40/G16). This variant was observed in all affected individuals, appearing in homozygous state in III-6 and IV-10, and heterozygous state in III-2, III-14, and III-16, as well as in one unaffected individual (III-8). However, the variant’s location adjacent to the CAG repeat region of ATXN3 raises technical concerns regarding the reliability of short-reads in this genomic context. Comprehensive evaluation of rs12895357 through multiple approaches failed to establish its pathogenic significance. Population frequency data (G allele = 0.1062) and absence of clinical associations in the ClinVar database suggest it represents a benign polymorphism. eQTLs analysis revealed no significant regulatory effects on ATXN3 expression in the GTEx database. In-silico pathogenicity predictions yielded conclusive results of neutral variant, and 3D structural remodeling indicated the p.Gly306Arg substitution would not substantially perturb protein function. Analysis of other reported SCA3 modifier variants similarly failed to demonstrate significant cosegregation with disease status (disease onset and severity) or consistent phenotypic effects within this pedigree (Table S1–Sheet5). The potential cumulative impact of these variants remains uncertain given current sample size limitations and the complex genetic architecture of phenotypic modulation in SCA3. These findings suggest that while rs12895357 and other candidate variants were detected, their contribution to SCA3 pathogenesis or clinical variability appears minimal based on available evidence. Further investigation employing variant validation methods and expanded cohort studies would be required to definitively assess the role of genetic modifiers in SCA3.

Human blood: genes and pathways related to SCA3 progression

Differential gene expression analysis of human peripheral blood showed a total of 795 DEGs between the severe and normal groups, comprising 444 up-regulated and 351 down-regulated genes (Fig. 3A; Table S2–Sheet2). Functional GO/KEGG enrichment analysis of these DEGs highlighted significant associations (p.adjust < 0.05) with the coagulation/anticoagulation processes, viral response pathways, immune cell migration/activation, cytoskeletal organization, and key pathways such as ‘Focal Adhesion’ and ‘ECM-Receptor Interaction’ (Fig. S2A; Table S2–Sheet3).

Fig. 3.

The bioinformatic analysis of peripheral blood transcriptomes from grouped family members. (A) Differential gene expression analysis identifies 795 DEGs associated with SCA3 occurrence. (B-E) Progression-related analyses include: (B) GO/KEGG enrichment analysis of 957 DEGs; (C) expression clustering and the followed functional enrichment via heatmap (N, Normal; M-A, Mild-Asymptomatic; S, Severe); (D) GSEA for pathway-level insights; and (E) PPI network construction with hub node identification, highlighting key regulatory molecules. Color scale: red (most interactors) → yellow (fewest interactors).

To identify gene transcripts correlated with SCA3 progression, we analyzed two DEG datasets: Dataset A [243 genes; intersection of significant DEGs in (severe vs normal, p < 0.05) and (mild-asymptomatic vs normal, p < 0.05) comparisons] and Dataset B [838 genes; significant only in (severe vs normal, p < 0.05)]. From Dataset A, the 119 DEGs showing progressive expression changes (|log₂FC_{severe vs normal}| > |log₂FC_{mild−asymptomatic vs normal}|) were selected as potential biomarkers (Sub-Dataset A; Table S2–Sheet4). Combined analysis of the 957 DEGs (Sub-Dataset A and Dataset B) revealed significant enrichment (p.adjust < 0.05) in terms related to the coagulation/anticoagulation processes, cytoskeletal organization, and key pathways including ‘Focal Adhesion’, ‘ECM-Receptor Interaction’, and ‘Phagosome’ (Fig. 3B; Table S2–Sheet5). Trend analysis clustered these DEGs into four patterns, with cluster 1, 3, and 4 showing disease-stage-dependent expression: the cluster 1 enriched for coagulation and pathways like ‘Focal Adhesion’, ‘ECM-Receptor Interaction’, ‘Phagosome’, and ‘Oxidative Phosphorylation’; the cluster 3 for ‘RNA Splicing’, and cluster 4 for ‘Golgi to Endosome Transport’ (Fig. 3C; p < 0.01, Table S2–Sheet6). To capture genes with subtle mRNA changes and maximize biological insights, we performed GSEA analysis comparing ‘severe vs normal’ and ‘mild-asymptomatic vs normal’ groups. Pathways were prioritized based on the normalized enrichment scores (NES), with only ‘Focal Adhesion’ and ‘ECM-Receptor Interaction’ pathways meeting stringent thresholds (|NES_{severe vs normal}| > |NES_{mild−asymptomatic vs normal}|, |NES| > 1, p.adjust < 0.25; Fig. 3D; Table S2–Sheet7).

PPI network analysis (constructed via STRING and analyzed in Cytoscape) of the 957 human blood DEGs identified ten hub molecules potentially regulating SCA3 progression, and then ranked them by interaction degree: AKT1 (103), TGFB1 (57), MAPK3 (53), CALM3 (52), APP (50), H3-5 (42), ATM (42), PECAM1 (41), IFI35 (40), and VWF (40) (Fig. 3E; Table S2–Sheet8). We subsequently validated these genes by assessing their tissue-specific expression patterns using GTEx database profiles. With the exception of H3-5, all genes exhibited high expression levels in brain tissue (Fig. S2E). Notably, CALM3 showed significant expression in both neuronal and peripheral tissues (brain, heart, and skeletal muscle). As a key modulator of intracellular calcium homeostasis, its elevated neural expression implies potential involvement in neuronal excitability and synaptic plasticity — mechanisms that could underlie the neuronal dysfunction characteristic of SCA3 pathogenesis.

Mouse blood: genes and pathways related to SCA3 progression

To validate human findings, we further analyzed mouse blood RNA-seq dataset (GSE108069), defining two datasets: Dataset C [109 genes; intersection of DEGs from (MJD84.2 mice, 70w-84Q vs. -WT, p < 0.05) and (40w-84Q vs. -WT, p < 0.05)] and Dataset D [3,729 genes; significant only in (70w-84Q vs. -WT, p < 0.05)]. From Dataset C, 79 DEGs showing disease progressive changes were stored into Sub-Dataset C. Hierarchical clustering of the combined 3,808 genes (Sub-Dataset C and Dataset D; Table S2–Sheet9) revealed two major categories: clusters 5/6 and 7/8, with the clusters 6 and 8 showing age- and disease-dependent patterns in MJD84.2 mice. Functional enrichment identified the cluster 6 genes associated with RNA splicing, autophagy, and proteasome, while cluster 8 genes were linked to immune response (Fig. 4A; p < 0.005, Table S2–Sheet10). PPI network from the 3,808 mouse blood DEGs highlighted Trp53 (490 hits) and Akt1 (402 hits) as top hubs, with AKT1 conservation between species reinforcing its potential regulatory role (Fig. 4B; Table S2–Sheet11).

Fig. 4.

Cross-species blood transcriptomic analyses of SCA3 progression. (A) Mouse blood DEGs are clustered and functionally annotated, revealing disease-stage-associated patterns. (B) PPI network analysis identifies ten hub genes in mouse blood. Color scale: red (most interactors) → yellow (fewest interactors). (C) Intersection analysis highlights conserved DEGs between human and mouse blood. (D) Functional enrichment of the cross-species 218 DEGs elucidates evolutionarily conserved pathways in SCA3 pathogenesis.

Intersection analysis of human and mouse blood biomarkers identified 218 conserved DEGs as potential cross-species SCA3 progression biomarkers (Fig. 4C; Table S2–Sheet12), with functional enrichment highlighting mRNA splicing and spliceosome pathways (Fig. 4D; p.adjust < 0.05, Table S2–Sheet13). Further filtering for consistent expression trends [(log₂FC_{severe vs. normal or 70w-84Q vs. -WT} × log₂FC_{mild−asymptomatic vs. normal or 40w-84Q vs. -WT} > 0), |ΔFC| > 20%] yielded ten high-confidence candidates: seven positively (CAMTA1, IFIT2, IFIT3, IFITM1, PGRMC1, THBS1, TSC22D3) and three negatively (ALS2, C3, SLC35A2) associated with SCA3 severity, all of them with established neurological disease relevance (Table S2–Sheet14).

