Differential tear fluid miRNAs in patients with Parkinson’s disease and atypical Parkinsonian syndromes

Differential tear fluid miRNAs in patients with Parkinson’s disease and atypical Parkinsonian syndromes | 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 Research Article Differential tear fluid miRNAs in patients with Parkinson’s disease and atypical Parkinsonian syndromes Antonia F. Demleitner, Lucas Caldi Gomes, Lara Wenz, Laura Tzeplaeff, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6512717/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Aug, 2025 Read the published version in Molecular Neurobiology → Version 1 posted 9 You are reading this latest preprint version Abstract Parkinson’s disease (PD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP), are neurodegenerative disorders diagnosed by clinical criteria with limited diagnostic specificity in early stages. Diagnostic biomarkers facilitating early and precise diagnosis are needed. Tear fluid (TF) is an easily accessible body fluid reflecting pathophysiological changes in ocular and systemic diseases. This study explores TF as a non-invasive source of disease-specific miRNAs for PD, MSA, and PSP. We demonstrate reduced TF production in PD patients. Using a real-time quantitative PCR-based array targeting 1113 miRNAs, we identified 55 miRNAs exclusively expressed in PD, 35 miRNAs in PSP, and 14 in MSA, respectively. Several of these have previously been identified in other biofluids. Overrepresentation analysis of target genes showed apoptotic and cell differentiation pathways as common targets. These findings suggest that miRNA alterations in TF might reflect disease mechanisms in PD and atypical Parkinsonian syndromes, warranting further exploration as potential biomarkers. tear fluid miRNA biomarker Parkinson’s syndrome Progressive supranuclear palsy Multiple system atrophy Figures Figure 1 Figure 2 Figure 3 Introduction Parkinson’s disease (PD) and atypical Parkinsonian syndromes (aPS), including multiple system atrophy (MSA) and progressive supranuclear palsy (PSP), but also corticobasal degeneration (CBD) and dementia with Lewy bodies (DLB), are neurodegenerative disorders characterized by the progressive loss of motor and cognitive functions. While specific diagnostic criteria relying on the clinical history, physical examination as well as the patient’s response to levodopa, have been developed for all mentioned diseases [ 1 – 3 ], an overlap of symptoms is common, especially in early stages of the disease. Furthermore, definite diagnosis is only possible postmortem through neuropathological examination of brain tissue. Therefore, the current diagnostic approaches remain challenging and frequently result in delays and misdiagnosis [ 4 ]. This emphasizes the critical need for reliable biomarkers to facilitate early detection and more targeted interventions, ultimately enhancing disease management and patient outcomes. MicroRNAs (miRNA) are small, non-coding RNAs of about 22 nucleotides length. They can regulate the post-transcriptional gene-expression through binding with the 3’UTR of its target mRNA leading to cleavage or translational repression [ 5 ]. While one miRNA regulates the expression of multiple genes, a single gene can be regulated by multiple miRNAs. Therefore, the impact of miRNAs on a given biological process is complex. In the central nervous system (CNS), miRNAs regulate key processes such as neurite outgrowth, dendritic development, neuronal differentiation and synaptic plasticity [ 6 ]. In PD, miRNAs have been shown to play a significant role in key pathomechanisms like mitochondrial dysfunction, protein aggregation, oxidative stress, and neuroinflammation [ 7 ]. Although typically restricted to tissue, some miRNAs are released in extracellular biofluids [ 8 ]. There, their composition and levels have been shown to reflect different disease states. Growing evidence suggests that miRNA in cerebrospinal fluid (CSF) can help