Transcriptional changes in the peripheral blood of at-risk individuals without clinical manifestation of Parkinson’s Disease

Transcriptional changes in the peripheral blood of at-risk individuals without clinical manifestation of Parkinson’s Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Transcriptional changes in the peripheral blood of at-risk individuals without clinical manifestation of Parkinson’s Disease Benjamin Whittle, Chun Chen, Ossagie Izuogu, Michael Jackson, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9178344/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract We have previously reported a global reduction of circular RNA (circRNA) levels in the peripheral blood of patients with early-stage idiopathic PD (iPD). This reduction was accompanied by increased expression of genes involved in innate immune response to double-stranded RNA. (dsRNA-IIR). Here, we extend these findings using whole-blood RNA sequencing data from 916 participants in the Parkinson’s Progression Markers Initiative. These consisted of individuals with idiopathic PD, pathogenic mutations in LRRK2 and GBA1, with and without disease manifestation, prodromal individuals with REM sleep behaviour disorder or hyposmia, two clinical features considered to indicate increased PD risk and healthy controls. We demonstrate that reduced circRNA abundance is not restricted to iPD but is also present in LRRK2 and GBA1-associated PD and in mutation carriers without manifest disease. CircRNA reduction was accompanied by increased expression of class I and class II transposable elements (TEs) and upregulation of dsRNA sensing and interferon-responsive genes, (ADAR1, DDX58/RIG-I, EIF2AK2/PKR and RNASEL. Inferred transcription factor activity was consistent with activation of antiviral and stress-signalling pathways. Among the prodromal groups, only individuals with hyposmia showed circRNA reduction, not those with REM sleep behaviour disorder, although, both groups exhibited elevated TE expression and increased expression of the genes mentioned above. Our results indicate that changes in circRNA levels are a general feature of PD and that their onset occurs early during disease development. They are consistent with dsRNA-IIR involvement in the in the development of PD and point to potential approaches for intervention. Health sciences/Diseases Biological sciences/Genetics Biological sciences/Molecular biology Health sciences/Neurology Biological sciences/Neuroscience Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterised by both motor and non-motor symptoms 1 . There is currently no cure, and available treatments provide only symptomatic relief. PD risk is multifactorial, involving genetic, environmental, and lifestyle contributors. Genome-wide association studies have identified over 200 genes associated with PD risk 1 . Variants in glucocerebrosidase beta (GBA1) and leucine-rich repeat kinase 2 (LRRK2) represent the most common genetic risk factors for PD 2 , with penetrance estimates of 15–80% 3 and 10–30% 4 , respectively, although values vary between studies 3 , 5 , 6 . LRRK2-associated PD typically progresses more slowly, with milder motor symptoms and relatively preserved cognition 3 . In contrast, GBA1 variants are associated with enhanced α-synuclein aggregation 7 , resulting in a more severe phenotype and increased risk of dementia and other non-motor symptoms 8 , 9 . REM sleep behaviour disorder (RBD) and hyposmia are common in PD, affecting approximately 30–50% 10 and 69–90% of patients, respectively. Both can precede motor symptoms by years 11 , 12 . These features are now recognised as predictors of future disease, with more than 70% of individuals with isolated RBD developing PD within 10–15 years 11 , and hyposmic individuals showing an elevated PD risk of 10–30% 12 . Neuropathologically, PD is characterised by the loss of dopaminergic neurons in the substantia nigra pars compacta. However, it is increasingly evident that PD is a multisystem disorder involving peripheral autonomic, enteric, and sensory nervous systems, with such changes often preceding clinical diagnosis 13 . Peripheral alterations in blood cell composition have also been reported, including increased neutrophils and reduced lymphocytes, resulting in elevated neutrophil-to-lymphocyte ratios consistent with systemic inflammation 14 . Innate and adaptive immune mechanisms are now understood to contribute significantly to PD pathogenesis 15 . Misfolded α-synuclein can activate both innate and adaptive immune responses, promoting neuroinflammation and neuronal injury 16 . In parallel, infiltration of peripheral immune cells into the CNS exacerbates inflammation through complement activation and cytokine release 17 . Microglial activation is a hallmark of PD 18 , characterised by upregulation of pro-inflammatory mediators. In the periphery, a meta-analysis of 20 studies (including n = 3584 PD and n = 2487 controls) revealed that elevated neutrophil-to-lymphocyte ratios are associated with PD 19,20 , and altered T-cell and monocyte function suggests immune involvement 21 . These peripheral immune signatures parallel inflammatory changes observed in PD brains 14 , 22 . However, the initiating triggers, temporal sequence, and relative contributions of these immune components to PD remain unresolved. Our recent work 23 demonstrated that peripheral blood cells from early-stage idiopathic PD (iPD) patients exhibit reduced levels of circular RNAs (circRNAs), accompanied by increased expression of genes inducible by double-stranded RNA (dsRNA) and linked to innate immune response (IIR) 24 . A reduction global circRNAs have been associated with activation of innate immunity through multiple mechanisms 25 . Associations have also been observed between circRNA biogenesis and transposable elements (TEs), as reverse-complementary Alu and other TE-derived intronic sequences can promote back-splicing and affect circRNA abundance 26 . TEs expression is elevated during inflammation 27 , 28 . TE expression activates innate immunity, through RNA intermediates which are recognized by dsRNA sensors 28 . These findings indicate that TEs and circRNAs are associated with the IIR, but the relevance of these associations in the context of PD risk and progression requires further investigation. Immune-related molecular signatures, originally observed in iPD, are also present in genetic forms of PD, which differ in onset and severity 3 , 7 , and in non-manifesting individuals at elevated risk due to LRRK2 or GBA1 variants or prodromal features such as RBD or hyposmia (as defined by PPMI 29 , 30 ). Effects on immune response have been shown in both LRRK2- and GBA1-associated PD; however, evidence for immune dysregulation in patients and carriers remains limited and sometimes contradictory 8 , 31 , 32 . Reduced expression of pro-inflammatory cytokines has been reported for LRRK2 mutations in preclinical models 33 – 36 , whereas GBA1 variants have been associated with increased cytokine expression 37 , complement activation 38 – 40 and microglial activation 41 , 42 ; potentially due to lysosomal dysfunction and inflammasome activation⁷. RBD and hyposmia are likewise linked to innate immune alterations: cytokines influence REM sleep, and sleep disruption modulates microglial activity and cytokine release 43 ; hyposmia in PD has been associated with microglia-driven neuroinflammatory changes related to α-synuclein accumulation in the olfactory bulb 44 . Here, we expand our observations in iPD 23 by examining circRNA abundance, TE transcript levels, and expression of genes involved in dsRNA sensing and IIR in both PD patients and non-manifesting carriers of genetic and non-genetic risk factors (Fig. 1 ). Our analyses reveal that reduced circRNA levels, together with increased TE expression and activation of dsRNA-responsive pathways, are not unique to iPD. Instead, these molecular features are also present in PD patients with genetic risk factors and in non-manifesting individuals at increased risk for PD. (a) Schematic describing the generation of RNA expression data in PPMI blood samples. (b) Breakdown of PPMI top-level study cohorts into analysis subgroups. Material and Methods Study cohort We used baseline blood-based RNA sequencing (RNAseq) data 29 made available from the Parkinson’s Progression Marker Initiative (PPMI, www.ppmi-info.org ) 30 , which includes an established cohort of participants including early stage, untreated PD (both idiopathic and individuals carrying pathogenic variants), prodromal individuals and healthy controls ( STable 1 ). Subject recruitment and eligibility criteria for the PPMI study have been previously published 30 . From PPMI, we selected RNA-seq data from individuals with PD (n = 490) and stratified this cohort into: i) idiopathic PD (iPD; n = 324), and ii) genetic PD (genPD; n = 161), comprising individuals carrying pathogenic variants in LRRK2 (LRRK2-PD; n = 125) or GBA1 (GBA-PD; n = 36). PD patients carrying pathogenic variants in both LRRK2 and GBA1 were excluded (n = 5). All participants in the PD cohort were untreated at baseline, had a clinical diagnosis of PD, and had a positive dopamine transporter (DAT) SPECT scan at enrolment ( www.ppmi-info.org ). In addition, we included RNA-seq data from prodromal individuals (proPD; n = 264), defined by PPMI as otherwise healthy participants aged > 60 years, with no clinical diagnosis of PD, parkinsonism, or dementia, but who exhibited risk factors or clinical features associated with PD (i.e., REM sleep behaviour disorder (RBD) and/or hyposmia and DAT deficit and/or a genetic risk variant in SNCA , LRRK2 or GBA ) 30 . For contrast with the PD cohorts, we specifically selected prodromal individuals with either a known genetic risk factor (LRRK2 carriers, n = 136; GBA1 carriers, n = 76), REM sleep behaviour disorder (RBD; n = 24), or hyposmia defined by UPSIT testing (n = 18) 30 ( STable 2 ). For comparisons, we included RNA-seq data from healthy controls (HC; n = 162), defined by PPMI as individuals with no current or previous clinically significant neurological disorder, no first-degree relative with PD, and normal DaT-SPECT imaging by visual inspection ( www.ppmi-info.org ). An overview of the study design is shown in Fig. 1 and SFigure 1 , and clinical and demographic summaries for each top-level study group are provided in STable 2 . In total, 916 samples were available for analysis (Fig. 1 b). RNA Collection, Sequencing, and Quantification Whole-blood RNA collection, isolation, and total RNA sequencing have been previously described 29 and are detailed in the PPMI Biologicals Manual ( www.ppmi-info.org ). Sequencing quality control and quantification of transcripts was performed as described previously 23 . CircRNAs (as back-splice junctions, BSJs) and forward splice junctions (FSJs) were quantified using CIRIquant v1.1.2 45 and HISAT v2.20 46 , on adapter and quality (PHRED 5 reads in > 2 samples 48 to leave a final dataset of 18,751 BSJs. Raw BSJ counts were normalised based on gene expression size factors using the median of ratios methods implemented in DESeq2 v1.48.1 49 , before undergoing a variance-stabilising transformation 50 . Transposable Element Quantification Transposable element (TE) transcript data were generated following alignment to the GRCh38 reference genome using STAR v2.7.11b 51 . Multimapped reads were permitted (--winAnchorMultimapNmax 200; --outFilterMultimapNmax 100) to enable accurate TE subfamily quantification. Technical alignment metrics were obtained using RNA-SeQC v2.4.2 52 . TE subfamily expression was quantified using featureCounts v2.0.6. Genic loci were quantified using the GENCODE v46 primary assembly annotation, while TE subfamilies were annotated using files provided by the TEtranscript developers 53 . We focused on class I elements (LTR, LINE, SINE, and SVA) and class II elements (DNA transposons and rolling-circle transposons). In total, 1,076 TE subfamilies were quantified across PPMI groups. Prior to analyses of TE expression variation, TE subfamily counts were normalised using the trimmed mean of M values (TMM) implemented in edgeR v4.2 54 . TE subfamilies were normalised using the library sizes and TMM normalisation factors derived from gene expression quantification. Normalised TE counts were then transformed using voom (limma v3.62.2 55 ). Identifying sources of RNA quantification variation We assessed the impact of known sources of RNA expression variation across 20,558 genes, 4,545 BSJs detected in > 50% of samples, and all 1,076 TE subfamilies ( SFigure 2 ). We calculated principal components (PCs) for each RNA type using prcomp. We then used univariate linear regression to measure the association between the first five RNA expression PCs and potential technical variation. To highlight the largest contributors to RNA expression variation, we identified metrics that were associated with PC1 (Benjamini-Hochberg adjusted P 0.9), we retained the explaining the most PC1 variance. We then iteratively constructed multivariate linear models to predict PC1 values, adding explanatory variables based on PC1 variance explained. Technical metrics maximizing the Bayesian Information Criterion (BIC) were selected. Any selected technical metrics, along with age, sex and sequencing batch, were also included in linear mixed models using variancePartition v1.36.3 56 , to quantify their contribution to individual RNA variance. Categorical variables were encoded as random effects in the linear mixed models. Gene set enrichment analysis Gene set enrichment analysis was performed using fgsea v1.32.2 57 . Gene sets were obtained from Reactome via msigdbr v10.0.1. Genes included in each differential expression analysis were ranked by moderated t-statistics. Enrichment P values were calculated from 10,000 permutations and adjusted for multiple testing using the Benjamini–Hochberg procedure. Transcription factor activity inference Transcription factors and their targets were collected from CollecTRI using OmniPathR v 3.18.2 58,59 . The Univariate Linear Model method implemented in decoupleR v2.16.0 60 was used to infer transcription factor activity based on the moderated t-statistics of target genes calculated by comparing gene expression in each subgroup to controls. Statistical Analyses RNA expression was normalised using the trimmed mean of M-values method 61 in edgeR v4.2.1 54 . BSJ, FSJ and TE subfamily expression were normalised using library size and normalisation factor estimates generated by gene expression data. Low expressed genes and TE subfamilies were removed using filterByExpr with default parameters. BSJ and their corresponding FSJ loci with counts per million < 0.1 in < 50% of samples were removed. RNA expression was voom transformed and linear models fit to each RNA using limma v3.62.2 62 . Differential expression analyses of transcripts, FSJs, BSJs, and TEs by contrasting each study group against healthy controls. All differential expression models were adjusted for age (categorised as 65 years), sex, and sequencing batch). As previously described 23 , we further adjusted for RNA-specific technical covariates: transcripts by percent usable bases, BSJs/FSJs by percent intronic bases, and TEs by proportion of intronic reads and median coverage standard deviation 63 , 64 . Global shifts in transcript, FSJ, BSJ, and TE expression were assessed using the distribution of moderated t-statistics from each differential expression analysis 65 . For each comparison versus healthy controls, we assessed whether the observed moderated t-statistic distribution for each RNA type differed from a null distribution centred on zero, generated via 10,000 bootstrap samples. Differences in BSJ and FSJ moderated t-statistic distributions were assessed by using a permutation test (10,000 permutations). Bootstrap and permutation analyses were performed using infer v1.0.7. All analyses were conducted in R v4.5.1. Where applicable, multiple testing correction was applied using Bonferroni or Benjamini–Hochberg methods. Statistical significance was defined as an adjusted P value < 0.05. Results circRNAs are reduced in PD patients and prodromal individuals with hyposmia. We first compared back-splice junction (BSJ) read counts, used as a proxy for circRNA levels, with forward-splice junction (FSJ) counts, representing the corresponding linear RNA levels, across all study groups using healthy controls as the reference (Fig. 2 and STable 3 ). Both manifest PD and prodromal individuals showed a significant reduction in BSJ counts compared with controls (Fig. 2 a). When stratifying PD into idiopathic (iPD), LRRK2-PD, and GBA1-PD subgroups, we observed a consistent reduction in circRNA levels across all three groups (Fig. 2 b). Similarly, circRNA reductions were observed across prodromal groups with genetic and non-genetic risk factors (Fig. 2 c). Notably, non-manifesting LRRK2 and GBA1 variant carriers exhibited BSJ reductions comparable to those seen in manifest PD (Fig. 2 a). Interestingly, while individuals with hyposmia demonstrated a significant reduction in circRNA abundance, individuals with RBD showed a significant increase in BSJ counts relative to controls (Fig. 2 c). Importantly, all BSJ reductions were independent of FSJ changes, indicating that the change in BSJ abundance is not a reflection of globally reduced transcription (Fig. 2 , SFigure 3 and STable 4 ). Mean moderated t-statistics (+/- 95% confidence interval) from the comparison of circRNA (BSJ) and linear RNA (FSJ) expression to Controls (n = 162) across (a) top-line PPMI cohorts (PD n = 490, Prodromal n = 264), (b) PD subgroups (iPD n = 324, LRRK2 PD n = 125, GBA PD n = 36), (c) Prodromal subgroups ( LRRK2 control n = 136, GBA Control n = 76, Hyposmia n = 18, RBD n = 24). Significance assessed using a simulated null distribution centred on zero. P -values were Bonferroni corrected based on two tests for each comparison. Source data is available in STables 3, 4 and 5 . PD patients and prodromal individuals show elevated TE expression. Increases of selected transposable elements (TE) expression has been reported in PD 66 , and circRNA abundance has been linked to TE activity 67 . We therefore investigated whether differential TE expression occurs in the PD and prodromal groups (Fig. 3 and STables 6 and 7 ). PD and prodromal individuals showed opposing patterns of BSJ abundance and TE expression. Expression of both class I (LTR, LINE, SINE, SVA) and class II (DNA transposons, rolling-circle transposons) elements was significantly increased in iPD and in prodromal individuals compared with controls when analysed as aggregated groups (Fig. 3 a). Elevated TE expression was also observed when stratifying individuals by genetic (e.g., LRRK2, GBA1) and non-genetic risk factors, in both manifest PD (Fig. 3 b) and prodromal groups (Fig. 3 c). Notably, TE expression was increased in individuals with RBD, despite this subgroup not showing reduced circRNA levels (Fig. 2 c). Mean moderated t-statistics (+/- 95% confidence interval) from the comparison of Class I and II transposable element subfamilies to Controls (n = 162) across (a) top-line PPMI cohorts (PD n = 490, Prodromal n = 264), (b) PD subgroups (iPD n = 324, LRRK2 PD n = 125, GBA PD n = 36), (c) Prodromal subgroups ( LRRK2 control n = 136, GBA Control n = 76, Hyposmia n = 18, RBD n = 24). Significance assessed using a simulated null distribution centred on zero. P -values were Bonferroni corrected based on two tests for each comparison. T-statistic distributions are shown in SFigure 4 and source data is available in STable 6 . The dsRNA-IIR is activated in LRRK2 and GBA1 PD, and prodromal individuals In our previous study 23 , reduced circRNA levels in iPD were accompanied by increased expression of genes involved in circRNA biogenesis as well as genes responding to double-stranded RNA (dsRNA) and activating innate immune pathways. We therefore investigated whether similar transcriptional signatures were present in genetic forms of PD and in prodromal individuals. Patients with LRRK2- and GBA1-associated PD displayed circRNA biogenesis and dsRNA-IIR expression patterns similar to those observed in iPD 23 (Fig. 4 and STable 8 ), although these do not always reach statistical significance. Both groups showed increased expression of the RNA-editing enzyme ADAR 68 , the dsRNA sensor RIG-I (encoded by DDX58 ) 69 , the antiviral kinase EIF2AK2 (also known as PKR) 68 , the inflammasome component NLRP1 69 , and the interferon-induced endoribonuclease RNASEL 70 (Fig. 4 a,b). This expression pattern is consistent with the observed reduction in ILF3 71 , a negative regulator of the innate immune response that also participates in circRNA biogenesis⁵⁶ (Fig. 4 a). Similar to ADAR1 and RNASEL, decreased ILF3 expression is consistent with reduced circRNA production 72 . Interestingly, expression of the dsRNA sensor MDA5 (encoded by IFIH1 ) 69 and its regulatory partner LGP2 (encoded by DHX58 ) 73 were both increased in iPD and in GBA1-PD, but reduced in LRRK2-PD (Fig. 4 b), suggesting mutation-specific differences in innate immune regulation. Comparable expression profiles were observed across both genetic and non-genetic prodromal groups (Fig. 4 b), including in individuals with RBD, who notably did not exhibit reduced circRNA levels (Fig. 2 c). Because the downstream effects of the dsRNA response are mediated through transcription factor (TF) activation, we next assessed TF expression to determine whether the observed dsRNA-IIR signature translated into functional regulatory changes. We inferred TF activity from transcript-level changes in their target gene regulons ( STable 9 ), focusing on 12 TFs known to mediate the dsRNA-IIR (Fig. 4 c). Analysis revealed increased expression of AP-1 and its component JUN in iPD, LRRK2-PD, and GBA-PD patients (Fig. 4 c). NF-κB, STAT1, and STAT2 regulons were increased in iPD and GBA-PD patients (Fig. 4 c), consistent with activation of canonical antiviral signalling