Mouse brain: genes and pathways related to SCA3 progression

To identify SCA3 progression biomarkers in the brain, we analyzed mouse RNA-seq from cerebellum, brainstem, and striatum in two datasets (GSE107958 and GSE145613). GSE107958 revealed 522, 2,346, and 2,046 DEGs in the above brain areas respectively, with 94 shared DEGs linked to SCA3 occurrence (Table S2–Sheet15 ~ 16). GSE145613 yielded a total of 3,398 cerebellar DEGs associated with SCA3 progression (Table S2–Sheet17). Intersection analysis identified 41 conserved brain DEGs (Fig. S2B; Table S2–Sheet18), functionally enriched in neurodevelopmental and metabolic pathways (Fig. S2C; p.adjust < 0.05, Table S2–Sheet19). PPI network analysis of these DEGs highlighted ten hub nodes, including four with therapeutic potential: Mobp and Klk6 (multiple sclerosis targets) [46, 47], Mal (B-cell leukemia target) [48], and Gja1 (clinical trials for diabetic foot ulcers/radiation syndrome) [49, 50] (Fig. S2D; Table S2–Sheet20 ~ 21).

Robustness assessment of an SCA3 bioinformatic pipeline

To validate the robustness of our SCA3 bioinformatic pipeline, we sought independent neurological or movement disorder datasets (PD/HD/other SCAs) with severity-stratified RNA-seq data, but none were available. We therefore applied our analytical framework to a non-neurological condition, ulcerative colitis (UC; GSE128682), comprising mucosal RNA-seq profiles from 16 healthy control patients, 14 remission, and 14 active-phase UC patients. Given the extensive UC-DEGs identified, we implemented more stringent statistical thresholds to enhance specificity.

Comparative transcriptomic analysis between active UC patients and healthy controls identified 666 DEGs (p.adjust < 0.05, |log₂FC| ≥ 2), consisting of 456 upregulated and 210 downregulated genes (Table S2–Sheet22). Employing our established analytical framework, we subsequently stratified the data into two distinct datasets: Dataset A, representing the intersection of DEGs significant in both active versus control and remission versus control (both p.adjust < 0.001) comparisons; and Dataset B, comprising DEGs significant in active versus control (p.adjust < 0.001) but not in remission versus control (p.adjust > 0.05) analyses (Table S2–Sheets22 ~ 23). This stratification yielded 2,015 DEGs in Dataset A and 2,927 DEGs in Dataset B. From Dataset A, we further refined the analysis by selecting a total of 1,144 DEGs exhibiting progressively increased expression during active disease (defined as |log₂FC_{Active UC vs. Controls}| > |log₂FC_{Remission UC vs. Controls}|) as candidate UC biomarkers. The combined 4,071 UC progression DEGs from Sub-Dataset A and Dataset B underwent comprehensive functional enrichment analysis, revealing significant associations with lymphocyte differentiation and immune regulation pathways (Table S2–Sheets24 ~ 25), and subsequently aligning with established mechanisms of immune pathway mediation in IBD and its subtypes [51].

PPI network analysis of these progression-associated DEGs identified ten central hub genes ranked by connectivity: TNF (565 interactions), IL6 (483), EGFR (462), IL1B (451), ACTB (447), IFNG (430), STAT3 (401), CD8A (396), PTPRC (358), and STAT1 (313) (Table S2–Sheet26). Prior studies have demonstrated elevated expression of both TNF and IL6 in active UC, with IL6 serving as a validated marker of disease activity [52]. Furthermore, P2RY13 activation of the IL6/STAT3 pathway contributes to intestinal barrier dysfunction and UC disease progression, corroborating the pathological relevance of our identified hub genes and signaling pathways [53]. The consistency between our identified UC progression-associated DEGs/central hub genes and previously validated UC biomarkers confirms the reliability and robustness of our SCA3 analytical pipeline.