distinguish PD from healthy controls [ 9 , 10 ]. Limited data is available regarding expression patterns in the different aPS [ 11 , 12 ]. While most data on miRNAs in biofluids stems from either blood or CSF, miRNAs have also been detected in other biofluids such as saliva and tear fluid (TF) [ 8 ]. Particularly, TF has recently gained attention as a potential source of biomarkers for neurological diseases [ 13 – 17 ]. Although TF is an ultrafiltrate from blood, the lacrimal gland, through the innervating parasympathetic nerves, receives input from regions in the brain stem – areas commonly affected in most neurodegenerative disorders. Interestingly, reduced TF production has been demonstrated in a range of neurodegenerative diseases, supporting the widespread involvement of the lacrimal system [ 18 ]. TF could therefore serve as a valuable, non-invasive and easily accessible bioliquid for early detection and monitoring of PD and other neurodegenerative diseases. Indeed, several studies report changes in established biomarkers of neurodegenerative diseases such as PD, Alzheimer’s dementia (AD), Creutzfeldt-Jakob-Disease and Huntington’s disease [ 19 , 20 , 14 , 15 , 21 ]. Data on miRNAs in the TF of patients with neurodegenerative diseases is limited. Changes in the expression of miRNAs associated with amyloid beta production and inflammation in TF of transgenic mice mimicking AD have been linked to concomitant neurodegeneration [ 22 ]. Kenny et al. have demonstrated high concentrations of miRNA in TF with a significant difference in total miRNA levels between AD and healthy controls. Moreover, specific miRNAs have been identified to serve as potential biomarkers [ 16 ]. However, the role of miRNA in TF of PD and aPS remains unexplored. This study aimed to investigate the expression patterns of miRNA in TF of PD, MSA and PSP patients in comparison to healthy controls to gain insight into miRNAs as possible biomarkers in the diagnosis of Parkinsonian syndromes. For this, we performed an RT-qPCR-based analysis of the miRNAome of PD, MSA and PSP as well as control TF samples using pooled cDNA samples (Fig. 1 a). Our results show distinct differences between groups. Results TF was collected using Schirmer test strips from 56 patients. 10 of these were healthy control patients without evidence of neurodegenerative disease, 29 were patients with probable or clinically established PD (mean Hoehn and Yahr stage 2.2 +/- 1). In addition, we included 7 patients with either possible or probable MSA, of which 4 were of the cerebellar and 3 of the parkinsonian subtype, and 10 patients with either possible or probable PSP were included. All patients were well matched regarding age, sex, concomitant eye diseases or medication (Table 1 ). Disease duration, however, was significantly shorter in both MSA and PSP, compared to PD (mean 8.3 +/- 0.7 years in PD, 1.9 +/- 0.9 years in MSA and 2.7 +/- 1.6 years in PSP, p ANOVA = 0.005, p Posthoc−PSP = 0.02, p Posthoc−MSA = 0.03). Importantly, wetting length (WL) of the Schirmer test strips was significantly shorter in the PD but not the PSP and MSA group compared to the control group when correcting for age in a multiple linear regression model (37 +/- 22 mm/ 5 min in control, 21 +/- 17 mm/ 5 min in PD, 20 +/- 21 mm/ 5 min in MSA, 18 +/- 9 mm/ 5 min in PSP) (p = 0.02, estimate − 10.73, 95% confidence interval [-19.39, -2.07]). Table 1 Characteristics of the study population. Continuous data are presented as either median (minimum - maximum) or mean (+/- standard deviation). Categorical data are presented as absolute numbers (percentages). PD = Parkinson’s disease, MSA = multiple system atrophy, PSP = progressive supranuclear palsy. A Fisher’s Exact test, B One-way ANOVA with Tukey post-hoc testing, C multiple linear regression correcting for age. Controls PD MSA PSP p Patients N 10 29 7 10 Sex (female) N (%) 3 (30%) 5 (17%) 2 (29%) 6 (60%) 0.89 A Age (y) Mean +/- SD 74 +/- 8.2 68 +/- 9.1 67 +/- 13 72 +/- 