pathways 74 . As RNase L–mediated circRNA degradation(REF) can activate the integrated stress response (ISR) via PKR-dependent phosphorylation of eIF2α 75 , we also examined key ISR-associated TFs. Regulons of ATF4 and DDIT3 (CHOP), central effectors of the ISR, were increased in iPD patients, with elevated DDIT3 activity also observed in GBA-PD patients (Fig. 4 c). Intriguingly, pathway analysis excluding terms directly linked to dsRNA sensing or circRNA modulation revealed broad reductions in pathways associated with RNA metabolism and global RNA processing including translation, RNA turnover, and mRNA splicing (e.g., EIF2AK4 response, translation initiation/elongation), across PD and prodromal groups (Fig. 4 d and STable 10 ). In contrast, pathways related to olfactory signalling and sensory perception were elevated in iPD, LRRK2-PD, and in all prodromal groups (Fig. 4 d). Pathways associated with infectious disease responses were increased in iPD and GBA1-PD, but not in prodromal individuals. (a) Expression changes of genes involved in modulating the levels of multiple circRNAs. (b) Expression changes of genes involved in the sensing of double-stranded RNA (dsRNA). (c) Inferred activity of transcription factors (TFs) downstream of the dsRNA response. (d) Reactome pathway gene set enrichment analysis. Only terms with FDR-adjusted P < 0.05 in at least six PPMI subgroup comparisons are shown. Where present, * indicates FDR-adjusted P < 0.05. Source data is available in STables 8, 9 and 10. Discussion Our previous work 23 demonstrated that early-stage idiopathic Parkinson’s disease (iPD) patients show a global reduction of circRNAs in blood compared with matched controls. Here, we extend these findings across genetic and prodromal forms of PD and demonstrate that reduced circRNA abundance and the transcriptional changes in peripheral blood cell populations is detectable not only in manifest idiopathic and genetic PD, but also in individuals at increased risk of disease. As observed in iPD patients, these groups also display increased expression of genes associated with dsRNA-induced innate immune response (dsRNA-IIR), along with elevated levels of class I and class II transposable elements (TEs). Together, these results identify a conserved peripheral RNA signature across idiopathic, genetic, and at-risk groups. The global reduction of blood circRNAs across PD idiopathic and genetic sub-groups suggests a shared disturbance in RNA homeostasis, irrespective of the underlying genetic or idiopathic drivers. Notably, the magnitude and pattern of circRNA reduction are highly similar between iPD, LRRK2 -, and GBA1 -PD, suggesting that this change is largely independent of clinical heterogeneity and synuclein pathology 7 .Importantly, circRNA reductions were also observed in non-manifesting LRRK2 and GBA1 carriers, indicating that altered circRNA abundance is detectable prior to clinical diagnosis. Given that around 50% of LRRK2 3 and around 30% of GBA1 76 carriers will develop PD by age 80, these findings raise the possibility that reduced circRNAs could serve as early indicators of disease risk. However, longitudinal studies will be required to determine whether circRNA abundance predicts phenoconversion. Our observations are supported by previous work using the Parkinson’s Disease Biomarkers Program dataset, which showed that LRRK2 and GBA1 mutations are associated with a reduction of specific circRNAs 77 . Interestingly, although the authors focus on specific circRNAs, their figures showed a general reduction in circRNA levels 77 . How GBA1 and LRRK2 variants influence circRNA abundance remains unclear. Both genes have been linked to immune dysregulation and type-I interferon signalling 35 , 78 , 79 . Our data are consistent with a model in which genetic risk variants amplify or modify activation of pattern-recognition receptors (e.g., RIG-I, MDA5, TLR3) and downstream interferon signalling pathways, potentially promoting RNase-mediated RNA decay mechanisms that reduce steady-state circRNA levels. However, our cross-sectional analyses cannot establish directionality or causality between dsRNA pathway activation and circRNA depletion. Additionally, LRRK2 and GBA1 variants may perturb RNA homeostasis through distinct mechanisms that influence on circRNA regulation. LRRK2-driven changes in translation and RNA-binding protein activity are predicted to shift the balance between canonical splicing and back-splicing, potentially altering circRNA production 80 , 81 . In parallel, GBA1-mediated lysosomal and ER stress may remodel transcriptional and RNA-processing networks 82 , which could likewise influence circRNA biogenesis and turnover. While our data suggest a shared effect on downstream RNA regulatory pathways, mutation-specific differences in dsRNA sensor expression indicate that upstream mechanisms may differ between subtypes 77 . Interestingly, individuals with hyposmia exhibited circRNA reductions similar to those observed in manifest PD, whereas those with RBD did not. This divergence may reflect biological heterogeneity between prodromal subtypes, differences in disease stage, or limited statistical power in smaller subgroups such as RBD (n = 24). Although these findings could be interpreted within proposed ‘brain-first’ and ‘body-first’ models of PD 83 , our data do not directly distinguish between these frameworks and should be interpreted cautiously. Inflammatory processes can involve increased TE expression 84 , and TE-derived RNA intermediates can activate dsRNA sensing pathways. Consistent with this, reduced circRNA abundance across PD and prodromal subgroups was accompanied by elevated TE expression and increased expression of dsRNA-IIR genes, including ADAR1, DDX58 (RIG-I), EIF2AK2 (PKR), and RNASEL 68 , 73 , 85 . These changes support a model of enhanced antiviral-like signalling in peripheral blood. However, we did not directly measure dsRNA species, RNA editing levels, or RNase activity, and therefore cannot determine whether TE expression is a driver or consequence of innate immune activation. The presence of increased TE expression in RBD individuals without circRNA reduction further suggests that TE dysregulation and circRNA depletion may occur at different stages or represent partially independent processes and suggests that TE expression could be used as a biomarker of PD onset or progression 66 . Variable expression of OAS2, IFIH1 (MDA5), and DHX58 (LGP2), particularly the relative attenuation in LRRK2-PD compared with iPD and GBA1-PD, suggests subtype-specific modulation of dsRNA sensing pathways. These differences are consistent with prior reports that LRRK2-associated PD exhibits a distinct inflammatory signature 35 , 79 . Our findings suggest that while RIG-I signalling appears activated across PD groups to some degree, MDA5 expression differs between genotypes. Whether these differences reflect intrinsic mutation-specific immune modulation or secondary effects of disease stage warrants further investigation. Pathway analysis indicates reductions in RNA metabolism, translation, and RNA processing pathways across PD and prodromal groups. This widespread downregulation of RNA processing pathways, alongside upregulation of dsRNA-responsive transcription factors (AP-1, NF-κB, STAT1/2, ATF4, DDIT3), suggests coordinated remodelling of RNA surveillance and stress-response networks in peripheral blood 86 – 88 . Upregulation of genes involved in olfactory signalling pathways in both PD and prodromal groups was notable and may reflect systemic molecular correlates of early sensory dysfunction 89 , although the functional relevance of peripheral olfactory pathway enrichment remains uncertain. Compared with the number of controls and PD patients analysed in this study the prodromal cohorts were small, reducing the power to detect statistically significant expression changes. However, the changes we were able to detect were congruent with the observations in patients with manifest disease. Thus it is unlikely that they represent false positives a phenomenon that can affect underpowered studies 90 . In summary, we identify a conserved peripheral molecular signature characterised by reduced circRNA abundance, increased TE expression, and activation of dsRNA-induced innate immune pathways across idiopathic, genetic, and prodromal PD. These findings support a model in which dysregulated RNA homeostasis and antiviral-like signalling are detectable in peripheral blood prior to clinical diagnosis. While mechanistic relationships remain to be established, this RNA-based signature provides a framework for future longitudinal and functional studies aimed at clarifying its role in PD pathogenesis and its potential utility in risk stratification. Declarations Consent for publication Not applicable Data availability PPMI raw RNA sequencing and corresponding clinical data are available from https://www.ppmi-info.org/. Summary data used to generate summary statistics and figures are included as supplementary datasets. Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Code availability The code used in this analysis is available at https://github.com/bj-w/PD-lin-circ-RNA-paper Competing Interests The authors declare no competing interests. Author Contributions MJ, MSK and GH designed the study. BW, OI and CC performed data generation and analysis. BW, MSK and GH conducted statistical analysis. BW, MSK and GH wrote the manuscript with feedback and input from all authors. All authors read and approved the final manuscript. Acknowledgements This work was funded by the Michael J. Fox Foundation (Grant ID MJFF-007574). GH receives funding from Wellcome (203105/Z/16/Z), the Michael J. Fox Foundation (MJFF-007574), Parkinson’s UK (G-2003 and G-2201) and is supported through the LifeArc Centre for Rare Mitochondrial Disease (REF:10748) and the National Institute for Health Research (NIHR) Newcastle Biomedical Research Unit and Centre (BRC) based at Newcastle upon Tyne Hospitals NHS Foundation Trust and Newcastle University. The authors would like to express their deepest gratitude to the Parkinson’s Progression Markers Initiative team members as well as to the patients and staff at each study. References Buniello, A., et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res 47, D1005-D1012 (2019). Schindlbeck, K.A., et al. LRRK2 and GBA Variants Exert Distinct Influences on Parkinson's Disease-Specific Metabolic Networks. Cereb Cortex 30, 2867–2878 (2020). Kmiecik, M.J., et al. Genetic analysis and natural history of Parkinson's disease due to the LRRK2 G2019S variant. 