Discussion

This study genetically confirmed a suspected hereditary cerebellar ataxia as SCA3 in a Chinese family through comprehensive genetic analysis. Blood transcriptomic profiling identified disease-associated DEGs involved in coagulation, immune regulation, and cytoskeletal dynamics, particularly highlighting dysregulation in FA and ECM-Receptor Interaction pathways with AKT1, TGFB1, MAPK3, CALM3, APP as central molecular hubs. Cross-species validation reinforced these findings, revealing conserved biomarkers (C3/ALS2/SLC35A2↓ and THBS1/CAMTA1↑) and demonstrating significant disturbances in immune and RNA splicing function. Brain-specific mRNA profiles in SCA3 mice uncovered critical neurodevelopmental and metabolic alterations, pinpointing promising therapeutic targets (Mobp, Mal, Gja1, Klk6). This work provides mechanistic insights into SCA3 pathogenesis while offering valuable biomarkers for disease monitoring and novel therapeutic avenues.

SCA3 phenotypic diversity and pathology

In this study, the family members with severe SCA3 (III-2, III-6, III-14) exhibited a high frequency of extrapyramidal signs, consistent with a previous report [54]. These manifestations reflect nigrostriatal dopaminergic pathway dysfunction, as evidenced by the reduced 99mTc-TRODAT-1 binding on brain single photon emission computed tomography (SPECT) [55]. Cranial MRI findings revealed variable atrophy in the corpus callosum, cerebellar vermis, peduncles, and suggested concurrent myelinopathy and CSVD, in this pedigree. While widespread WM damage and atrophy begin pre-ataxia [56, 57], with most prominent changes occurring in cerebellar vermis and peduncles [56–59], corpus callosum atrophy remains exceptionally rare in SCA3 patients [60], having been reported only in an Indonesian family [61] despite being common in TCF4-related intellectual disability [62–64]. Human SCA3 is associated with cerebellar demyelination, but the extent of cerebral cortical pathology remains uncertain [65, 66]. In contrast, various SCA3 mouse and cell models demonstrate consistent widespread demyelination and oligodendrocyte impairment [36, 65–68]. Blood-brain barrier impairment in SCA3, attributed to tight junction protein dysregulation [69], appears distinct from CSVD, which lacks clear documentation in SCA3. Additionally, vestibular eye movement abnormalities (e.g., central nystagmus, saccadic pursuit, and square-wave jerks) indicate multisystem neurological involvement. Neuropsychiatric symptoms — including cognitive decline, anxiety, and depression — represent prevalent features of SCA3 [70] and show progressive worsening with disease advancement [71, 72]. The underlying pathophysiology may involve widespread neuronal damage [73], cerebellar dopaminergic/metabolite dysfunction [73, 74], and cerebellar/brainstem atrophy [73, 75]. However, our pedigree exhibited no clear correlation between ataxia severity and neuropsychiatric/neuroimaging findings. The concurrent presentation of atypical features (corpus callosum atrophy, CSVD) with neuropsychiatric symptoms may reflect population-specific genetic influences, environmental factors, or study limitations inherent to small cohort sizes.

Genetic anticipation in SCA3 age trends

In STR-associated diseases, genetic anticipation commonly occurs, resulting in a decline in the age of onset over generations. In this SCA3 pedigree, the first and second generations exhibited relatively later onset, while the third generation typically presented symptoms at 40–45 years, and the fourth generation showed symptomatic individuals as early as 30 years, consistent with genetic anticipation. This trend is particularly evident in the right branch of this pedigree (II-6: 63 years, III-14: 44 years, IV-10: 30 years), correlating with progressive CAG expansion in ATXN3 (69 to 70 to 71 repeats). In the other branch, anticipation is less clear due to incomplete age-of-onset data; for instance, I-1 (55? years) and II-2 (40? years) relied on family recall, which may lack precision. SCA3 onset is also influenced by genetic and epigenetic factors, complicating the pattern. Notably, phenotypic heterogeneity exists even among male patients of similar ages — severe in III-6 (28/74 repeats) and III-14 (23/70 repeats) but mild in III-16 (23/68 repeats) — suggesting modifiers beyond CAG repeat length.

