Parkinson’s disease is associated with an imbalance in circular RNA expression

doi:10.21203/rs.3.rs-3202518/v1

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Parkinson’s disease is associated with an imbalance in circular RNA expression

https://doi.org/10.21203/rs.3.rs-3202518/v1

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published 20 Jan, 2024

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Neurodegeneration in Parkinson’s disease (PD) precedes diagnosis by years. Early neurodegeneration may be reflected in RNA levels and measurable as a biomarker. Here, we present the largest quantification of whole blood linear and circular RNAs (circRNA) in early-stage PD, using RNA sequencing data from two cohorts (PPMI = 259 PD, 161 Controls; ICICLE-PD = 48 PD, 48 Controls). We identified a replicable increase in TMEM252 and LMNB1 gene expression in PD. We identified novel differences in the expression of circRNAs from ESYT2, BMS1P1 and CCDC9, and replicated trends of previously reported circRNAs. Overall, using circRNA as a diagnostic biomarker in Parkinson’s did not show any clear improvement over linear RNA. More interestingly, we observed a general reduction in circRNA expression in both PD cohorts, accompanied by an increase in RNASEL expression. This imbalance implicates the activation of an innate antiviral immune response and suggests a previously unknown aspect of circRNA regulation in PD.

Biological sciences/Computational biology and bioinformatics/Gene regulatory networks

Health sciences/Medical research/Preclinical research

Health sciences/Biomarkers/Predictive markers

Parkinson&rsquo

s disease

biomarker

messenger RNA

circular RNA

Parkinson’s disease (PD) is a neurodegenerative disease characterised by a progressive loss of dopaminergic neurons in the substantia nigra¹. Diagnosis is predominantly based on the presentation of cardinal symptoms, including motor (such as bradykinesia and gait disturbances) and non-motor (sleep problems, cognitive impairment and dementia) symptoms¹, which result in substantial morbidity². Unfortunately, the clinical manifestation of PD typically occurs years after the onset of dopaminergic neuron loss, making early diagnosis challenging³. Measures to slow the rate of progression, therefore, remain a key goal in PD research⁴. The reliance on the observation of clinical symptoms hampers the detection in the earliest phases of neurodegeneration when disease-modifying intervention would be most beneficial⁵. Additionally, diagnostic accuracy is adversely affected by shared clinical presentations with other neurodegenerative conditions, resulting in an estimated misdiagnosis rate of ~ 25% in the early stages of disease³. The identification of biomarkers that can facilitate accurate early diagnosis and lead to the discovery of new therapeutic targets are therefore crucial for the future clinical management of PD⁶.

Despite significant effort, few PD biomarkers have been translated into clinical practice⁶, in part limited by a lack of replication⁷. Efforts to identify biomarkers of PD onset have been hampered by heterogeneity in clinical presentation and the rate of progression of patients⁸. Familial PD cases, both dominant and recessive, account for ~ 10–15% of cases⁹, whilst the majority of remaining cases are considered idiopathic. The current largest GWAS meta-analysis identified 90 variants explaining ~ 16–36% of PD heritability¹⁰, yet polygenic risk scoring is not currently at a stage to be used clinically to predict an individual’s risk¹⁰. Neuroimaging show promising results, with accuracies typically > 80%¹¹. However, these techniques are expensive and usually most accurate in advanced disease¹² or when applied in combination¹³.

Biofluid-based biomarker candidates, including neurochemicals such as orexin¹⁴ and metabolite biomarkers including lactate and acylcarnitines^15,16 have been proposed. Recent work assessing α-synuclein seeding in CSF is promising in specific PD subgroups¹⁷, although the invasive nature of a lumbar puncture limits its utility. The accessibility of whole blood, as well as a high degree of overlap with neuronal expression (> 80% shared gene expression)^18,19, has prompted research into assessing blood-based RNAs as biomarkers of PD. Early array-based and, more recently, RNA sequencing-based experiments have identified several messenger RNA (mRNA)^20–28, long non-coding RNAs (lncRNA)²⁹ and microRNAs (miRNAs)^30–33 as potential biomarkers of PD. However, there is little concordance between studies^34,24,26,35, likely a result of differences in methodologies, sample cohorts and study design^36,24,26.

Circular RNA (circRNA) is an RNA species characterised by the formation of a back-splice junction (BSJ, Fig. 1)³⁷. CircRNA expression can vary across cell, tissue and development stages^38–40. Interestingly, circRNA expression can be uncoupled from that of its parental host gene^41,40,42 and several modulators of global circRNA levels have been reported^43–53. CircRNAs are abundant in the brain⁵⁴, enriched in genes related to neuronal function⁵⁵, and readily identifiable in blood^56,40. Their reported resistance to RNA exonuclease activity and increased stability compared to their linear counterparts^57,58, have prompted investigations of their use as biomarkers in a range of human diseases, including PD^59,60. Recent work has identified differentially expressed circRNAs in blood cells and brain tissue from PD patients^61,62. However, a large-scale detection of blood circRNAs in PD has yet to be undertaken.

Here, we investigate the potential utility of linear and circular RNA as biomarkers for PD in two large unrelated cohorts, The Michael J Fox Foundation Parkinson’s Progression Markers Initiative⁶³ (PPMI) and The Incidence of Cognitive Impairment in Cohorts with Longitudinal Evaluation-PD (ICICLE-PD)⁶⁴, adopting a discovery-replication strategy. Our analysis indicates that, although specific genes and circular RNAs are differently expressed, neither has sufficient discriminatory power to displace existing PD biomarkers. Interestingly, we observed a reproducible reduction in general circRNA expression levels in PD patients, which was not recapitulated in canonical splice junction expression, suggestive of a link between circRNA expression and PD.