5.8 0.20 B Median (min - max) 74 (57–86) 68 (53–82) 64 (45–85) 72 (64–79) Clinical data Disease duration (y) Mean +/- SD NA 8.3 +/- 0.7 1.9 +/- 0.9 2.7 +/- 1.6 0.005 B Median (min - max) NA 6.4 (0.1–23) 1.5 (0.9–3.1) 3.0 (0.3–5) Hoehn & Yahr Stage Mean +/- SD NA 2.2 +/- 1 Ophthalmic data Eye disease (yes) N (%) 3 (30%) 8 (28%) 1 (14%) 2 (20%) 0.91 A Eye medication (yes) N (%) 4 (40%) 7 (24%) 1 (14%) 1 (10%) 0.49 A Wetting length (mm/ 5 min) Mean +/- SD 37 +/- 22 21 +/- 17 20 +/- 21 18 +/- 9 0.01 C Median (min - max) 44 (10–65) 12 (3–70) 11 (5–62) 18 (3–35) miRNA concentration (ng/µl) Mean +/- SD 22 +/- 15 13 +/- 10 12 +/- 13 17 +/- 11 0.52 A Median (min - max) 19 (2.1–46) 6.7 (3.5–38) 3.5 (1.3–31) 14 (4.8–42) miRNA concentration correlates with wetting length We next isolated miRNA from the TF eluate and quantified its concentration. No significant difference between the groups was observed (Table 1 ). Correlating the miRNA concentration to clinical parameters, a significant and strong correlation with WL was observed in the overall cohort (R P = 0.68, p < 0.001, 95% CI [0.50, 0.80]) (Fig. 1 b). This effect was persistently observed in each of the individual patient groups, as well (control R P = 0.68, p = 0.029, 95% CI [0.10, 0.92], PD R P = 0.58, p < 0.001, 95% CI [0.27, 0.78], MSA R P = 0.91, p = 0.004, 95% CI [0.50, 0.99], PSP R P = 0.74, p = 0.013, 95% CI [0.22, 0.94]) (Fig. 1 c). Age at sampling and miRNA concentration did not show any significant correlation in the overall cohort (Fig. 1 d). However, a strong negative correlation was found in the control group (R = -0.76, p = 0.011, 95% CI [-0.94, -0.24]), whereas no trend was observed for PD, MSA, and PSP (Fig. 1 e). Lastly, we evaluated the relationship of disease duration and miRNA concentration. Combining all disease groups, PD, MSA, and PSP, no significant correlation was observed. Exploring the effect in the subgroups, no correlation was seen for PD and PSP whereas a strong positive correlation was seen for MSA (R = 0.84, p = 0.038, 95% CI [0.08, 0.98]) (Supp. Fig. S1 ). PCR-profiling of miRNAs in the tear fluid of patients with PD and aPD After isolation of miRNAs, we quantified the miRNA levels of the pooled control, PD, MSA and PSP samples using an RT-qPCR-based miRNA profiling kit. Of all 1113 quantifiable miRNAs, 286 were found in all groups, whereas 244 miRNAs were not found in any group. Unsupervised hierarchical clustering of the expression status of all quantified miRNAs revealed a clustering of the PD and PSP groups, as well as the MSA and control groups (Fig. 2 a). Next, we aimed to identify miRNAs that were uniquely identified in each of the disease groups. For this, all miRNAs exclusively amplified with high certainty in the respective groups or exclusively amplified in all other groups, but not the disease group itself, were analysed using an UpSetR plot (Fig. 2 b). 55 miRNAs were exclusively amplified with high certainty in the PD group, whereas 4 miRNAs were exclusively amplified with high certainty in all groups but the PD group. In the MSA group, 14 miRNAs were exclusively amplified with high certainty and 41 miRNAs were exclusively amplified with high certainty in all other groups except for the MSA group. 35 miRNAs were exclusively amplified with high certainty in the PSP group, whereas 27 miRNAs were exclusively amplified with high certainty in all groups but the PSP group (a detailed listing of all miRNAs in the intersections is available in Supplementary Table S1 ). The annotated names from the profiling kit were converted to the current annotation in miRBase (v22) and only currently annotated miRNAs were used for further analysis. Presence of TF miRNA in other biofluids To understand whether the miRNAs identified have been previously described in other biomaterials or are potentially specific for tear fluid, we compared our findings to data obtained in other biofluids. A detailed listing of all literature used for this comparison can be found