Genetic and epigenetic modifiers of SCA3 are diverse. Although ATXN3 CAG repeat length remains the primary determinant of clinical decline [76], other factors like CAG tract lengths in ATXN2 [77] and other polyQ-related genes [78–80], as well as common variants in ATXN3 [81, 82] and ATXN2 [83]. SCA3 onset and phenotype are also linked to genetic variants that affecting lipid metabolism [84], mitochondrial function [85], autophagy [86], DNA methylation [87], and neurotransmitters [88]. Recent genome-wide association [89] and WES [90] studies have identified dozens of modifiers. Rare exonic variants, such as PRKN-V380L (rs1801582, impairing mitophagy) [91], PIAS1-S510G (rs755539001, delaying onset via SUMOylation regulation) [92, 93], and a 9-bp duplication in ATXN2 (exacerbating phenotype) [94], further modulate SCA3. Mathematical algorithms integrating CAG length and polyQ-related gene data now aid in predicting onset age [78–80, 95, 96].

Precision medicine for SCA genetic diagnosis

In this study, HTS analysis was performed on proband II-6 or nine other members, including short-read TS, WES with genome linkage analysis, and LR-WGS, to determine causal variants in the SCA pedigree. Short-read TS and WES failed to reliably detect pathological variants, likely due to the high GC content in STR expansion regions of ATXN3, which complicates genetic variant capture, mapping, and calling. In contrast, LR-WGS provided a comprehensive genomic review, encompassing coding and non-coding regions, including STRs and telomeres, while improving sequence capture, mapping, and phasing. LR-WGS is particularly valuable for understanding the genetic architecture and consequences of SVs and STRs [97–100], and also excels in identifying insertions, transposable elements, complex deletions, and disease-related STRs, overcoming limitations of short-read WGS [101]. With optimized bioinformatic processing pipelines [98, 102], LR-WGS outperforms short-read methods in detecting pathogenic SVs and STRs, especially in REDs [97, 99, 103]. Although it has improved diagnostic yield [104, 105] and accuracy [106, 107] in known STR-associated diseases, these preliminary findings require validation in larger cohorts to confirm their clinical significance. Additionally, LR-WGS provides critical insights into STR configuration/haplotype phasing, intergenerational variation, phenotypic heterogeneity, and co-occurrence in SCA individuals [108–110].

The diagnosis of SCA3, caused by CAG repeat expansions in the ATXN3, requires a strategic approach balancing clinical assessment with appropriate molecular genetic testing. Initial evaluation should focus on characteristic symptoms like progressive ataxia, ophthalmoplegia, pyramidal impairment, dysarthria, and distinctive features like dystonia or bulging eyes, along with a detailed family history [111]. For the individuals with classic SCA3 presentation, targeted ATXN3 CAG repeat analysis using triplet repeat-primed PCR followed by capillary electrophoresis remains the gold-standard for its high accuracy, rapid turnaround, and cost-effectiveness [112–116]. Triplet repeat-primed PCR usually serves as an essential supplementary validation tool for pathogenic repeat expansion detection in novel STR disorders [117–120]. However, when clinical features overlap with atypical manifestations, other HAs or SCAs, and movement disorders, the combination of HA repeat expansion panel or triplet repeat-primed PCR and WES provides a practical intermediate method [121–124]. An alternative approach involves integrating WES raw data with specialized bioinformatic tools (e.g., ExpansionHunter, exSTRa, and STRetch) for repeat expansion analysis. While these methods enable rapid detection of small repeat expansions with high sensitivity and specificity, they remain limited in identifying large or novel repeat expansions and high-GC repeats [115, 125]. In diagnostically challenging individuals where initial molecular testing is negative, WGS offers comprehensive detection of repeat expansions and simultaneously evaluating for the other types of genetic etiologies. Thus, WGS has been endorsed by multiple authoritative institutions as the first-line approach for clinical genetic testing, including rare disease and RED analysis [116, 126–128]. For REDs specifically, WGS demonstrates near-perfect sensitivity and specificity (approaching 100%) relative to conventional triplet repeat-primed PCR assays [116]. Furthermore, WGS surpasses WES by providing comprehensive genome-wide coverage and enabling detection of all variant types across coding and non-coding regions [126]. A tiered diagnostic strategy — beginning with targeted triplet repeat-primed PCR for high-probability cases, progressing to targeted repeat expansion panels and WES for broader evaluation, and reserving WGS for complex cases — optimizes both diagnostic yield and resource utilization (Table S1–Sheet6). This strategy recognizes WGS’s growing importance in broad analysis while preserving PCR’s proven utility for routine SCA3 diagnostics.