Differentially expressed genes in PD

We utilised high-depth total RNA sequencing to quantify linear and circular RNA expression in the blood across two distinct cohorts (Fig. 1). We compared early-stage idiopathic Parkinson’s disease (PD) patients (diagnosed < 13 months) to matched controls, using PPMI as a discovery cohort and ICICLE-PD as a replication cohort (Fig. 1). Testing for gene differential expression was carried out on 16,191 (PPMI) and 15,852 (ICICLE-PD) genes respectively (overlap of 95.53%). In PPMI, we identified 44 significantly differentially expressed genes (log₂FC<-0.1/>0.1, FDR < 0.05), with 28 downregulated and 16 upregulated in PD (Fig. 2a and STable 2). Of these 44, TMEM252 and LMNB1 were similarly upregulated in ICICLE-PD (log₂FC > 0.1, FDR < 0.05) (Fig. 2a-b and STable 2).

To gain an overview of global alterations in gene expression patterns we performed gene set enrichment analysis (GSEA) ⁶⁵. After classifying into GO terms, we identified 97 biological processes, 11 molecular functions and 29 cellular components that were significantly enriched in PPMI and subsequently replicated in ICICLE-PD (FDR < 0.05 with the same direction of enrichment) (STable 3). Ontologies related to ribosomal function and translation were more likely to be decreased in PD. In contrast, ontologies related to the innate immune response, including antiviral and interferon signalling processes, tended to be increased in PD (the top 10 ranked by PPMI P-value in each ontology are shown in Fig. 2c with all results present in STable 3). GSEA of KEGG pathways also showed the significant enrichment of the pathways Ribosome (PPMI FDR = 7.58x10^− 24; ICICLE-PD FDR = 5.57x10^− 10) and NOD-like receptor signalling (PPMI FDR = 0.0016, ICICLE-PD FDR = 2.27x10^− 6), part of the innate immune response⁶⁶ (STable 4).

We were able to independently replicate (FDR < 0.05) genes previously reported as differentially expressed in the blood^67–70 using PPMI (LSMEM1, TPST1 and SLED1) and ICICLE-PD (IFIT1, RSAD2, IFI44L and OLIG2) datasets (Fig. 2d and STable 5). Overlapping the expression of genes potentially underpinning PD GWAS risk loci¹⁰, identified five significantly differentially expressed genes (BST1, FCGR2A, SIPA1L2, NOD2 and VAMP4, log₂FC > 0.1/<-0.1, FDR < 0.05) in PPMI, although none passed multiple testing correction in ICICLE-PD (Fig. 2e and STable 6). Overlapping with highly penetrant pathogenic parkinsonism genes, we identified PTRHD1 as significantly decreased in PD (FDR = 0.015), with a similar trend reported in ICICLE-PD (FDR = 0.088) (STable 7).

Circular RNAs are differentially expressed in PD

Circular RNAs were detected based on the presence of a back-spliced junction (BSJ, Fig. 1), combining multiple detection algorithms to define high-confidence BSJs (see Circular RNA detection and quantification). Filtering of lower expressed circRNAs left a set of abundant circRNAs (PPMI = 403, ICICLE-PD = 457, 62.57% overlap). We observed no overrepresentation of GO terms after grouping circRNAs based on their host gene (STable 8). We identified three BSJs significantly reduced in PD versus controls in PPMI (log₂FC<-0.1/>0.1, FDR < 0.05) (Figs. 3a-b and STable 9). These BSJs, derived from the genes ESYT2, BMS1P1, and CCDC9 were similarly decreased in ICICLE-PD but did not reach statistical significance (FDR > 0.05) (Figs. 3a-b and STable 9). Seven circRNAs previously reported as differentially expressed in PD^61,62 were sufficiently expressed in the PPMI or ICICLE-PD cohorts (STable 10). Two BSJs, derived from DOP1B and INTS6L, showed altered expression in PPMI (P-value < 0.05) (STable 10). While no BSJ reached statistical significance after multiple testing correction (FDR > 0.05), we were able to replicate the direction of change of four BSJs in PPMI and three in ICICLE-PD (Fig. 3c and STable 10).

PD is characterised by an imbalance in circRNA abundance

Whilst we observed differential expression of specific BSJs, perhaps the more striking observation was an imbalance of BSJ fold changes, i.e., where the expression of the majority of BSJs tended to be reduced in PD compared to controls (PPMI imbalance = 0.91, P-value = 2.31x10^− 32, ICICLE-PD imbalance = 0.71, P-value = 2.34x10^− 10, Exact binomial test) (Fig. 4a, STable 11). FSJ counts provide the best approximation of the linear RNA as gene expression estimates will inevitably include circRNA-derived reads that do not span the BSJ and thus cannot be distinguished. Crucially, an imbalance was not evident when examining the fold changes of flanking forward-spliced junctions (FSJs) (PPMI imbalance = 0.46, P-value = 0.74, ICICLE-PD imbalance = 0.50, P-value = 1, Exact binomial test) (Fig. 4a, STable 11). A comparison of fold changes based only on genes that host circRNAs identified an imbalance in PPMI (imbalance = 0.73, P-value = 0.029, Exact binomial test), but not in ICICLE-PD (imbalance = 0.46, P-value = 0.59, Exact binomial test).