in Table 2 (for a detailed description of methodological aspects of the search see the Methods Section “Literature search”). While approximately one third of the miRNAs did not show any differential expression in the respective disease groups in other biofluids before, some miRNAs have been shown to be differentially regulated (Fig. 2 c). For the PD intersections, 56 of the 59 miRNAs identified as being either exclusively present or absent in PD with the profiling kit were annotated in the current version of miRBase (v22). Of those, 40 were not previously described as significantly altered in PD. 12 were previously described in blood, hsa-miR-542-5p was previously described in CSF, hsa-miR-516a-5p in brain tissue. Hsa-miR-95-3p and hsa-miR-374a-5p have been identified to be altered in CSF and brain tissue, while the latter was also described in blood. 53 of the 55 miRNAs found in the MSA intersections are currently annotated. Of these, 46 have not been shown to be altered in MSA before. Hsa-miR-130a-3p, hsa-miR-29c-3p, hsa-miR-92a-1-5p and hsa-miR-93-5p have been depicted differently expressed in blood, while hsa-miR-1203 and hsa-miR-1909-5p were discriminative in brain tissue. Hsa-miR-24-1-5p was shown to be altered in blood and CSF. Lastly, while we identified 62 miRNAs in the PSP intersects, only 60 of them were annotated in miRBase. Of these, 56 have not been described as altered in other biofluids. Hsa-miR-425-5p and hsa-miR-99b-5p were previously shown to be altered in blood. Hsa-miR-423-5p was identified to be altered in CSF and hsa-miR-132-3p in brain tissue. Taken together, most previously described miRNAs have been shown in blood, while only two and four have been identified in CSF and brain tissue, respectively. Only three miRNAs, two of them belonging to the group of miRNAs identified in PD, have been described in more than one biomaterial. The majority of miRNAs, namely 16, have been described in PD, while only seven and four have been identified in MSA and PSP, respectively. Table 2 MicroRNAs in the intersections of the cohort. Group miRNA Blood CSF Brain Tissue PD hsa-miR-106b-3p Xie 2022 PD hsa-miR-128-3p Ravanidis 2020, Braunger 2024 PD hsa-miR-193a-3p Dong 2016 PD hsa-miR-199a-5p Martins 2011, Li 2024 PD hsa-miR-410-3p Ravanidis 2020 PD hsa-miR-487b-3p Kern 2021 PD hsa-miR-505-3p Khoo 2012, Yao 2018 PD hsa-miR-193b-5p Baghi 2021 PD hsa-miR-103a-3p Schwienbacher 2017, Serafin 2015, Soto 2023 PD hsa-miR-454-5p Cardo 2013 PD hsa-miR-654-5p Cai 2021, Hou 2023 PD hsa-miR-671-5p Uwatoko 2019, Khoo 2012 PD hsa-miR-542-5p Mo 2016 PD hsa-miR-516a-6p Hoss 2016, Chatterjee 2017 PD hsa-miR-95-3p dos Santos 2018 Briggs 2015 PD hsa-miR-374a-5p Martins 2011, Tong 2022 Tong 2022 Briggs 2015 MSA hsa-miR-93-5p Pérez-Soriano 2020 MSA hsa-miR-92a-1-5p Kume 2017 MSA hsa-miR-29c-3p Vallelunga 2014 MSA hsa-miR-130a-3p Kume 2017 MSA hsa-miR-1203 Wakabayashi 2016 MSA hsa-miR-1909-5p Wakabayashi 2016 MSA hsa-miR-24-1-5p Vallelunga 2014, Kume 2017 Marques 2017 PSP hsa-miR-425-5p Manna 2021, Ramaswamy 2022 PSP hsa-miR-99b-5p Ramaswamy 2022 PSP hsa-miR-423-5p Nonaka 2022 PSP hsa-miR-132-3p Smith 2011 miRNAs that have been reported to be significantly changed between the disease groups and control in other biofluid studies sorted by group as indicated in the first column and by colour (blue = PD, orange = MSA, red = PSP). PD = Parkinson’s disease, MSA = multiple system atrophy, PSP = progressive supranuclear palsy, CSF = cerebrospinal fluid. Overrepresentation analysis of the unique intersects of miRNAs To better inform about the function of the miRNAs identified in TF, we performed an overrepresentation analysis (ORA) using the DIANA miRPath online tool (v4). miRNAs that were exclusively identified in the respective disease groups were analysed separately from those exclusively absent in one of the respective groups (Fig. 3 ). Negative enrichment ratio values were assigned to exclusively absent miRNAs and positive values to exclusively