In this study, the proband II-6 underwent comprehensive clinical, laboratory, and imaging evaluations, revealing presentations atypical for SCA, including corpus callosum atrophy, CSVD, and incomplete genetic anticipation. Since conventional SCA diagnostic frameworks rely on prior clinical confirmation — challenging in our pedigree — a multi-omics genomic analysis (TS/WES/LR-WGS) was performed. Although more time-consuming and costly than triplet repeat-primed PCR assays (Table S1–Sheet6), such an approach is justified in complex cases, as robust cost-effectiveness analysis often derive from large-scale cohort studies rather than single-family analyses. These findings underscore the need for diagnostic algorithms that integrate the specificity and sensitivity of genetic testing, cost-effectiveness, and re-evaluation of prior clinical and molecular data, advancing the paradigm for HAs/SCAs/REDs diagnosis.

ECM-receptor interaction and focal adhesion pathways in SCA3 pathogenesis

Transcriptomic profiling of SCA3 patient blood samples revealed conserved dysregulation in ‘ECM-Receptor Interaction’ and ‘Focal Adhesion’ pathways, mirroring findings in other neurodegenerative and neuropsychiatric disorders, including Parkinson’s disease (PD) [129, 130], Alzheimer’s disease (AD) [131, 132], and schizophrenia [133]. The extracellular matrix (ECM), a network of structural proteins (e.g., elastin, laminins, collagens) and glycans, interacts with cell-surface receptors (integrins, CD44, TLR2/4) to regulate neurodevelopmental processes such as neuronal migration, axonal guidance, and synaptic plasticity [134–136]. Focal adhesion (FA) facilitates ECM-cytoskeletal interactions, cellular experiments reveal that cytoskeletal disruption and integrin signaling defects arise from either ataxin-3 deficiency or its pathogenic expansion [137, 138]. Pre-clinical SCA3 models further demonstrate early cytoskeletal disruption, impaired axonal transport, and metabolic dysfunction [139, 140], indicating that ECM-FA-cytoskeleton disruption as a key driver of SCA3 pathology. The presymptomatic appearance of these defects positions ECM/FA pathways as attractive targets for early therapeutic intervention.

PolyQ-ataxin-3 disrupts cytoskeletal integrity, impairing axonal transport, neuronal morphology, and metabolic function, ultimately leading to neuronal cell death [137–140]. Defective integrin signaling via FA components destabilizes synaptic connections [137, 141], while ECM degradation and its aberrant receptor signaling disrupt neurodevelopmental processes and synaptic plasticity [134, 135, 141, 142]. These results identify ECM-FA-cytoskeleton dysregulation as a core pathogenic mechanism in SCA3, with peripheral tissue detection enabling potential early biomarker development.

