The submitted work is important because it describes Oxford Nanopore sequenced long-read transcriptomic changes in Parkinson’s disease (PD) blood. The work advances the field by identifying novel transcripts related to PD. Our research highlights several new transcripts and splicing changes in transcript isoforms that could not be detected with short reads. This study demonstrates that long-read sequencing can uncover significant differences that other technologies cannot.

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterised clinically by bradykinesia, rigidity, tremor, and postural instability [1]. Pathologically, it involves the loss of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and the buildup of α-synuclein–rich Lewy bodies [1, 2]. Over the past 2 decades, advances in human genetics - supported by genome-wide association studies (GWAS), research on monogenic families, and high-resolution sequencing - have revealed that PD is a complex condition with multiple distinct yet interconnected biological pathways [3, 4]. While fewer than 10% of patients have pathogenic monogenic variants, research on genes such as SNCA, LRRK2, VPS13C, PARK2, PINK1, PARK7, GBA1, and other risk loci has identified key mechanisms, including α-synuclein proteostasis, mitochondrial quality control, lysosomal and autophagic dysfunction, vesicular trafficking, and immune and inflammatory responses [3–5].

At the same time, the functional genomic understanding of these genetic networks has been somewhat delayed, and gene expression analysis in PD patients has not been very intensive. The complex interactions involved suggest that the underlying genomic mechanisms are likely multifactorial and intricate [6–10]. Multiple studies have used transcriptomic analyses to identify PD signatures, revealing distinct transcriptional patterns or splicing events in genes expressed in the basal ganglia [11, 12]. Due to the challenges of obtaining brain tissue from patients, there is a growing need for peripheral biomarkers. Analyses of transcriptomes from accessible tissues such as skin, keratinocytes, and blood, through the lens of CNS expression data from PD patients, have revealed notable differences between PD and control groups [7, 8, 13]. These findings underscore the importance of studying peripheral tissues not only as a means to find biomarkers for disease prediction but also to deepen our understanding of neurodegeneration.

Peripheral tissues offer a rich source of biomarkers for chronic neurological conditions, and most commonly, blood can be used for this purpose. Indeed, blood has been shown to provide a great source for RNA and transcriptomic profiling for clinical use, with a very good overlap with protein analysis, even in neurological conditions [13–15]. Serum amyloid A is a good example of a potential biomarker that shows changes in RNA and protein levels in PD patients. Many previous studies have performed whole-transcriptome analysis of blood from PD patients [16–22]. Interestingly, SNCA expression in the blood of PD patients is lower than in healthy controls [16]. In addition, the changes in the PD genes (e.g., GBA1, LRRK2) have been inconclusive. All these studies have been based on Illumina short reads, which have limitations in analysing transcriptome complexity. While the short reads are excellent to capture aggregated gene-based transcription signals, they are challenging to call individual transcripts of specific genes. It is possible to use short reads for more specific analytical workflows and to measure exonic or intronic reads, as well as long non-coding RNAs [21–23].

Gene expression analysis can target a single gene, a specific gene group, or all genes in the genome, serving as a valuable tool for biomarker or diagnostic development. This information is also essential for understanding the underlying pathological processes. Whole transcriptome profiling, also known as genome-wide gene expression analysis, provides the most comprehensive view of a biological system or its response to stimuli, making it invaluable for complex analyses. It can be performed in different forms. This method can be applied to various biosamples and environmental samples through meta-transcriptome analysis. It is crucial to understand the changes in RNA expression that arise directly from a disease-causing mutation or from the interaction of the mutated and dysfunctional gene product. Additionally, RNA profiling is particularly useful for characterising molecular changes associated with disease states, aiding in the identification of potential RNA-based biomarkers.

In the present study, we employed Oxford Nanopore long-read sequencing of blood RNA from patients with PD and healthy controls (HC). We used a whole-transcriptome protocol that captures both poly-A and non-poly-A RNA, providing a comprehensive profile of variable RNA isoforms. This method allowed us to accurately characterise differential expression at the levels of genes, transcripts, and transcript usage by gene. We also compared our findings with existing PD transcriptome studies, like the Parkinson’s Progression Markers Initiative (PPMI), to gain a broader understanding of the blood transcriptome in PD patients.

The present study employed Nanopore long-read sequencing to characterise whole-transcriptome changes in the blood of PD patients. This approach enables the accurate identification of full-length transcript isoforms without computational imputation and gives much clearer resolution of isoform diversity within whole transcriptomes, which often remains undetected or is only partially identified. It also enables the detection of transcript-level dynamics or differential transcript usage (DTU) associated with gene activation [30, 41]. Transcript isoform-level dynamics is especially important for neurodegenerative disorders, where the isoform diversity has already been suggested to drive the pathology [42, 43].