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Multi-species analysis of inflammatory response elements reveals ancient and lineage-specific contributions of transposable elements to NF-kB binding. Genome Res 35, 1544–1559 (2025). Cottrell, K.A., Ryu, S., Torres, L.S., Schab, A.M. & Weber, J.D. Induction of viral mimicry upon loss of DHX9 and ADAR1 in breast cancer cells. bioRxiv (2023). Karasik, A., Lorenzi, H.A., DePass, A.V. & Guydosh, N.R. Endonucleolytic RNA cleavage drives changes in gene expression during the innate immune response. bioRxiv (2023). Pakos-Zebrucka, K., Koryga, I., Mnich, K., Ljujic, M., Samali, A. & Gorman, A.M. The integrated stress response. EMBO Rep 17, 1374–1395 (2016). Jensen, S. & Thomsen, A.R. Sensing of RNA viruses: a review of innate immune receptors involved in recognizing RNA virus invasion. J Virol 86, 2900–2910 (2012). Ielo, A., et al. Structural and functional connectomics of the olfactory system in Parkinson's disease: a systematic review. Parkinsonism Relat Disord 132, 107230 (2025). Carnahan, R.M. & Brown, G.D. The power and pitfalls of underpowered studies. Pharmacotherapy 44, 698–700 (2024). Additional Declarations No competing interests reported. Supplementary Files WhittleetalSFiguresSubmissionNPJParkv2.docx WhittleetalSTablesSubmissionNPJParkv2.xlsx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 10 Apr, 2026 Reviews received at journal 09 Apr, 2026 Reviews received at journal 06 Apr, 2026 Reviews received at journal 04 Apr, 2026 Reviewers agreed at journal 01 Apr, 2026 Reviewers agreed at journal 01 Apr, 2026 Reviewers agreed at journal 01 Apr, 2026 Reviewers agreed at journal 01 Apr, 2026 Reviewers agreed at journal 01 Apr, 2026 Reviewers invited by journal 01 Apr, 2026 Editor assigned by journal 24 Mar, 2026 Submission checks completed at journal 23 Mar, 2026 First submitted to journal 20 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9178344","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":617651140,"identity":"a0f45db1-b67a-445d-b823-2a6d74d96432","order_by":0,"name":"Benjamin Whittle","email":"","orcid":"","institution":"Newcastle University","correspondingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"","lastName":"Whittle","suffix":""},{"id":617651141,"identity":"f1ac815e-5727-4263-940b-1fa2ae21062f","order_by":1,"name":"Chun Chen","email":"","orcid":"","institution":"Newcastle University","correspondingAuthor":false,"prefix":"","firstName":"Chun","middleName":"","lastName":"Chen","suffix":""},{"id":617651143,"identity":"1038c690-5490-4a2a-9663-c8da4d9db920","order_by":2,"name":"Ossagie Izuogu","email":"","orcid":"","institution":"European Bioinformatics Institute","correspondingAuthor":false,"prefix":"","firstName":"Ossagie","middleName":"","lastName":"Izuogu","suffix":""},{"id":617651145,"identity":"75471331-ee05-4fec-b784-7b1da5babb0f","order_by":3,"name":"Michael Jackson","email":"","orcid":"","institution":"Newcastle University","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Jackson","suffix":""},{"id":617651147,"identity":"a8ece003-d54f-4b42-a9b2-8bfd33307906","order_by":4,"name":"Mauro Santibanez-Koref","email":"","orcid":"","institution":"Newcastle University","correspondingAuthor":false,"prefix":"","firstName":"Mauro","middleName":"","lastName":"Santibanez-Koref","suffix":""},{"id":617651150,"identity":"803eea13-e50f-46ae-8295-3f4712a18807","order_by":5,"name":"Gavin Hudson","email":"data:image/png;base64,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","orcid":"","institution":"Newcastle University","correspondingAuthor":true,"prefix":"","firstName":"Gavin","middleName":"","lastName":"Hudson","suffix":""}],"badges":[],"createdAt":"2026-03-20 11:23:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9178344/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9178344/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106405085,"identity":"697400f2-ff52-463e-885e-05f3c74c12d8","added_by":"auto","created_at":"2026-04-08 09:21:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":950638,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy overview.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a) \u003c/strong\u003eSchematic describing the generation of RNA expression data in PPMI blood samples.\u003cstrong\u003e (b) \u003c/strong\u003eBreakdown of PPMI top-level study cohorts into analysis subgroups.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9178344/v1/160912be2dc450574ebd37e8.png"},{"id":106405080,"identity":"e5dda5a2-1234-422e-8642-727ad10e75a3","added_by":"auto","created_at":"2026-04-08 09:20:59","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":346816,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReduced blood circular RNA expression in PD patients and at-risk individuals.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMean moderated t-statistics (+/- 95% confidence interval) from the comparison of circRNA (BSJ) and linear RNA (FSJ) expression to Controls (n = 162) across \u003cstrong\u003e(a)\u003c/strong\u003e top-line PPMI cohorts (PD n = 490, Prodromal n = 264), \u003cstrong\u003e(b)\u003c/strong\u003e PD subgroups (iPD n = 324, \u003cem\u003eLRRK2\u003c/em\u003ePD n = 125, \u003cem\u003eGBA\u003c/em\u003ePD n = 36), \u003cstrong\u003e(c) \u003c/strong\u003eProdromal subgroups (\u003cem\u003eLRRK2\u003c/em\u003e control n = 136, \u003cem\u003eGBA\u003c/em\u003eControl n = 76, Hyposmia n = 18, RBD n = 24). Significance assessed using a simulated null distribution centred on zero. \u003cem\u003eP\u003c/em\u003e-values were Bonferroni corrected based on two tests for each comparison. Source data is available in \u003cstrong\u003eSTables 3, 4 \u003c/strong\u003eand \u003cstrong\u003e5\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9178344/v1/76ed207462aacd269ae5c09a.jpeg"},{"id":106405087,"identity":"dca8fca9-7066-4858-a967-fdc64c32ad91","added_by":"auto","created_at":"2026-04-08 09:21:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":57957,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIncreased blood transposable element expression in PD patients and at-risk individuals.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMean moderated t-statistics (+/- 95% confidence interval) from the comparison of Class I and II transposable element subfamilies to Controls (n = 162) across \u003cstrong\u003e(a)\u003c/strong\u003e top-line PPMI cohorts (PD n = 490, Prodromal n = 264), \u003cstrong\u003e(b)\u003c/strong\u003e PD subgroups (iPD n = 324, \u003cem\u003eLRRK2\u003c/em\u003ePD n = 125, \u003cem\u003eGBA\u003c/em\u003ePD n = 36), \u003cstrong\u003e(c) \u003c/strong\u003eProdromal subgroups (\u003cem\u003eLRRK2\u003c/em\u003e control n = 136, \u003cem\u003eGBA\u003c/em\u003eControl n = 76, Hyposmia n = 18, RBD n = 24). Significance assessed using a simulated null distribution centred on zero. \u003cem\u003eP\u003c/em\u003e-values were Bonferroni corrected based on two tests for each comparison. T-statistic distributions are shown in SFigure 4 and source data is available in \u003cstrong\u003eSTable 6\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9178344/v1/cef4dffe372c1b3b667bce32.png"},{"id":106405076,"identity":"e4375925-1b81-4419-9dcd-6f90c4a22299","added_by":"auto","created_at":"2026-04-08 09:20:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":207732,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptionally altered biological mechanisms across PD and prodromal subgroups.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003eExpression changes of genes involved in modulating the levels of multiple circRNAs. \u003cstrong\u003e(b)\u003c/strong\u003e Expression changes of genes involved in the sensing of double-stranded RNA (dsRNA). \u003cstrong\u003e(c)\u003c/strong\u003eInferred activity of transcription factors (TFs) downstream of the dsRNA response. \u003cstrong\u003e(d)\u003c/strong\u003e Reactome pathway gene set enrichment analysis. Only terms with FDR-adjusted P \u0026lt; 0.05 in at least six PPMI subgroup comparisons are shown. Where present, * indicates FDR-adjusted P \u0026lt; 0.05. Source data is available in \u003cstrong\u003eSTables 8, 9 \u003c/strong\u003eand\u003cstrong\u003e 10.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9178344/v1/f05feb1c0dd0cc52873b854a.png"},{"id":106408084,"identity":"92b7df48-a4aa-4657-bae7-fad03c6a5f3f","added_by":"auto","created_at":"2026-04-08 09:40:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2404781,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9178344/v1/7c862c86-3a84-49ba-b12d-af2f1ccb4157.pdf"},{"id":106405818,"identity":"434d4560-f57c-4a7c-aec2-ce745c5b220a","added_by":"auto","created_at":"2026-04-08 09:28:42","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":6960868,"visible":true,"origin":"","legend":"","description":"","filename":"WhittleetalSFiguresSubmissionNPJParkv2.docx","url":"https://assets-eu.researchsquare.com/files/rs-9178344/v1/a1e852dfa785c774a57fe5d4.docx"},{"id":106405089,"identity":"efecd3bd-6cc3-445d-bcb8-f448e8504e05","added_by":"auto","created_at":"2026-04-08 09:21:17","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19855479,"visible":true,"origin":"","legend":"","description":"","filename":"WhittleetalSTablesSubmissionNPJParkv2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9178344/v1/688d1dc97a58dd0abf08885c.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transcriptional changes in the peripheral blood of at-risk individuals without clinical manifestation of Parkinson’s Disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eParkinson\u0026rsquo;s disease (PD) is a progressive neurodegenerative disorder characterised by both motor and non-motor symptoms\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. There is currently no cure, and available treatments provide only symptomatic relief. PD risk is multifactorial, involving genetic, environmental, and lifestyle contributors.