present miRNAs. Semantic similarity analysis (using REVIGO) was then performed to reduce redundancy and summarize the complete list of terms. After filtering, the GO term analysis for the PD intersections revealed only significantly enriched terms pointing to the mitogen-activated protein kinase (MAPK) pathway in the terms annotated to the molecular function (MF) category supported by the REVIGO analysis, which summarized the enriched terms under protein kinase activity (Fig. 3 a, Supplementary Fig. S2 , Supplementary Data S2). The MAPK pathway has been associated with cell proliferation, differentiation, and survival. Exploring the REVIGO analysis in detail, several terms belonging to in-utero embryonic development are listed in the biological process (BP) category, e.g. nervous system development, axon guidance and generation of neurons (Supplementary Fig. S2 , Supplementary Data S2). Additionally, several other terms are summarized under the terms regulation of cell population proliferation and apoptotic process (Supplementary Fig. S2 , Supplementary Data S2). For the miRNAs exclusively found in MSA, several apoptosis-related terms were enriched in the BP category, such as negative regulation of cell growth and negative regulation of extrinsic apoptotic signaling pathway (Fig. 3 f, h), which was also complemented by the REVIGO analysis (Supplementary Fig. S3, Supplementary Data S2). Similar to the analysis in the PD group, heart development was another overarching term in the semantic similarity analysis of these BP terms, which contained many terms belonging to developmental pathways such as nervous system development (Supplementary Fig. S3, Supplementary Data S2). Lastly, we analysed the annotated terms of the ORA of the PSP miRNA intersections. Here, looking into the MF terms, neurotrophin TrkA receptor binding - a term belonging to a family of terms encompassing many neurotrophic pathways associated with neuronal development -, was one of the most enriched ones among the MF terms enriched in the exclusively absent miRNAs (Fig. 3 i). Interestingly, some terms associated with immune system-related functions were among the top terms enriched in the exclusively present miRNAs, such as MHC class II protein complex binding in the MF terms and cellular response to interleukin-7 in the BP terms (Fig. 3 j). Semantic similarity analysis revealed apoptotic pathways summarized under the term of apoptotic process in the BP category of the exclusively present miRNAs (Supplementary Fig. S4, Supplementary Data S2). Interestingly, also microtubule-based process was among the terms enriched in this category (Supplementary Fig. S4, Supplementary Data S2). Discussion In this pilot study, we provide a first description of the miRNAome in the TF of a cohort of patients with PD and the atypical Parkinsonian syndromes MSA and PSP. The WL of the Schirmer test strips was significantly reduced in the PD group compared to controls and this finding was previously observed in other cohorts [ 20 , 18 ]. Predominantly postganglionic autonomic dysfunction is part of the pathology in PD [ 23 ]. While predominantly preganglionic autonomic dysfunction is a hallmark symptom in MSA [ 24 ], the impairment of the autonomic nervous system in PSP is less understood [ 25 ]. Thus, while WL did not differ significantly in the MSA and PSP groups compared to the control group, it is less apparent as to whether this might be due to the small sample size of these groups or other contributing factors. We show a strong positive correlation of miRNA concentration with WL across all disease groups, while no difference in concentration between the disease groups was observed. A positive correlation between protein concentration in TF and WL has been observed in several studies [ 20 , 26 ]. Interestingly, another study looking into the miRNA profiles of TF in patients with AD describes significantly increased miRNA concentration in AD while the WL was not significantly different