Previous studies have used short-read sequencing with Illumina or SOLiD technologies [7, 8, 16–19]. Although these technologies differ significantly, their outcomes show notable overlaps, with similar genes being upregulated or downregulated across studies. Typically, genes involved in the heat shock response, the ubiquitin-proteasome pathway, or the immune response are impacted [8]. Remarkably, SNCA has consistently been downregulated in PD patients in most transcriptomic analyses [18].

Different attempts have been made in the past to uncover the complexity of transcriptomic regulation in PD patients. One approach is to use short read data to analyse exonic and intronic transcription, which can provide some insight into isoform-level activation and RNA isoform switching [21, 22]. The intronic activation was evident when PD patients were compared to the controls, and also throughout the disease course, as thousands of introns were differentially expressed after 3 years of disease progression [21]. These previous studies clearly warrant deeper analysis of the PD transcriptome using nanopore technology, enabling more precise isoform detection.

Indeed, in the present study, we identified not only a large list of differentially expressed genes but also a huge number of differentially expressed transcripts between PD and controls. We identified 18,467 differentially expressed genes and 46,180 differentially expressed transcripts between the PD and HC groups (Table 3; Supplementary Table 5), including many novel transcripts and those that have never been linked to PD pathology. One of the most remarkable transcripts is the BCL2L2-PABPN1 readthrough, which combines two adjacent genes, BCL2L2 and PABPN1 and results in a fusion protein. While fusion proteins are more typical of cancer syndromes, the BCL2L2-PABPN1 has been found to be associated with obesity and metabolic rate [44]. The BCL2L2-PABPN1 gene was also differentially expressed in the gene-based analysis.

Another notable finding is the highly significant differential expression of the SQSTM1 transcripts. SQSTM1 encodes p62, a multifunctional adaptor protein that plays a central role in autophagy, proteostasis, cell signalling, inflammation, and bone metabolism. p62 acts as a scaffold protein that binds ubiquitinated cargo and delivers it to the autophagosome via its LC3-interacting region (LIR), making it a key receptor in selective autophagy pathways. SQSTM1 is involved in many neurodegenerative diseases, such as motor neurone disease (MND), Alzheimer’s disease, and Parkinson’s disease. Similarly, we observed differential expression of transcripts for the PABPC1 (poly(A) binding protein cytoplasmic 1) gene, which is involved in the pathogenesis of MND. Changes in PABPC1 localisation and expression have been linked to amyotrophic lateral sclerosis (ALS) through interactions with TDP-43 and FUS and stress granule dysfunction disorders. Its function links translational dysregulation to neuronal vulnerability [45, 46].

We also observed differential expression of VPS13C (Vacuolar Protein Sorting 13 Homolog C) at both gene and transcript levels. Interestingly, VPS13C was also identified in the previously mentioned study through intronic analysis [21]. VPS13C is essential for maintaining lipid balance, supporting lysosomal and mitochondrial functions, and managing cellular stress responses [47]. Mutations or disruptions in VPS13C are strongly linked to early-onset Parkinson’s disease, with additional connections to metabolic, inflammatory, and neurodegenerative disorders [48].

Based on the NCBI Gene database, we compiled a list of 849 genes associated with PD. This list defines PD-genes for our study, and although this definition is somewhat broad, it is inclusive and helps capture signals that might otherwise be missed in the analysis of our transcriptomic profile. We found that 393 genes with clear PD connections were differentially expressed.

One of such genes was AAK1 (Adaptor-Associated Kinase 1). AAK1 is linked to PD because it affects endocytosis and protein aggregation, both of which are crucial in the disease’s progression [49–51].

Furthermore, we compared this list of DEGs with published results for the PPMI DEGs and found that 145 genes were identically altered in the blood of PD patients. Some examples of the overlapping genes between the PPMI and our study include SNCA, DNAJB6, PINK1-AS, SELENOT, GRN, PARK7, and CHI3L1. More specifically, SNCA was downregulated in patients with PD, a finding reported by others.