\u003c/p\u003e \u003cp\u003eGenome-wide association studies have identified over 200 genes associated with PD risk\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Variants in glucocerebrosidase beta (GBA1) and leucine-rich repeat kinase 2 (LRRK2) represent the most common genetic risk factors for PD\u003csup\u003e2\u003c/sup\u003e, with penetrance estimates of 15\u0026ndash;80%\u003csup\u003e3\u003c/sup\u003e and 10\u0026ndash;30%\u003csup\u003e4\u003c/sup\u003e, respectively, although values vary between studies\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. LRRK2-associated PD typically progresses more slowly, with milder motor symptoms and relatively preserved cognition\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. In contrast, GBA1 variants are associated with enhanced α-synuclein aggregation\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, resulting in a more severe phenotype and increased risk of dementia and other non-motor symptoms\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. REM sleep behaviour disorder (RBD) and hyposmia are common in PD, affecting approximately 30\u0026ndash;50%\u003csup\u003e10\u003c/sup\u003e and 69\u0026ndash;90% of patients, respectively. Both can precede motor symptoms by years\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. These features are now recognised as predictors of future disease, with more than 70% of individuals with isolated RBD developing PD within 10\u0026ndash;15 years\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, and hyposmic individuals showing an elevated PD risk of 10\u0026ndash;30%\u003csup\u003e12\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNeuropathologically, PD is characterised by the loss of dopaminergic neurons in the substantia nigra pars compacta. However, it is increasingly evident that PD is a multisystem disorder involving peripheral autonomic, enteric, and sensory nervous systems, with such changes often preceding clinical diagnosis\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Peripheral alterations in blood cell composition have also been reported, including increased neutrophils and reduced lymphocytes, resulting in elevated neutrophil-to-lymphocyte ratios consistent with systemic inflammation\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eInnate and adaptive immune mechanisms are now understood to contribute significantly to PD pathogenesis\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Misfolded α-synuclein can activate both innate and adaptive immune responses, promoting neuroinflammation and neuronal injury\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In parallel, infiltration of peripheral immune cells into the CNS exacerbates inflammation through complement activation and cytokine release\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Microglial activation is a hallmark of PD\u003csup\u003e18\u003c/sup\u003e, characterised by upregulation of pro-inflammatory mediators. In the periphery, a meta-analysis of 20 studies (including n\u0026thinsp;=\u0026thinsp;3584 PD and n\u0026thinsp;=\u0026thinsp;2487 controls) revealed that elevated neutrophil-to-lymphocyte ratios are associated with PD\u003csup\u003e19,20\u003c/sup\u003e, and altered T-cell and monocyte function suggests immune involvement\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. These peripheral immune signatures parallel inflammatory changes observed in PD brains\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. However, the initiating triggers, temporal sequence, and relative contributions of these immune components to PD remain unresolved.\u003c/p\u003e \u003cp\u003eOur recent work\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e demonstrated that peripheral blood cells from early-stage idiopathic PD (iPD) patients exhibit reduced levels of circular RNAs (circRNAs), accompanied by increased expression of genes inducible by double-stranded RNA (dsRNA) and linked to innate immune response (IIR)\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. A reduction global circRNAs have been associated with activation of innate immunity through multiple mechanisms\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Associations have also been observed between circRNA biogenesis and transposable elements (TEs), as reverse-complementary Alu and other TE-derived intronic sequences can promote back-splicing and affect circRNA abundance\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. TEs expression is elevated during inflammation\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. TE expression activates innate immunity, through RNA intermediates which are recognized by dsRNA sensors\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. These findings indicate that TEs and circRNAs are associated with the IIR, but the relevance of these associations in the context of PD risk and progression requires further investigation.\u003c/p\u003e \u003cp\u003eImmune-related molecular signatures, originally observed in iPD, are also present in genetic forms of PD, which differ in onset and severity\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, and in non-manifesting individuals at elevated risk due to LRRK2 or GBA1 variants or prodromal features such as RBD or hyposmia (as defined by PPMI\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e). Effects on immune response have been shown in both LRRK2- and GBA1-associated PD; however, evidence for immune dysregulation in patients and carriers remains limited and sometimes contradictory\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Reduced expression of pro-inflammatory cytokines has been reported for LRRK2 mutations in preclinical models\u003csup\u003e\u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, whereas GBA1 variants have been associated with increased cytokine expression\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, complement activation\u003csup\u003e\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e and microglial activation\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e; potentially due to lysosomal dysfunction and inflammasome activation⁷. RBD and hyposmia are likewise linked to innate immune alterations: cytokines influence REM sleep, and sleep disruption modulates microglial activity and cytokine release\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e; hyposmia in PD has been associated with microglia-driven neuroinflammatory changes related to α-synuclein accumulation in the olfactory bulb\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHere, we expand our observations in iPD\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e by examining circRNA abundance, TE transcript levels, and expression of genes involved in dsRNA sensing and IIR in both PD patients and non-manifesting carriers of genetic and non-genetic risk factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Our analyses reveal that reduced circRNA levels, together with increased TE expression and activation of dsRNA-responsive pathways, are not unique to iPD. Instead, these molecular features are also present in PD patients with genetic risk factors and in non-manifesting individuals at increased risk for PD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e(a)\u003c/b\u003e Schematic describing the generation of RNA expression data in PPMI blood samples. \u003cb\u003e(b)\u003c/b\u003e Breakdown of PPMI top-level study cohorts into analysis subgroups.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy cohort\u003c/h2\u003e\n \u003cp\u003eWe used baseline blood-based RNA sequencing (RNAseq) data\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e made available from the Parkinson\u0026rsquo;s Progression Marker Initiative (PPMI, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.ppmi-info.org\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, which includes an established cohort of participants including early stage, untreated PD (both idiopathic and individuals carrying pathogenic variants), prodromal individuals and healthy controls (\u003cstrong\u003eSTable 1\u003c/strong\u003e). Subject recruitment and eligibility criteria for the PPMI study have been previously published\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eFrom PPMI, we selected RNA-seq data from individuals with PD (n\u0026thinsp;=\u0026thinsp;490) and stratified this cohort into: i) idiopathic PD (iPD; n\u0026thinsp;=\u0026thinsp;324), and ii) genetic PD (genPD; n\u0026thinsp;=\u0026thinsp;161), comprising individuals carrying pathogenic variants in LRRK2 (LRRK2-PD; n\u0026thinsp;=\u0026thinsp;125) or GBA1 (GBA-PD; n\u0026thinsp;=\u0026thinsp;36). PD patients carrying pathogenic variants in both LRRK2 and GBA1 were excluded (n\u0026thinsp;=\u0026thinsp;5). All participants in the PD cohort were untreated at baseline, had a clinical diagnosis of PD, and had a positive dopamine transporter (DAT) SPECT scan at enrolment (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.ppmi-info.org\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn addition, we included RNA-seq data from prodromal individuals (proPD; n\u0026thinsp;=\u0026thinsp;264), defined by PPMI as otherwise healthy participants aged\u0026thinsp;\u0026gt;\u0026thinsp;60 years, with no clinical diagnosis of PD, parkinsonism, or dementia, but who exhibited risk factors or clinical features associated with PD (i.e., REM sleep behaviour disorder (RBD) and/or hyposmia and DAT deficit and/or a genetic risk variant in \u003cem\u003eSNCA\u003c/em\u003e, \u003cem\u003eLRRK2\u003c/em\u003e or \u003cem\u003eGBA\u003c/em\u003e)\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. For contrast with the PD cohorts, we specifically selected prodromal individuals with either a known genetic risk factor (LRRK2 carriers, n\u0026thinsp;=\u0026thinsp;136; GBA1 carriers, n\u0026thinsp;=\u0026thinsp;76), REM sleep behaviour disorder (RBD; n\u0026thinsp;=\u0026thinsp;24), or hyposmia defined by UPSIT testing (n\u0026thinsp;=\u0026thinsp;18)\u003csup\u003e30\u003c/sup\u003e (\u003cstrong\u003eSTable 2\u003c/strong\u003e). For comparisons, we included RNA-seq data from healthy controls (HC; n\u0026thinsp;=\u0026thinsp;162), defined by PPMI as individuals with no current or previous clinically significant neurological disorder, no first-degree relative with PD, and normal DaT-SPECT imaging by visual inspection (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.ppmi-info.org\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAn overview of the study design is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cstrong\u003eSFigure 1\u003c/strong\u003e, and clinical and demographic summaries for each top-level study group are provided in \u003cstrong\u003eSTable 2\u003c/strong\u003e. In total, 916 samples were available for analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eRNA Collection, Sequencing, and Quantification\u003c/h3\u003e\n\u003cp\u003eWhole-blood RNA collection, isolation, and total RNA sequencing have been previously described\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e and are detailed in the PPMI Biologicals Manual (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.ppmi-info.org\u003c/span\u003e\u003cspan address=\"http://www.ppmi-info.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Sequencing quality control and quantification of transcripts was performed as described previously\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. CircRNAs (as back-splice junctions, BSJs) and forward splice junctions (FSJs) were quantified using CIRIquant v1.1.2\u003csup\u003e45\u003c/sup\u003e and HISAT v2.20\u003csup\u003e46\u003c/sup\u003e, on adapter and quality (PHRED\u0026thinsp;\u0026lt;\u0026thinsp;15) trimmed reads produced by Trim Galore! v0.67 running cutadapt v4.2\u003csup\u003e47\u003c/sup\u003e. We initially detected 948,059 unique BSJs across 901 samples, filtering to BSJs with at \u0026gt;\u0026thinsp;5 reads in \u0026gt;\u0026thinsp;2 samples\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e to leave a final dataset of 18,751 BSJs. Raw BSJ counts were normalised based on gene expression size factors using the median of ratios methods implemented in DESeq2 v1.48.1\u003csup\u003e49\u003c/sup\u003e, before undergoing a variance-stabilising transformation\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eTransposable Element Quantification\u003c/h3\u003e\n\u003cp\u003eTransposable element (TE) transcript data were generated following alignment to the GRCh38 reference genome using STAR v2.7.11b\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Multimapped reads were permitted (--winAnchorMultimapNmax 200; --outFilterMultimapNmax 100) to enable accurate TE subfamily quantification. Technical alignment metrics were obtained using RNA-SeQC v2.4.2\u003csup\u003e52\u003c/sup\u003e. TE subfamily expression was quantified using featureCounts v2.0.6. Genic loci were quantified using the GENCODE v46 primary assembly annotation, while TE subfamilies were annotated using files provided by the TEtranscript developers\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. We focused on class I elements (LTR, LINE, SINE, and SVA) and class II elements (DNA transposons and rolling-circle transposons). In total, 1,076 TE subfamilies were quantified across PPMI groups. Prior to analyses of TE expression variation, TE subfamily counts were normalised using the trimmed mean of M values (TMM) implemented in edgeR v4.2\u003csup\u003e54\u003c/sup\u003e. TE subfamilies were normalised using the library sizes and TMM normalisation factors derived from gene expression quantification. Normalised TE counts were then transformed using voom (limma v3.62.2\u003csup\u003e55\u003c/sup\u003e).