Immune dysregulation and RNA splicing defects in SCA3

Immune dysregulation plays a significant role in SCA3 pathogenesis, as evidenced by altered immune cell migration/activation pathways in transcriptomic analyses. Atxn3-knockout mice exhibit lymphocyte depletion and impaired immune defense [143], while ataxin-3 deficiency suppresses innate immune responses via NOD2/TLR2 pathways [144]. PolyQ-ataxin-3 further disrupts myeloid cell function in patients and knock-in mice [145]. Neuroinflammation is supported by microglial/astrocyte activation and inflammatory gene upregulation in human SCA3 pons [146, 147] and mouse models [143, 148]. RNA splicing defects also contribute to SCA3 progression. PolyQ-ataxin-3 disrupts Cajal body function in snRNP biogenesis [149, 150], forms RNA foci that sequester MBNL1 splicing factors [151], and causes early spliceosome protein dysregulation [139]. These findings underscore immune dysfunction and RNA processing defects as key mechanisms in SCA3, offering additional therapeutic targets.

Conserved biomarkers and novel pathways in SCA3 progression

Ten conserved blood-derived DEGs were identified across human and mouse SCA3 models, with five key genes — C3↓, ALS2↓, SLC35A2↓, THBS1↑, CAMTA1↑ — showing strong neurological associations. C3 and THBS1, linked to ‘ECM-Receptor Interaction’, ‘Focal Adhesion’, and ‘Phagosome’ pathways, exhibit context-dependent effects: C3 reduction improves synaptic integrity in multiple sclerosis [152] and attenuates astrogliosis in epilepsy [153] but impairs neurodevelopment [154]; THBS1 elevation exacerbates epileptogenesis [155] yet protects against Aβ toxicity in AD [156]. Clinical findings show inverse correlations between C3 levels and neurological outcomes [157, 158], while THBS1 elevation correlates positively [159, 160], suggesting their biomarker potential.

ALS2 and CAMTA1 genes further demonstrate critical neurological roles. ALS2 regulates endosomal trafficking, mitochondrial localization, and axonal growth [161–163], with its deficiency causing motor deficits in mice [164] and humans [165]. CAMTA1 variants can lead to human ataxia [166, 167], and its knockout mice exhibit ataxia and Purkinje cell degeneration [168]. SLC35A2, a glycosylation regulator, is implicated in SCA3 due to its association with cerebellar atrophy and WM defects [169, 170]. These findings highlight novel biomarkers and therapeutic targets, bridging hematological transcriptomics with central nervous system (CNS) pathology.

Our findings align with and extend previous research on transcriptional dysregulation in SCA3/MJD, particularly regarding blood-based biomarkers identified across multiple cohort studies. Building on the work of Mafalda Raposo, who identified FCGR3B, P2RY13, and SELPLG as consistently upregulated genes from patient blood samples [23], our findings further support their relevance in the immune cell migration/activation pathway. We notice that P2RY13’s significance as a disease-onset transcriptional biomarker has been independently validated in blood and brain samples [25], enhancing the emerging connection between purinergic signaling (mediated by P2RY13 receptor) [171] and SCA3 pathogenesis. Further support comes from studies highlighting DDIT4 and TRIM13 as high-precision discriminators of CAG-dependent expression patterns [25], aligning with our observed downregulation of C3, ALS2, and SLC35A2 and reinforcing AKT1’s central role in SCA3 progression-related PPI network. These findings highlight convergent dysregulation in stress response and ubiquitin-proteasome pathways. Another research by Mafalda Raposo [24] has further elucidated relevant pathways interacting with FA and immune responses. This proposes SAFB2, SFSWAP, and LTBP4 as potential pre-ataxic biomarkers — findings that demonstrate strong conceptual alignment with our results showing ECM receptor/FA-mediated ECM remodeling processes. These studies validate blood-based transcriptional profiling as a meaningful proxy for CNS pathology in SCA3. Our current data integrates these prior observations by identifying potential biomarkers (THBS1, CAMTA1) and drug targets (AKT1, KLK6), while providing pathway-level insights into SCA3 progression mechanisms. This growing body of evidence solidifies blood transcriptomics as a robust platform for both patient stratification and therapeutic development in SCA3 research.