Global gene expression showed no imbalance in PPMI (imbalance = 0.51, P-value = 0.63), yet a significant imbalance in the opposite direction in ICICLE-PD (imbalance = 0.44, P-value = 9.05x10^− 19) (Fig. 4a, STable 11). As fold change imbalance estimates are dependent on the number of RNAs passing the fold change threshold, we also directly compared expression levels of all RNAs included in differential expression analyses. This also provided evidence of a significant reduction in circRNA expression in PD (PPMI P-value = 4.38x10^− 6, ICICLE-PD P-value = 8.58x10^− 11, Wilcoxon rank-sum test) (Fig. 4b), yet no reduction in the expression of flanking FSJs, expression of circRNA host genes or total gene expression (SFigure 7).

To highlight differences in circRNA regulation, we compared fold changes between BSJ expression and their flanking FSJs. For a subset of junctions, this was not possible as no corresponding FSJs were detected (PPMI = 95/403 or 23.6%, ICICLE-PD = 92/457 or 20.1%). We observed a weak correlation in fold change differences between BSJs and corresponding FSJs (PPMI Spearman’s rho = 0.31, ICICLE-PD Spearman’s rho = 0.21, Fig. 4c). BSJ fold changes were consistently lower, albeit generally modest, across both cohorts (PPMI P-value = 2.88x10^− 34, ICICLE-PD P-value = 1.60x10^− 9, Wilcoxon rank-sum test) (Fig. 4d). Together, these results suggest that the observed imbalance in circRNA expression in PD is not solely driven by a reduction in parental host gene expression.

RNASEL and ADAR expression is increased in PD

CircRNA biogenesis and degradation are linked to several cis- and trans-acting factors³⁷. We explored whether differential expression of previously reported circRNA regulators^43–53 may be contributing to the reduced circRNA abundances in PD (regulators and evidence are given in STable 12). In PPMI, RNASEL and ADAR were significantly increased in PD, with RNASEL also increased in ICICLE-PD and significant after multiple testing correction (Fig. 4e, STable 12). RNASEL encodes Ribonuclease L, which is known to be involved in the degradation of circRNA, subsequently allowing the activation of PKR as part of the integrated stress response⁵⁰. In line with this, we observed significantly increased expression of EIF2AK2, encoding PKR, in both cohorts (PPMI P-value = 0.010; ICICLE-PD P-value = 0.019) (Fig. 4f, STable 2).

Evaluating RNA expression as a predictor of early-stage idiopathic PD

To evaluate the use of RNA expression as a predictor of early-stage idiopathic PD, we first examined the potential of BSJ and FSJ junctions, as well as BSJ:FSJ junction ratios, to separate PD from controls. Area under the receiver operating characteristics curves (AUC) were generated using the expression of each abundant junction type as a predictor. The median AUC across all junction measures was higher in ICICLE-PD (0.57) compared to PPMI (0.53). The strongest correlation between PPMI and ICICLE-PD AUCs was for BSJ:FSJ ratios (ρ = 0.33), followed by FSJ (ρ = 0.22) and BSJ counts (ρ = 0.18) (Fig. 5a).

With no clear individual junction biomarker candidates, we included multiple features in multivariable regularised logistic regression models (see Classification of Parkinson’s status using RNA abundances). Using cross-validation, we trained and evaluated the classification performance of models using the PPMI cohort. We then used the ICICLE-PD as an independent test cohort to assess the performance of classifiers trained using the PPMI cohort. The best performance in the PPMI cohort was achieved by combining gene and BSJ counts (AUC = 0.85 [0.81, 0.89], Fig. 5b) and appeared primarily driven by gene expression (AUC = 0.84 [0.81, 0.88], Fig. 5b) rather than BSJ counts (AUC = 0.61 [0.55, 0.66], Fig. 5b). In ICICLE-PD, combining gene and BSJ counts produced an AUC of 0.60 [0.48, 0.71] (Fig. 5b). Similarly, the performance of the gene expression strategy also decreased in the ICICLE-PD cohort (AUC = 0.59 [0.48, 0.71], Fig. 5b). All other classifiers had an AUC < 0.6 in the PPMI cohort (Fig. 5b, STable 13). Using BSJ:FSJ ratio as a classifier achieved the best generalisability in the ICICLE-PD cohort (AUC = 0.63 [0.52, 0.74], Fig. 5b). All other unmentioned models achieved an AUC < 0.6 in the ICICLE-PD cohort (Fig. 5b, STable 13).

We identified differences in the linear and circular transcriptome between early-stage PD patients and controls, with elevated expression of TMEM252 and LMNB1 observed in both PPMI and ICICLE-PD PD cases compared to controls. We independently validated expression changes in genes shown to modulate PD risk¹⁰ and in genes that had been previously reported to be differentially expressed in PD^67–70. Additionally, we discovered three novel circRNAs that were decreased in PD, with similar trends observed in our replication cohort. After evaluating the performance of multivariable classification models, our results suggest that circRNAs are unlikely to be useful biomarkers for early-stage PD, particularly when compared to classification using gene expression. Perhaps more interestingly, we observed a tendency towards generalised decreased circRNA expression in PD in both cohorts, which correlated alongside decreased expression of genes involved in protein translation and increased expression of genes involved in the immune response. These findings suggest that reduced circRNA levels in the blood may be a proxy measure of the underlying pathology and add to the growing body of evidence linking RNA metabolism and immune response to PD^71,72.