\u003c/p\u003e\n\u003ch3\u003eIdentifying sources of RNA quantification variation\u003c/h3\u003e\n\u003cp\u003eWe assessed the impact of known sources of RNA expression variation across 20,558 genes, 4,545 BSJs detected in \u0026gt;\u0026thinsp;50% of samples, and all 1,076 TE subfamilies (\u003cb\u003eSFigure 2\u003c/b\u003e). We calculated principal components (PCs) for each RNA type using prcomp. We then used univariate linear regression to measure the association between the first five RNA expression PCs and potential technical variation. To highlight the largest contributors to RNA expression variation, we identified metrics that were associated with PC1 (Benjamini-Hochberg adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). If metrics were correlated (absolute Pearson\u0026rsquo;s r\u0026thinsp;\u0026gt;\u0026thinsp;0.9), we retained the explaining the most PC1 variance. We then iteratively constructed multivariate linear models to predict PC1 values, adding explanatory variables based on PC1 variance explained. Technical metrics maximizing the Bayesian Information Criterion (BIC) were selected. Any selected technical metrics, along with age, sex and sequencing batch, were also included in linear mixed models using variancePartition v1.36.3\u003csup\u003e56\u003c/sup\u003e, to quantify their contribution to individual RNA variance. Categorical variables were encoded as random effects in the linear mixed models.\u003c/p\u003e\n\u003ch3\u003eGene set enrichment analysis\u003c/h3\u003e\n\u003cp\u003eGene set enrichment analysis was performed using fgsea v1.32.2\u003csup\u003e57\u003c/sup\u003e. Gene sets were obtained from Reactome via msigdbr v10.0.1. Genes included in each differential expression analysis were ranked by moderated t-statistics. Enrichment P values were calculated from 10,000 permutations and adjusted for multiple testing using the Benjamini\u0026ndash;Hochberg procedure.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTranscription factor activity inference\u003c/h2\u003e \u003cp\u003eTranscription factors and their targets were collected from \u003cem\u003eCollecTRI\u003c/em\u003e using \u003cem\u003eOmniPathR\u003c/em\u003e v 3.18.2\u003csup\u003e58,59\u003c/sup\u003e. The Univariate Linear Model method implemented in \u003cem\u003edecoupleR\u003c/em\u003e v2.16.0\u003csup\u003e60\u003c/sup\u003e was used to infer transcription factor activity based on the moderated t-statistics of target genes calculated by comparing gene expression in each subgroup to controls.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003eRNA expression was normalised using the trimmed mean of M-values method\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e in edgeR v4.2.1\u003csup\u003e54\u003c/sup\u003e. BSJ, FSJ and TE subfamily expression were normalised using library size and normalisation factor estimates generated by gene expression data. Low expressed genes and TE subfamilies were removed using filterByExpr with default parameters. BSJ and their corresponding FSJ loci with counts per million\u0026thinsp;\u0026lt;\u0026thinsp;0.1 in \u0026lt;\u0026thinsp;50% of samples were removed. RNA expression was voom transformed and linear models fit to each RNA using limma v3.62.2\u003csup\u003e62\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDifferential expression analyses of transcripts, FSJs, BSJs, and TEs by contrasting each study group against healthy controls. All differential expression models were adjusted for age (categorised as \u0026lt;\u0026thinsp;55, 55\u0026ndash;60, and \u0026gt;\u0026thinsp;65 years), sex, and sequencing batch). As previously described\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, we further adjusted for RNA-specific technical covariates: transcripts by percent usable bases, BSJs/FSJs by percent intronic bases, and TEs by proportion of intronic reads and median coverage standard deviation\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGlobal shifts in transcript, FSJ, BSJ, and TE expression were assessed using the distribution of moderated t-statistics from each differential expression analysis\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. For each comparison versus healthy controls, we assessed whether the observed moderated t-statistic distribution for each RNA type differed from a null distribution centred on zero, generated via 10,000 bootstrap samples. Differences in BSJ and FSJ moderated t-statistic distributions were assessed by using a permutation test (10,000 permutations). Bootstrap and permutation analyses were performed using infer v1.0.7. All analyses were conducted in R v4.5.1. Where applicable, multiple testing correction was applied using Bonferroni or Benjamini\u0026ndash;Hochberg methods. Statistical significance was defined as an adjusted P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003ecircRNAs are reduced in PD patients and prodromal individuals with hyposmia.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe first compared back-splice junction (BSJ) read counts, used as a proxy for circRNA levels, with forward-splice junction (FSJ) counts, representing the corresponding linear RNA levels, across all study groups using healthy controls as the reference (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cb\u003eSTable 3\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eBoth manifest PD and prodromal individuals showed a significant reduction in BSJ counts compared with controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). When stratifying PD into idiopathic (iPD), LRRK2-PD, and GBA1-PD subgroups, we observed a consistent reduction in circRNA levels across all three groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Similarly, circRNA reductions were observed across prodromal groups with genetic and non-genetic risk factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Notably, non-manifesting LRRK2 and GBA1 variant carriers exhibited BSJ reductions comparable to those seen in manifest PD (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Interestingly, while individuals with hyposmia demonstrated a significant reduction in circRNA abundance, individuals with RBD showed a significant increase in BSJ counts relative to controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Importantly, all BSJ reductions were independent of FSJ changes, indicating that the change in BSJ abundance is not a reflection of globally reduced transcription (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cb\u003eSFigure 3\u003c/b\u003e and \u003cb\u003eSTable 4\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMean moderated t-statistics (+/- 95% confidence interval) from the comparison of circRNA (BSJ) and linear RNA (FSJ) expression to Controls (n\u0026thinsp;=\u0026thinsp;162) across \u003cb\u003e(a)\u003c/b\u003e top-line PPMI cohorts (PD n\u0026thinsp;=\u0026thinsp;490, Prodromal n\u0026thinsp;=\u0026thinsp;264), \u003cb\u003e(b)\u003c/b\u003e PD subgroups (iPD n\u0026thinsp;=\u0026thinsp;324, \u003cem\u003eLRRK2\u003c/em\u003e PD n\u0026thinsp;=\u0026thinsp;125, \u003cem\u003eGBA\u003c/em\u003e PD n\u0026thinsp;=\u0026thinsp;36), \u003cb\u003e(c)\u003c/b\u003e Prodromal subgroups (\u003cem\u003eLRRK2\u003c/em\u003e control n\u0026thinsp;=\u0026thinsp;136, \u003cem\u003eGBA\u003c/em\u003e Control n\u0026thinsp;=\u0026thinsp;76, Hyposmia n\u0026thinsp;=\u0026thinsp;18, RBD n\u0026thinsp;=\u0026thinsp;24). Significance assessed using a simulated null distribution centred on zero. \u003cem\u003eP\u003c/em\u003e-values were Bonferroni corrected based on two tests for each comparison. Source data is available in \u003cb\u003eSTables 3, 4\u003c/b\u003e and \u003cb\u003e5\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePD patients and prodromal individuals show elevated TE expression.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIncreases of selected transposable elements (TE) expression has been reported in PD\u003csup\u003e66\u003c/sup\u003e, and circRNA abundance has been linked to TE activity\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. We therefore investigated whether differential TE expression occurs in the PD and prodromal groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cb\u003eSTables 6\u003c/b\u003e and \u003cb\u003e7\u003c/b\u003e).\u003c/p\u003e \u003cp\u003ePD and prodromal individuals showed opposing patterns of BSJ abundance and TE expression. Expression of both class I (LTR, LINE, SINE, SVA) and class II (DNA transposons, rolling-circle transposons) elements was significantly increased in iPD and in prodromal individuals compared with controls when analysed as aggregated groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Elevated TE expression was also observed when stratifying individuals by genetic (e.g., LRRK2, GBA1) and non-genetic risk factors, in both manifest PD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) and prodromal groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Notably, TE expression was increased in individuals with RBD, despite this subgroup not showing reduced circRNA levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMean moderated t-statistics (+/- 95% confidence interval) from the comparison of Class I and II transposable element subfamilies to Controls (n\u0026thinsp;=\u0026thinsp;162) across \u003cb\u003e(a)\u003c/b\u003e top-line PPMI cohorts (PD n\u0026thinsp;=\u0026thinsp;490, Prodromal n\u0026thinsp;=\u0026thinsp;264), \u003cb\u003e(b)\u003c/b\u003e PD subgroups (iPD n\u0026thinsp;=\u0026thinsp;324, \u003cem\u003eLRRK2\u003c/em\u003e PD n\u0026thinsp;=\u0026thinsp;125, \u003cem\u003eGBA\u003c/em\u003e PD n\u0026thinsp;=\u0026thinsp;36), \u003cb\u003e(c)\u003c/b\u003e Prodromal subgroups (\u003cem\u003eLRRK2\u003c/em\u003e control n\u0026thinsp;=\u0026thinsp;136, \u003cem\u003eGBA\u003c/em\u003e Control n\u0026thinsp;=\u0026thinsp;76, Hyposmia n\u0026thinsp;=\u0026thinsp;18, RBD n\u0026thinsp;=\u0026thinsp;24). Significance assessed using a simulated null distribution centred on zero. \u003cem\u003eP\u003c/em\u003e-values were Bonferroni corrected based on two tests for each comparison. T-statistic distributions are shown in SFigure 4 and source data is available in \u003cb\u003eSTable 6\u003c/b\u003e.