SELENOT (Selenoprotein T) was upregulated in both PPMI and our study, making it an interesting gene for PD. SELENOT is a highly conserved selenoprotein containing selenocysteine, which allows for strong redox regulation and protection against oxidative stress [52]. It is primarily located in the endoplasmic reticulum (ER), where it plays vital roles in protein folding, ER homeostasis, calcium regulation, and cellular stress responses. Essential for cell survival and development, knockout models show embryonic lethality, highlighting its critical physiological function [53, 54]. SELENOT supports antioxidant defences, neuroendocrine regulation, and protection from ER stress, and has been linked to metabolic diseases, neurodegeneration, and inflammation. Its thioredoxin-like redox motif drives its molecular activity, positioning it as a key regulator of cellular balance. The strong ER-protective and antioxidant properties of SELENOT are especially relevant in conditions involving oxidative stress and protein misfolding, such as Parkinson’s, Alzheimer’s, and ALS/MND. Lower levels of SELENOT increase neuronal susceptibility to ER stress, while experimental activation reduces apoptosis and promotes neuronal survival. Additionally, its role in calcium homeostasis helps maintain synaptic integrity and provides neuroprotection [55].

GRN (granulin precursor) was also upregulated in PPMI and our study. GRN encodes progranulin, a secreted, multifunctional growth factor that supports cell survival, lysosomal activity, inflammation regulation, and tissue repair. Progranulin can be split into smaller granulin peptides, each with unique biological functions. It is highly expressed in the brain, immune system, and epithelial tissues, where it aids in maintaining neurons, activating microglia, healing wounds, and managing protein stability [56]. Haploinsufficiency of GRN is a major factor in frontotemporal dementia (FTD), leading to a gradual loss of progranulin and consequent lysosomal issues. Changes in GRN activity are also associated with neuroinflammation, Parkinson’s disease, Alzheimer’s disease, autoimmune disorders, and various cancers [56]. Due to its dual role in inflammation and growth factor signalling, GRN is a critical gene in neurodegeneration and immune regulation.

PARK7 was downregulated in both studies, and this is a well-known PD-related gene.

LRRK2 and GBA1 were not differentially expressed in gene-based analysis, but they became statistically significant when we analysed specific transcripts. Indeed, both genes showed significant transcript switching in PD patients compared to controls. This indicates the importance of analysing the transcriptome at the transcript level, in addition to traditional gene-level analysis. Interestingly, LRRK1 and GBA2 were also differentially expressed in the gene-based analysis. LRRK1 is a paralog of LRRK2, and while there have been no LRRK1 mutations linked to PD, the functional similarity between LRRK1 and LRRK2 suggests that LRRK1 might have some impact on the PD pathology. GBA2 (Glucosylceramidase 2) encodes a non-lysosomal β-glucosidase that helps break down glucosylceramide on the cytoplasmic side of the endoplasmic reticulum and Golgi apparatus. While GBA1 is the main gene associated with PD (due to lysosomal glucocerebrosidase deficiency), increasing evidence suggests that GBA2 also impacts sphingolipid levels and might influence PD risk and its underlying mechanisms.

We would also like to comment on our study design. PD is closely associated with ageing and shows known sex differences in immune and gene expression profiles, which can confound blood-based transcriptomic studies. To minimise the impact of these demographic factors, we designed a case–control study that matched participants by age- and sex-specific proportions. This approach aimed to prevent demographic confounding early on, reducing the need for extensive post hoc adjustments. This is particularly important for long-read Oxford Nanopore RNA sequencing, where age-related changes in transcript structures, isoform usage, and immune gene expression can be complex and not fully captured by statistical covariates. Using a matched cohort allows downstream analysis to focus on disease-specific transcriptional differences under similar demographic conditions. We acknowledge that this matching may reduce the effective sample size and limit generalisability, but it was chosen to improve internal validity and clarity in this exploratory transcriptomic study. When necessary, additional analyses, including age and sex as covariates, were performed to confirm the robustness of the results.

The main limitations of our study are the cross-sectional design and the absence of data on environmental factors. A cross-sectional approach was unavoidable due to the sampling process. Simultaneously, accurately collecting environmental data is quite challenging. For future studies, one environmental factor to consider is the season when blood samples are collected. The response to medication and quality of life in Parkinson’s patients are also affected by the seasons [57]. Although the exact mechanism remains unclear, seasonal fluctuations have been shown to influence behaviour and the endocrine system even in experimental settings [58, 59]. Additionally, the availability of the dopamine transporter depends on sunlight exposure, as shown in the PPMI cohort [60]. Likewise, several studies have demonstrated the link between seasons and sleep disturbances [61, 62].

On the other hand, our sample size is large enough to provide sufficient power for a thorough analysis. The high statistical power is reflected in the extensive list of differentially expressed genes and transcripts identified. However, for our future studies, more stratified analyses would be helpful for separating patients by genetic mutations or disease progression.