The global downregulation of circRNA expression we observed in blood is reminiscent of the reduction in microRNA expression seen in PPMI participants³³. CircRNA expression is influenced by trans-acting proteins, including Ribonuclease L (RNase L)⁵⁰ and adenosine deaminase RNA specific (ADAR), an enzyme responsible for A-I RNA editing that can modulate the base-pairing of reverse complementary matches in the flanking introns of circRNAs^54,73. ADAR was significantly increased in PPMI PD cases and showed a similar trend in ICICLE-PD (STable 12). Although ADAR expression levels do not necessarily correlate with editing activity⁷⁴, altered RNA editing has been observed in PD patients’ blood and brain regions^61,75. RNase L degrades circRNAs as a response to viral infection⁵⁰. The degradation of circRNAs is required for the activation of Protein Kinase R (PKR), a regulator of the integrated stress response (ISR)^76,50. This is consistent with our results, where the reduction in circRNAs in PD we observed coincides with increased expression of RNASEL (RNase L) and EIF2AK2 (PKR) (Figs. 4e-f, STable 2), as well as the upregulation of genes related to antiviral activity and the innate immune response (Fig. 2c, STables 3–4). Although the role of RNase L in the antiviral immune response is well documented⁷⁷, and despite the links between viral infection and PD⁷⁸, to our knowledge, there have been no reports showing RNAse L expression changes in PD. Conversely, PKR activation has been observed in PD^79,80. Endogenous dsRNA can lead to PKR activation^81,82 and has been recently implicated in neurodegenerative diseases other than PD^83–85. In mice, exogenous dsRNA triggered α-synuclein aggregation and dopaminergic neuron loss, suggesting the existence of aberrant dsRNA may have a potential role in the pathogenesis of PD⁸⁶. Activated PKR leads to eIF2α phosphorylation, and results in global protein synthesis attenuation⁷⁶. Protein synthesis is reduced in PD patient cell lines^87,88, consistent with the reduced expression of genes related to protein synthesis and ribosomal function we observed in PD (Fig. 2c, STables 3–4). Our findings suggest that reduced circRNA expression in PD is linked to the increased expression of RNase L. The degradation of circRNAs allows for the activation of PKR, consistent with the increased activity of PKR in lymphocytes of PD patients⁸⁰, and subsequently triggers the cellular ISR. Overall, these results highlight a central role of RNase L and PKR in the pathogenesis and/or systemic response to PD.

Using gene expression as a classifier of early-stage PD in the PPMI cohort was in line with previously published estimates (AUC = 0.84 compared to 0.80 in Makarious et al., 2022, Fig. 5c). In our independent replication cohort (ICICLE-PD), the AUC of the gene expression classifier was lower (ICICLE-PD AUC = 0.59, Fig. 5c). Despite promising results using circRNA as a biomarker in other diseases⁵⁹, circRNAs performed substantially worse than gene expression when classifying PD in PPMI (AUC = 0.61 vs 0.84, Fig. 5c), although similar performance was seen in ICICLE-PD (AUC = 0.59 vs 0.59, Fig. 5c). Similar to previous work assessing circRNAs as a lung cancer biomarker⁹⁰, combining circRNA expression with gene expression showed a small improvement in predictive ability over gene expression alone in both PPMI (AUC = 0.85 vs 0.84, Fig. 5c) and ICICLE-PD (AUC = 0.60 vs 0.59, Fig. 5c). This leads us to the conclusion that, for PD at least, circRNA expression has limited utility as a standalone biomarker but could be aggregated with other types of data to improve the detection of early-stage idiopathic PD.

When comparing differential gene expression, we identified two genes (TMEM252 and LMNB1) that were upregulated in PD cases in both cohorts, consistent with previous work²⁷. TMEM252 encodes a transmembrane protein of unknown function that has been linked to cancer⁹¹, but has not previously been implicated in PD. LMNB1 encodes Lamin B1, a component of the nuclear lamina. Increased LMNB1 expression has been reported in the dopaminergic neurons of PD patients⁹² and a variant within LMNB1 has been associated with cognitive outcomes in PD⁹³.

We were also able to independently replicate the differential expression of genes that have been previously reported in blood RNA-seq studies as differentially expressed in PD patients^67–70 (LSMEM1, TPST1 and SLED1, Fig. 2d). As previous studies were not limited to early-stage PD (diagnosed < 13 months), these genes may reflect those with altered expression throughout PD development. Of the three genes replicated using PPMI, SLED1 and LSMEM1 encode proteins of unknown function. TPST1 encodes tyrosylprotein sulfotransferase 1, an enzyme required for post-translational modification of proteins that also plays important roles in the inflammatory process, leukocyte movement and cytosis, viral cell entrance, and other cell-cell and protein-protein interactions⁹⁴. Interestingly, 3 out of the 4 previously reported genes replicated in ICICLE-PD (IFIT1, RSAD2, IFI44l, Fig. 2d) are induced by type-1 interferons⁹⁵. Their increase in expression is consistent with an increase in type-1 interferon signalling in PD⁹⁶. In addition, our data provide a potential mechanistic link between PD-associated genetic variation, identifying differential expression among genes associated with PD risk. These included PTRHD1 (STable 7), which encodes a peptidyl-tRNA hydrolase that has been linked to recessive parkinsonism^97,98, and genes identified by association studies (BST1, FCGR2A, SIPA1L2, NOD2 and VAMP4, Fig. 2e)¹⁰.