In summary, this study systematically investigated SCA3 DEGs, progression-related biomarkers/hub nodes, and therapeutic targets using a genetically homogeneous pedigree, minimizing variation through analysis of direct blood relatives while controlling for age-related confounders [172]. Our multi-species transcriptomic approach revealed stage-specific molecular changes, though several limitations warrant consideration: single-family analysis may not capture population heterogeneity; imperfect age/gender matching in patients and controls; small sample sizes potentially limiting generalizability; and absence of independent clinical and experimental validation. Future directions should include expanded cohorts, diagnostic efficacy studies, and validation in diverse populations/biological models to translate these findings into clinically applicable biomarkers. These findings nevertheless establish a robust foundation for SCA3 stratification and therapeutic development, with particular value for understanding disease progression in genetically defined subgroups.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

CNS

Central Nervous System

CNV

Copy Number Variant

CSVD

Cerebral Small Vessel Disease

DEG

Differentially Expressed Gene

DWI

Diffusion-weighted Imaging

ENG

Electronystagmography

FLAIR

Fluid-attenuated Inversion Recovery

HA

Hereditary Ataxia

LR-WGS

Long-read Whole-genome Sequencing

MJD

Machado-Joseph Disease

MRI

Magnetic Resonance Imaging

NGS

Next-generation Sequencing

polyQ

Polyglutamine

PPI

Protein-protein Interaction

RNA-seq

RNA Sequencing

SAS

Self-rating Anxiety Scale

SCA

Spinocerebellar Ataxia

SCL-90-R

Symptom Checklist-90-Revised

SDS

Self-rating Depression Scale

SV

Structural Variant

SWI

Susceptibility-weighted Imaging

TS

Targeted Sequencing

WES

Whole-exome Sequencing

Author contributions

Conceptualization, LS, YQ-R, and LC; Methodology, LS, YQ-R, CL, XW, and CX; Software, XW, CX, LC, and YQ-R; Validation, CX, YQ-R, XW, and CL; Formal Analysis, XW, CX, and YQ-R; Investigation, XW, LY-K, YL, HZ, CL, XX-L, and JQ-T; Resources, CL, XW, XX-L, SW-T, and ZT-Z; Data Curation, XW, LY-K, YL, HZ, CL, and XX-L; Writing — original draft preparation, LS, CL, CX, XW, and YQ-R; Writing — review and editing, LS, XW, LC, CL, and YQ-R; Visualization, XW, YQ-R, and YL; Supervision, LS, LC, and CL; Project Administration, XW, CL, YQ-R, and CX; Funding Acquisition, LS and LC. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key R&D Program of China (2020YFA0804000) and the National Natural Science Foundation of China (62222104, 62172130).

Data availability

The mouse tissue RNA-seq data in this study are publicly available in the NCBI GEO database (https://www.ncbi.nlm.nih.gov/geo/) under accession numbers GSE145613, GSE107958, and GSE108069. Human WES, LR-WGS, and RNA-seq data have been deposited in the Genome Sequence Archive for Human (GSA-Human, https://ngdc.cncb.ac.cn/gsa-human/) with accession numbers HRA010775 (https://ngdc.cncb.ac.cn/search/specific?db=hra&q=HRA010775), HRA010777 (https://ngdc.cncb.ac.cn/search/specific?db=hra&q=HRA010777), and HRA010785 (https://ngdc.cncb.ac.cn/search/specific?db=hra&q=HRA010785), respectively. Due to Chinese data protection regulations, these datasets are also available upon request from the corresponding author.

Declarations

Ethics approval

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the First Traditional Chinese Medicine Hospital in Changde City (Approval Number: 2020–020528) and Harbin Medical University (Approval Number: HMUIRB2022006).

Informed consent

Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patient(s) to publish this paper.

Competing interests

The authors declare no competing interests.