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eThe dsRNA-IIR is activated in LRRK2 and GBA1 PD, and prodromal individuals\u003c/h2\u003e \u003cp\u003eIn our previous study\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, reduced circRNA levels in iPD were accompanied by increased expression of genes involved in circRNA biogenesis as well as genes responding to double-stranded RNA (dsRNA) and activating innate immune pathways. We therefore investigated whether similar transcriptional signatures were present in genetic forms of PD and in prodromal individuals.\u003c/p\u003e \u003cp\u003ePatients with LRRK2- and GBA1-associated PD displayed circRNA biogenesis and dsRNA-IIR expression patterns similar to those observed in iPD\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cb\u003eSTable 8\u003c/b\u003e), although these do not always reach statistical significance. Both groups showed increased expression of the RNA-editing enzyme ADAR\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e, the dsRNA sensor RIG-I (encoded by \u003cem\u003eDDX58\u003c/em\u003e)\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e, the antiviral kinase EIF2AK2 (also known as PKR)\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e, the inflammasome component NLRP1\u003csup\u003e69\u003c/sup\u003e, and the interferon-induced endoribonuclease RNASEL\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea,b). This expression pattern is consistent with the observed reduction in ILF3\u003csup\u003e71\u003c/sup\u003e, a negative regulator of the innate immune response that also participates in circRNA biogenesis⁵⁶ (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Similar to ADAR1 and RNASEL, decreased ILF3 expression is consistent with reduced circRNA production\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. Interestingly, expression of the dsRNA sensor MDA5 (encoded by \u003cem\u003eIFIH1\u003c/em\u003e)\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e and its regulatory partner LGP2 (encoded by \u003cem\u003eDHX58\u003c/em\u003e)\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e were both increased in iPD and in GBA1-PD, but reduced in LRRK2-PD (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb), suggesting mutation-specific differences in innate immune regulation. Comparable expression profiles were observed across both genetic and non-genetic prodromal groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb), including in individuals with RBD, who notably did not exhibit reduced circRNA levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eBecause the downstream effects of the dsRNA response are mediated through transcription factor (TF) activation, we next assessed TF expression to determine whether the observed dsRNA-IIR signature translated into functional regulatory changes. We inferred TF activity from transcript-level changes in their target gene regulons (\u003cb\u003eSTable 9\u003c/b\u003e), focusing on 12 TFs known to mediate the dsRNA-IIR (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Analysis revealed increased expression of AP-1 and its component JUN in iPD, LRRK2-PD, and GBA-PD patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). NF-κB, STAT1, and STAT2 regulons were increased in iPD and GBA-PD patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec), consistent with activation of canonical antiviral signalling pathways\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. As RNase L\u0026ndash;mediated circRNA degradation(REF) can activate the integrated stress response (ISR) via PKR-dependent phosphorylation of eIF2α\u003csup\u003e75\u003c/sup\u003e, we also examined key ISR-associated TFs. Regulons of ATF4 and DDIT3 (CHOP), central effectors of the ISR, were increased in iPD patients, with elevated DDIT3 activity also observed in GBA-PD patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eIntriguingly, pathway analysis excluding terms directly linked to dsRNA sensing or circRNA modulation revealed broad reductions in pathways associated with RNA metabolism and global RNA processing including translation, RNA turnover, and mRNA splicing (e.g., EIF2AK4 response, translation initiation/elongation), across PD and prodromal groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed and \u003cb\u003eSTable 10\u003c/b\u003e). In contrast, pathways related to olfactory signalling and sensory perception were elevated in iPD, LRRK2-PD, and in all prodromal groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). Pathways associated with infectious disease responses were increased in iPD and GBA1-PD, but not in prodromal individuals.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e(a)\u003c/b\u003e Expression changes of genes involved in modulating the levels of multiple circRNAs. \u003cb\u003e(b)\u003c/b\u003e Expression changes of genes involved in the sensing of double-stranded RNA (dsRNA). \u003cb\u003e(c)\u003c/b\u003e Inferred activity of transcription factors (TFs) downstream of the dsRNA response. \u003cb\u003e(d)\u003c/b\u003e Reactome pathway gene set enrichment analysis. Only terms with FDR-adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in at least six PPMI subgroup comparisons are shown. Where present, * indicates FDR-adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Source data is available in \u003cb\u003eSTables 8, 9\u003c/b\u003e and \u003cb\u003e10.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur previous work\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e demonstrated that early-stage idiopathic Parkinson\u0026rsquo;s disease (iPD) patients show a global reduction of circRNAs in blood compared with matched controls. Here, we extend these findings across genetic and prodromal forms of PD and demonstrate that reduced circRNA abundance and the transcriptional changes in peripheral blood cell populations is detectable not only in manifest idiopathic and genetic PD, but also in individuals at increased risk of disease. As observed in iPD patients, these groups also display increased expression of genes associated with dsRNA-induced innate immune response (dsRNA-IIR), along with elevated levels of class I and class II transposable elements (TEs). Together, these results identify a conserved peripheral RNA signature across idiopathic, genetic, and at-risk groups.\u003c/p\u003e \u003cp\u003eThe global reduction of blood circRNAs across PD idiopathic and genetic sub-groups suggests a shared disturbance in RNA homeostasis, irrespective of the underlying genetic or idiopathic drivers. Notably, the magnitude and pattern of circRNA reduction are highly similar between iPD, \u003cem\u003eLRRK2\u003c/em\u003e-, and \u003cem\u003eGBA1\u003c/em\u003e-PD, suggesting that this change is largely independent of clinical heterogeneity and synuclein pathology\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.Importantly, circRNA reductions were also observed in non-manifesting \u003cem\u003eLRRK2\u003c/em\u003e and \u003cem\u003eGBA1\u003c/em\u003e carriers, indicating that altered circRNA abundance is detectable prior to clinical diagnosis. Given that around 50% of LRRK2\u003csup\u003e3\u003c/sup\u003e and around 30% of \u003cem\u003eGBA1\u003c/em\u003e\u003csup\u003e76\u003c/sup\u003e carriers will develop PD by age 80, these findings raise the possibility that reduced circRNAs could serve as early indicators of disease risk. However, longitudinal studies will be required to determine whether circRNA abundance predicts phenoconversion. Our observations are supported by previous work using the Parkinson\u0026rsquo;s Disease Biomarkers Program dataset, which showed that LRRK2 and GBA1 mutations are associated with a reduction of specific circRNAs\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. Interestingly, although the authors focus on specific circRNAs, their figures showed a general reduction in circRNA levels\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHow GBA1 and LRRK2 variants influence circRNA abundance remains unclear. Both genes have been linked to immune dysregulation and type-I interferon signalling\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e,\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e. Our data are consistent with a model in which genetic risk variants amplify or modify activation of pattern-recognition receptors (e.g., RIG-I, MDA5, TLR3) and downstream interferon signalling pathways, potentially promoting RNase-mediated RNA decay mechanisms that reduce steady-state circRNA levels. However, our cross-sectional analyses cannot establish directionality or causality between dsRNA pathway activation and circRNA depletion.\u003c/p\u003e \u003cp\u003eAdditionally, \u003cem\u003eLRRK2\u003c/em\u003e and \u003cem\u003eGBA1\u003c/em\u003e variants may perturb RNA homeostasis through distinct mechanisms that influence on circRNA regulation. LRRK2-driven changes in translation and RNA-binding protein activity are predicted to shift the balance between canonical splicing and back-splicing, potentially altering circRNA production\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e,\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. In parallel, GBA1-mediated lysosomal and ER stress may remodel transcriptional and RNA-processing networks \u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e, which could likewise influence circRNA biogenesis and turnover. While our data suggest a shared effect on downstream RNA regulatory pathways, mutation-specific differences in dsRNA sensor expression indicate that upstream mechanisms may differ between subtypes\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eInterestingly, individuals with hyposmia exhibited circRNA reductions similar to those observed in manifest PD, whereas those with RBD did not. This divergence may reflect biological heterogeneity between prodromal subtypes, differences in disease stage, or limited statistical power in smaller subgroups such as RBD (n\u0026thinsp;=\u0026thinsp;24). Although these findings could be interpreted within proposed \u0026lsquo;brain-first\u0026rsquo; and \u0026lsquo;body-first\u0026rsquo; models of PD\u003csup\u003e83\u003c/sup\u003e, our data do not directly distinguish between these frameworks and should be interpreted cautiously.