Our analysis of circRNAs identified three that were over-expressed in PPMI PD cases compared to controls (within the genes BMS1P1, CCDC9 and ESYT2) with similar trends observed in ICICLE-PD (Figs. 2a-b). These circRNAs, or their host genes, have not previously been associated with PD. The protein product of CCDC9 is believed to be a member of the exon junction complex involved in RNA splicing⁹⁹. CircRNAs from CCDC9 have been implicated in cancer, acting as a miRNA sponge to suppress tumorigenesis¹⁰⁰, and in stroke, suppressing NOTCH signalling in mouse models of ischaemia¹⁰¹. Interestingly, alterations in NOTCH signalling has been linked to PD through the function of LRRK2¹⁰². ESYT2 encodes extended synaptotagmin 2, a member of the E-Syt family, which are endoplasmic reticulum (ER) localised proteins involved in tethering the ER to the cellular plasma membrane¹⁰³. There is suggestive evidence that circRNAs derived from ESYT2 may be upregulated upon viral infection¹⁰⁴, in contrast to the decreased expression we observed in PD patients (Figs. 2a-b). BMS1P1 encodes a pseudogene of ribosome biogenesis factor pseudogene 1 (BMS1), and there are no known disease associations for circRNAs produced by this gene.

We were also able to independently replicate trends in several previously reported differentially expressed circRNAs observed in PD blood and brain tissue^61,62 (Fig. 3c, STable 10). Unfortunately, 23 of the 30 previously reported circRNAs (20 from the substantia nigra and three detected in peripheral blood mononuclear cells) were undetectable at required levels in either PPMI or ICICLE-PD. We identified altered expression of circRNAs (derived from DOP1B and INTS6L), whose host genes have not been functionally linked to PD (STable 10). However, copy number variation in DOP1B has previously been linked to Alzheimer’s disease¹⁰⁵, while deletion of INTS6L leads to a cardiomyopathic phenotype¹⁰⁶. Differences in circRNA expression when compared to previous work may be attributable to tissue-specific expression (whole blood compared to the brain) or differences in blood cell composition (whole blood compared to peripheral blood mononuclear cells). Nonetheless, our data extend the growing body of evidence linking circRNA dysregulation to PD⁶⁰.

As a key vehicle for immune cells, the differences we observe in blood RNA expression in PD patients add to the proposed role of inflammation and immune dysfunction in the development and response to PD⁷². It is possible that changes in blood can be detected early on in disease duration, in line with the early presentation of non-motor symptoms and peripheral aggregations of α-Synuclein¹⁰⁷. Pre-diagnosis changes in lymphocyte levels and function have been reported^108,109. In addition, inflammation markers within blood have been associated extensively with both PD risk and symptom progression¹¹⁰.

In this work, we have drawn together and performed the largest whole-blood circular transcriptome analysis of PD, focusing on the potential utility of circRNAs as biomarkers. However, several limitations must be considered. Biomarker detection using whole-blood has numerous advantages, including accessibility and ease of processing, but we could not account for variation in the proportions of different blood cell types across sample^111,112. In addition, despite efforts to increase homogeneity (see Cohorts), both cohorts differ in some respects. Firstly, PPMI participants were not receiving treatment at sample collection, whereas most ICICLE-PD participants were. Dopaminergic treatment may influence biological measures, such as circulating cell-free mitochondrial DNA levels¹¹³. It must also be noted that some PPMI samples were recruited during a treatment interval (up to ~ 60 days before enrolment) and a large number of individuals were taking concomitant medications for a range of non-PD and PD-related symptoms⁶³. This may have influenced our results, however, the concordances we see between RNA expression as well as the reproducible reduction in circRNA expression in PD patients between cohorts suggests this is minimal. Secondly, it is inevitable that the difference between cohort sizes affected our ability to detect rarer RNAs, particularly circRNAs. We attempted to mitigate against this by using the largest cohort as discovery (PPMI) opting to replicate in the smaller cohort (ICICLE-PD). In addition, we limited our analysis to abundant genes and circular RNAs, with the majority (95.53% of genes and 62.57% of circRNAs) detectable in both cohorts. Finally, some circRNA quantification studies use circRNA enrichment steps such as RNase R treatment¹¹⁴ to enhance the detection of lesser expressed circRNAs. However, not depleting linear RNAs allowed us to quantify both linear and circular RNAs simultaneously.

In conclusion, we observed specific and consistent alterations in the linear and circular blood transcriptome in early-stage idiopathic PD patients. Changes in circRNA levels were not sufficient to facilitate reliable PD classification. We did, however, identify a reproducible reduction in circRNA expression in PD. This imbalance, along with gene expression patterns, implicates the activation of an innate antiviral immune response, providing an opportunity for future investigations into this previously unknown aspect of circRNA regulation in PD.

Cohorts

We utilised samples with corresponding demographic (e.g., age at sample collection, sex) and clinical data (e.g., dopaminergic treatment status, disease duration) from two large cohorts of patients with Parkinson’s disease (PD) and controls of similar ages and sex. The discovery cohort was obtained from The Michael J Fox Foundation Parkinson’s Progression Markers Initiative⁶³ (PPMI, https://www.ppmi-info.org/), while The Incidence of Cognitive Impairment in Cohorts with Longitudinal Evaluation-PD⁶⁴ (ICICLE-PD, https://www.bam-ncl.co.uk/iciclepd) was used for replication. To study transcriptomic changes in the early stages of PD and to ensure parity between discovery and replication cohorts, only patients recently diagnosed with PD (< 13 months) were included. PD patients harbouring causative variants in select genes (e.g., LRRK2, GBA, SNCA, PINK1, PRKN) were excluded and are thus all PD cases are idiopathic. Both studies were conducted in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines after approval of local ethics committees of the participating sites^63,64.

ICICLE-PD RNA isolation and sequencing

Between June 2009 and December 2011, newly diagnosed PD patients from the community and hospital outpatient clinics in Newcastle-upon-Tyne and Gateshead. Idiopathic PD was diagnosed by a movement disorder specialist and fulfilled Queen’s Square Brain Bank criteria¹¹⁵. Full exclusion criteria have been published elsewhere⁶⁴. Briefly, participants were excluded if they had significant cognitive impairment at presentation (Mini Mental State Examination < 24) or a pre-existing diagnosis of dementia, an atypical Parkinsonian syndrome, or insufficient English to complete assessments. We limited our analysis to 48 early-stage idiopathic PD patients and 48 control samples matched for age and sex. Total RNA was extracted from 5ml of whole blood collected and stored in PAXgene tubes as per manufacturer’s instructions (Blood RNA Kit, Qiagen), quality assessed using an Agilent 2100 Bioanalyzer system and stored at -80 C. Only samples with an RNA integrity number > 8 were included. Library preparation, including ribosomal RNA and globin depletion (Globin-Zero Gold rRNA Removal Kit, Illumina), was carried out using the TruSeq Stranded Total RNA kit (Illumina). Sequencing was performed using an Illumina NovaSeq 6000 generating 150bp paired-end reads. Median sequencing depth was estimated as 89.2 (IQR = 15.7) million paired-end reads (SFigure 1).

PPMI RNA isolation and sequencing

Subject recruitment and eligibility criteria for the PPMI study have been previously published⁶³. We limited our analysis to 287 early-stage idiopathic PD patients and 176 controls matched for age and sex. Whole blood RNAseq data was downloaded as FASTQ files from the online PPMI repository (https://www.ppmi-info.org/). RNA collection, isolation and subsequent sequencing have been previously reported²⁷ and is described in the PPMI Biologicals Manual. Briefly, whole-blood RNA was extracted from PAXgene tubes (Blood RNA Kit, Qiagen), rRNA and globin depleted (Globin-Zero Gold rRNA Removal Kit, Illumina). Library preparation used the NEB/Kapa (NEBKAP) kit (see ref²⁷ for more information). Sequencing was performed using an Illumina NovaSeq 6000 generating 125-150bp paired-end reads. Median sequencing depth was estimated as 107 (IQR = 31.2) million paired-end reads (SFigure 1).

RNA alignment and quantification

FASTQ files were aligned to the human genome (Ensembl GRCh38¹¹⁶) using HISAT2 v2.1.0¹¹⁷. RNA transcripts were quantified using the Ensembl GRCh38 v101 cDNA reference and Salmon v1.3.0¹¹⁸ using the ‘selective alignment mode’ option and correcting for sequence-specific (--seqBias), GC-content (--gcBias) and positional biases (--posBias). Transcript counts were translated into gene-level counts using tximport v1.26.1¹¹⁹ and annotated using the Ensembl v101 GRCh38 reference.

Circular RNA detection and quantification

Reads derived from circRNA molecules were identified by the presence of a backspliced junction (BSJ, Fig. 1) detected using three tools: CIRI2 v2.0.6¹²⁰, PTESfinder v2.0¹²¹ (https://github.com/osagiei/pfv2) and CIRCexplorer v2.3.8¹²². First, FASTQ files were quality (Phred < 15) and then adapter trimmed using Trim Galore v0.67 running cutadapt v4.2¹²³. Reads were aligned to the human genome (Ensembl GRCh38) according to each tool's specification: STAR v.2.7.10a¹²⁴ for CIRCexplorer2 and PTESfinder v2, bowtie v2.3.4¹²⁵ for PTESfinder v2, and BWA v0.7.17¹²⁶ for CIRI2. All BSJs were annotated using the Ensembl v101 GRCh38 reference.

Similar to others⁵⁶, we limited false positives by retaining BSJs with a read count > 1 in at least two individuals. In addition, BSJs were only retained for downstream analysis if they were detected by at least two tools (SFigure 2). This reduced our initial set of BSJs (PPMI = 438,189 to 23,454, or 5.35%; ICICLE-PD = 222,133 to 15,345, or 6.91%). These BSJs, alongside the corresponding forward-spliced junctions (FSJs, Fig. 1), were quantified with CIRIquant v1.1.2¹²⁷ using HISAT v2.2.0¹¹⁷ as the aligner.

Sample level quality control

Sample level quality control was assessed in both cohorts to identify sample failure, RNA contamination and abnormal global transcriptome issues. Fastq Screen v0.14.1¹²⁸ and FastQC v0.11.7 (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) were used to assess contamination and obtain general sequencing metrics. Alignment metrics were obtained using CollectRnaSeqMetrics from Picard v2.27.5 (http://broadinstitute.github.io/picard/) and stats from SAMtools v1.6¹²⁹. Additionally, PPMI samples that had been previously flagged due to QC issues were removed²⁷. Validation of each participant’s clinically recorded sex was based on the normalised and variance-stabilising transformed¹³⁰ expression of the Y chromosomal genes RPS4Y1, KDM5D, DDX3Y and USP9Y. Principal component analysis was carried out and the first two principal components and was used to detect mismatches by visual inspection (SFigure 3). This identified one incorrectly coded individual in ICICLE-PD, which was corrected for analysis. After QC, the final PPMI dataset comprised 259 PD and 161 controls; the ICICLE-PD dataset comprised 48 PD patients and 48 controls (summarised in STable 1).