\u003c/p\u003e \u003cp\u003eInflammatory processes can involve increased TE expression\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e, and TE-derived RNA intermediates can activate dsRNA sensing pathways. Consistent with this, reduced circRNA abundance across PD and prodromal subgroups was accompanied by elevated TE expression and increased expression of dsRNA-IIR genes, including ADAR1, DDX58 (RIG-I), EIF2AK2 (PKR), and RNASEL\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e,\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e,\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e. These changes support a model of enhanced antiviral-like signalling in peripheral blood. However, we did not directly measure dsRNA species, RNA editing levels, or RNase activity, and therefore cannot determine whether TE expression is a driver or consequence of innate immune activation. The presence of increased TE expression in RBD individuals without circRNA reduction further suggests that TE dysregulation and circRNA depletion may occur at different stages or represent partially independent processes and suggests that TE expression could be used as a biomarker of PD onset or progression\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eVariable expression of OAS2, IFIH1 (MDA5), and DHX58 (LGP2), particularly the relative attenuation in LRRK2-PD compared with iPD and GBA1-PD, suggests subtype-specific modulation of dsRNA sensing pathways. These differences are consistent with prior reports that LRRK2-associated PD exhibits a distinct inflammatory signature\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e. Our findings suggest that while RIG-I signalling appears activated across PD groups to some degree, MDA5 expression differs between genotypes. Whether these differences reflect intrinsic mutation-specific immune modulation or secondary effects of disease stage warrants further investigation.\u003c/p\u003e \u003cp\u003ePathway analysis indicates reductions in RNA metabolism, translation, and RNA processing pathways across PD and prodromal groups. This widespread downregulation of RNA processing pathways, alongside upregulation of dsRNA-responsive transcription factors (AP-1, NF-κB, STAT1/2, ATF4, DDIT3), suggests coordinated remodelling of RNA surveillance and stress-response networks in peripheral blood\u003csup\u003e\u003cspan additionalcitationids=\"CR87\" citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e. Upregulation of genes involved in olfactory signalling pathways in both PD and prodromal groups was notable and may reflect systemic molecular correlates of early sensory dysfunction\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e, although the functional relevance of peripheral olfactory pathway enrichment remains uncertain.\u003c/p\u003e \u003cp\u003eCompared with the number of controls and PD patients analysed in this study the prodromal cohorts were small, reducing the power to detect statistically significant expression changes. However, the changes we were able to detect were congruent with the observations in patients with manifest disease. Thus it is unlikely that they represent false positives a phenomenon that can affect underpowered studies\u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn summary, we identify a conserved peripheral molecular signature characterised by reduced circRNA abundance, increased TE expression, and activation of dsRNA-induced innate immune pathways across idiopathic, genetic, and prodromal PD. These findings support a model in which dysregulated RNA homeostasis and antiviral-like signalling are detectable in peripheral blood prior to clinical diagnosis. While mechanistic relationships remain to be established, this RNA-based signature provides a framework for future longitudinal and functional studies aimed at clarifying its role in PD pathogenesis and its potential utility in risk stratification.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003ePPMI raw RNA sequencing and corresponding clinical data are available from https://www.ppmi-info.org/. Summary data used to generate summary statistics and figures are included as supplementary datasets. Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).\u003c/p\u003e\n\u003ch2\u003eCode availability\u003c/h2\u003e\n\u003cp\u003eThe code used in this analysis is available at https://github.com/bj-w/PD-lin-circ-RNA-paper\u003c/p\u003e\n\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\n\u003cp\u003eMJ, MSK and GH designed the study. BW, OI and CC performed data generation and analysis. BW, MSK and GH conducted statistical analysis. BW, MSK and GH wrote the manuscript with feedback and input from all authors. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThis work was funded by the Michael J. Fox Foundation (Grant ID MJFF-007574). GH receives funding from Wellcome (203105/Z/16/Z), the Michael J. Fox Foundation (MJFF-007574), Parkinson\u0026rsquo;s UK (G-2003 and G-2201) and is supported through the LifeArc Centre for Rare Mitochondrial Disease (REF:10748) and the National Institute for Health Research (NIHR) Newcastle Biomedical Research Unit and Centre (BRC) based at Newcastle upon Tyne Hospitals NHS Foundation Trust and Newcastle University. The authors would like to express their deepest gratitude to the Parkinson\u0026rsquo;s Progression Markers Initiative team members as well as to the patients and staff at each study.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBuniello, A., \u003cem\u003eet al.\u003c/em\u003e The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. \u003cem\u003eNucleic Acids Res\u003c/em\u003e 47, D1005-D1012 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchindlbeck, K.A., \u003cem\u003eet al.\u003c/em\u003e LRRK2 and GBA Variants Exert Distinct Influences on Parkinson's Disease-Specific Metabolic Networks. \u003cem\u003eCereb Cortex\u003c/em\u003e 30, 2867\u0026ndash;2878 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKmiecik, M.J., \u003cem\u003eet al.\u003c/em\u003e Genetic analysis and natural history of Parkinson's disease due to the LRRK2 G2019S variant. \u003cem\u003eBrain\u003c/em\u003e 147, 1996\u0026ndash;2008 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalestrino, R., Tunesi, S., Tesei, S., Lopiano, L., Zecchinelli, A.L. \u0026amp; Goldwurm, S. 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The power and pitfalls of underpowered studies. \u003cem\u003ePharmacotherapy\u003c/em\u003e 44, 698\u0026ndash;700 (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-parkinsons-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjparkd","sideBox":"Learn more about [npj Parkinson's Disease](http://www.nature.com/npjparkd/)","snPcode":"41531","submissionUrl":"https://submission.springernature.com/new-submission/41531/3","title":"npj Parkinson's Disease","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9178344/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9178344/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe have previously reported a global reduction of circular RNA (circRNA) levels in the peripheral blood of patients with early-stage idiopathic PD (iPD). This reduction was accompanied by increased expression of genes involved in innate immune response to double-stranded RNA. (dsRNA-IIR).\u003c/p\u003e \u003cp\u003eHere, we extend these findings using whole-blood RNA sequencing data from 916 participants in the Parkinson\u0026rsquo;s Progression Markers Initiative. These consisted of individuals with idiopathic PD, pathogenic mutations in LRRK2 and GBA1, with and without disease manifestation, prodromal individuals with REM sleep behaviour disorder or hyposmia, two clinical features considered to indicate increased PD risk and healthy controls.\u003c/p\u003e \u003cp\u003eWe demonstrate that reduced circRNA abundance is not restricted to iPD but is also present in LRRK2 and GBA1-associated PD and in mutation carriers without manifest disease. CircRNA reduction was accompanied by increased expression of class I and class II transposable elements (TEs) and upregulation of dsRNA sensing and interferon-responsive genes, (ADAR1, DDX58/RIG-I, EIF2AK2/PKR and RNASEL. Inferred transcription factor activity was consistent with activation of antiviral and stress-signalling pathways.\u003c/p\u003e \u003cp\u003eAmong the prodromal groups, only individuals with hyposmia showed circRNA reduction, not those with REM sleep behaviour disorder, although, both groups exhibited elevated TE expression and increased expression of the genes mentioned above.\u003c/p\u003e \u003cp\u003eOur results indicate that changes in circRNA levels are a general feature of PD and that their onset occurs early during disease development. They are consistent with dsRNA-IIR involvement in the in the development of PD and point to potential approaches for intervention.\u003c/p\u003e","manuscriptTitle":"Transcriptional changes in the peripheral blood of at-risk individuals without clinical manifestation of Parkinson’s Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-07 10:20:51","doi":"10.21203/rs.3.rs-9178344/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-10T10:02:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-09T11:40:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-06T23:32:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-04T21:48:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"310483155043498267388801713621947152666","date":"2026-04-02T03:39:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265597542605768589059616184192259489021","date":"2026-04-01T20:47:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"261476802335292770838153074374511266048","date":"2026-04-01T14:49:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"126742092144731558097600902849820365012","date":"2026-04-01T12:13:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"217015455048784543299443702698033416394","date":"2026-04-01T11:57:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-01T10:54:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-24T20:57:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-23T12:21:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Parkinson's Disease","date":"2026-03-20T11:11:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-parkinsons-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjparkd","sideBox":"Learn more about [npj Parkinson's Disease](http://www.nature.com/npjparkd/)","snPcode":"41531","submissionUrl":"https://submission.springernature.com/new-submission/41531/3","title":"npj Parkinson's Disease","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6b2c23e3-c68a-496a-8956-180370e2662f","owner":[],"postedDate":"April 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":65733330,"name":"Health sciences/Diseases"},{"id":65733331,"name":"Biological sciences/Genetics"},{"id":65733332,"name":"Biological sciences/Molecular biology"},{"id":65733333,"name":"Health sciences/Neurology"},{"id":65733334,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-04-10T10:11:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-07 10:20:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9178344","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9178344","identity":"rs-9178344","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}