Identification of sources of variation in RNA expression data

Biological and technical factors are known to impact the quantification of gene expression^131,132. Like previous large-scale transcriptomic studies, we quantified sources of expression variation at both the sample and RNA level^27,133,134. At the sample level, we used univariate linear regression to identify technical sequencing metrics (obtained from Picard and SAMtools) that explained a high proportion of the variance (R² > 0.5) associated with the first 10 principal components of gene and circRNA expression (SFigures 4a-b & 5a-b). At the gene level, based on recommendations¹³⁵, we excluded highly correlated factors (Spearman’s ρ > 0.9, SFigures 4c & 5c), subsequently quantifying the contribution of this reduced set of covariates to gene and circRNA expression variation using variancePartition v1.28.3 (SFigure 6). This final set of cohort-specific covariates were then included in regression modelling (see Differential expression).

Differential expression

Normalisation and differential expression analysis of genes and BSJs was carried out using DESeq2 v1.38.2¹³⁶. Gene and BSJ raw counts were filtered for low counts (> 10 counts in the smallest sample group). Gene counts were library normalised using the default median-of-ratios method in DESeq2 (Anders and Huber, 2010). Junction counts (both BSJs and FSJs) were normalised using the sample size factors generated during the normalisation of gene counts.

Differential expression between PD and controls was assessed using a Wald test in DESeq2, adjusted for specific technical and biological covariates relevant to each cohort and RNA type (see Identification of sources of variation in gene expression data). Across all cohorts and RNA types, we adjusted for sex, age of collection and sequencing batch. For gene differential expression, we adjusted for the percentage of usable bases (PPMI), in addition to the percentage of coding and intronic bases (ICICLE-PD). For circRNA differential expression, we adjusted for the percentage of intronic bases (PPMI and ICICLE-PD), in addition to the median Coefficient of Variance of transcript coverage (ICICLE-PD). P-values were adjusted for multiple significance by the Benjamini-Hochberg procedure at a false discovery rate (FDR) of 5%¹³⁷. Similar to previous work²⁷, significantly differentially expressed genes and BSJs were defined as those that had an FDR < 0.05 and a log₂ fold change (log₂FC) <-0.1/>0.1.

Ontology and pathway analysis

Enrichment of gene sets was performed using clusterProfiler v4.6.2¹³⁸. Gene set enrichment analysis (GSEA)⁶⁵ was based on gene lists ranked by fold change. For each cohort, all genes included in differential expression testing were used as the background set. Gene sets were obtained as Gene Ontologies¹³⁹ (including biological processes, molecular functions, and cellular compartments) and KEGG pathways¹⁴⁰ using the gseGO and gseKEGG functions, respectively.

GSEA is not suitable for circRNA enrichment at the junction level as each gene can host multiple circRNAs. As such, we carried out circRNA enrichment using an over-representation test of the host genes using the enrichGO function from clusterProfiler. To detect groups enriched in common BSJs, those included in differential expression testing were compared against all abundant BSJs detected in that cohort (> 10 reads in the smallest sample group). To detect categories enriched in differentially expressed BSJs, those that were significant were compared to all BSJs included in differential expression testing. For all enrichment analyses, where possible, categories that passed the multiple testing threshold in the PPMI (FDR < 0.05) were validated in the ICICLE-PD cohort.

Relating RNA expression to previous work

Genes reported to be differentially expressed in PD were taken from previously published blood RNA sequencing studies^67–70, resulting in a total of 354 genes. GWAS loci associated with PD risk were obtained from the current largest GWAS meta-analysis¹⁰. Expression of the nearest genes to these loci was then compared with 70 detected in PPMI and 69 detected in ICICLE-PD. Genes that host established pathogenic parkinsonism variants were obtained from the Genomics England Parkinson Disease and Complex Parkinsonism panel v1.111 (accessed 10/22)¹⁴¹. Out of 35 genes, 30 were detected at sufficient expression levels in the PPMI and ICICLE-PD cohorts.

Classification of Parkinson’s disease status using RNA expression

The following features were used for classification: normalised counts (gene, BSJ and FSJ) counts after Variance Stabilising Transformation as implemented in DESeq2¹³⁰. Circular to linear (BSJ:FSJ) ratios were extracted from CIRIquant¹²⁷. We also explored the use of circRNA differential expression imbalance as a classification feature. The principle was to compare the expression value of each BSJ from a sample we want to classify with the average expression value in controls, subsequently assessing whether the directional deviation from the mean (i.e., over- or under-expression) was consistent with the direction observed in the training group (PPMI). Each BSJ was scored as 1 when the directions agreed or as 0 when the directions disagreed. BSJ scores were summed per sample and the ability of this sum to discriminate between cases and controls was assessed.

For gene predictors, we selected those that were nominally significantly differentially expressed (P-value < 0.05). For BSJ, FSJ, BSJ:FSJ ratio and BSJ imbalance models, we used all highly expressed junctions. We investigated the following sets of features: Expression levels by gene (Gene), BSJ counts (BSJ), circular to linear ratios (BSJ:FSJ ratio), circular RNA imbalance (BSJ imbalance), expression levels by gene and BSJ counts (Gene and BSJ). With the exception of the BSJ imbalance (see above), the ability of the selected sets of features to classify cases and controls was assessed using regularised logistic regression as implemented in glmnet v4.1.6¹⁴² and quantified using the area under the receiver operating characteristic curve (AUC). AUCs and their 95% confidence intervals (Delong's method) were calculated using pROC v1.18.0¹⁴³. Classifiers were derived from the PPMI cohort, using the nestcv.glmnet function from nestedcv v0.4.4¹⁴⁴, and 10 fold cross-validation for both inner and outer cross-validation steps. Final model parameters were derived from a cross-validation fold fit on PPMI data. Model generalisability was assessed in the ICICLE-PD cohort.

Statistical analyses

All statistical analyses were carried out in R v4.2.1. Correlations were assessed by Spearman’s rank correlation (cor.test), reported in the text as Spearman’s rho (ρ). We use the term imbalance to describe whether there is an excess of features (genes or junctions depending on the context) that are overexpressed in controls compared to PD. It describes the number of features showing log₂ fold change <-0.1 divided by the number of transcripts with log₂ fold change > 0.1 or <-0.1. Significance was assessed using a two-sided exact binomial test (binom.test). Group differences were assessed using a Wilcoxon rank-sum test (wilcox.test). Where appropriate, multiple testing correction was performed using the Benjamini-Hochberg procedure (p.adjust).

Ethical Approval and Consent to participate

The local ethics committee at each centre approved each study (PPMI, https://www.ppmi-info.org/ and ICICLE-PD, Newcastle and North Tyneside Research Ethics Committee). All participants signed written informed consent. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this work is consistent with those guidelines.

Consent for publication

Not applicable

Data availability

PPMI raw RNA sequencing and corresponding clinical data are available from https://www.ppmi-info.org/. ICICLE-PD raw RNA sequence data is freely available upon request. 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.

Funding

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) and Parkinson’s UK (G-2003 and G-2201). ICICLE-PD was funded by Parkinson’s UK (J-0802, G-1301, G-1507) and supported by the Lockhart Parkinson’s Disease Research Fund, National Institute for Health Research (NIHR) Newcastle Biomedical Research Unit and Centre based at Newcastle upon Tyne Hospitals NHS Foundation Trust and Newcastle University. RAL is supported by a Janet Owens Parkinson’s UK Senior Research Fellowship (F-1801).

Author Contributions

MSJ, MSK and GH designed the study. BJW, OGI, DD and AP performed data generation and analysis. AP and HL prepared blood samples for RNA sequencing. JC performed the RNA sequencing. RAL and AJY provided clinical and patient samples (ICICLE-PD) and supported data interpretation. BJW, MSK and GH conducted statistical analysis. BJW, MSK and GH wrote the manuscript with feedback and input from all authors. All authors read and approved the final manuscript.

Acknowledgements

The authors would like to express their deepest gratitude to the Parkinson’s Progression Markers Initiative and The Incidence of Cognitive Impairment in Cohorts with Longitudinal Evaluation-PD study team members as well as to the patients and staff at each study. The authors wish to thank the Newcastle Genomics Core Facility for their assistance in experimental design and data generation. This research made use of the Rocket High-Performance Computing service at Newcastle University. In addition to the listed authors, we thank the following members of the ICICLE-PD Study Group who all made a significant contribution to the work reported in this paper: Patrick F Chinnery (Department of Clinical Neurosciences, University of Cambridge, UK, Principal Investigator); John T O’Brien (Department of Psychiatry, University of Cambridge, UK, Principal Investigator); Trevor W Robbins (Department of Psychology, University of Cambridge, UK, Principal Investigator); Caroline Williams Grey, Marta Camacho Gemma A Cummins, Jonathan Evans, Ruwani Wijeyekoon, Kirsten Scott, Tom Stoker, Julia Greenland, Natalie Valle Guzman, Lucy Collins, Simon Stott and Sarah Mason (John van Geest Centre for Brain Repair, University of Cambridge, UK, Site Investigators); David J Brooks (Translational and Clinical Research Institute, Newcastle University, UK, Principal Investigator); and James B Rowe (Behavioural and Clinical Neuroscience Institute, UK, Site Investigator).

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Parkinson’s disease is associated with an imbalance in circular RNA expression

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Journal Publication

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Abstract

Figures

Background

Results

Differentially expressed genes in PD

Circular RNAs are differentially expressed in PD

PD is characterised by an imbalance in circRNA abundance

Evaluating RNA expression as a predictor of early-stage idiopathic PD

Discussion

Materials and Methods

Cohorts

ICICLE-PD RNA isolation and sequencing

PPMI RNA isolation and sequencing

RNA alignment and quantification

Circular RNA detection and quantification

Sample level quality control

Identification of sources of variation in RNA expression data

Differential expression

Ontology and pathway analysis

Relating RNA expression to previous work

Classification of Parkinson’s disease status using RNA expression

Statistical analyses

Declarations

Ethical Approval and Consent to participate

Consent for publication

Data availability

Code availability

Competing Interests

Funding

Author Contributions

Acknowledgements

References

Supplementary Tables

Additional Declarations

Supplementary Files

Status:

Journal Publication

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