from the control group [ 16 ]. Dysregulation of specific miRNAs has been shown in a wide range of pathological processes. Whether these effects stem from a particular group of miRNAs with major differences in expression or from a global effect in miRNA production is yet unexplored. We further explored the specific miRNAs expressed in the TF of our cohort. For this, we used an RT-qPCR-based approach and pooled samples to account for the relatively low number of TF samples. Normalization in TF samples is challenging, as commonly used normalization targets are unavailable. We therefore chose to categorize the data based on raw CT values into 3 groups: amplified with high certainty, amplified with low certainty, and not amplified. In total, our TF analyses revealed 286 miRNAs in all conditions, which is in line with previous findings exploring the total miRNAome in TF [ 27 , 8 ]. We searched literature to identify miRNAs already described in the conditions. In contrast to previous literature, we identified 40 miRNAs that have not yet been described in PD, and 46 and 56 miRNAs that are described for the first time in MSA and PSP, respectively. The fact that more miRNAs in the PD cohort have been previously described might result from more available studies concerning PD. The large number of miRNAs that have been previously reported from blood samples may also reflect a publication bias as most of the studies so far have looked into miRNAs in blood [ 10 , 28 ]. However, as TF is mainly an ultrafiltrate from blood and studies have shown similarities between the miRNAome of TF and plasma when comparing biofluids from the same individual [ 29 ], the comparably large overlap of differentially expressed miRNAs found in TF and blood could also result from the close connection between both biofluids. The second largest overlap was observed with miRNAs previously described in brain tissue. As the lacrimal gland receives parasympathetic innervation from the brain stem, these miRNAs could have entered the TF through neuronal vesicles via anterograde synaptic transport. Although the miRNAs we identified in TF are extracellular and likely do not exert their known regulatory functions on RNA, overrepresentation analysis for predicted target genes could shed light on disease mechanisms regulated by these miRNAs. Consequently, it is important to point out that these miRNAs are not directly serving as intercellular regulators but rather potential markers of these processes. Terms comprising functions in cell death and differentiation such as angiogenesis and in utero embryonic development are shared among the disease groups. They all encompass important cell cycle-related proteins that play a crucial role in the survival of mature neurons and neuronal apoptotic processes [ 30 , 31 ]. Cell death is an important hallmark of all neurodegenerative diseases, including PD, MSA, and PSP, and is closely linked to the activation of apoptotic processes [ 32 , 33 ]. Importantly, all these processes were identified in all studied disease groups, highlighting their importance for neurodegeneration in general. Some processes, however, were only associated with specific entities. For example, terms belonging to the MAPK pathway were enriched in the PD group. Although the MAPK pathway has also been implicated in cell death and apoptosis, evidence also suggests roles in axonal growth and guidance as well as oxidative stress in neurodegenerative diseases in general, including PD [ 34 ]. In the analysis of the PSP-associated miRNAs, neurotrophin signaling-related terms were highlighted. Neurotrophic factors have been linked to the survival of neurons in other neurodegenerative diseases [ 35 ]. Interestingly, the semantic similarity analysis identified microtubule-based process as an overarching term enriched in the miRNAs exclusively found in PSP. Notably, mutations in microtubule-associated protein tau (MAPT) are found in cases of familial PSP and aggregations of MAPT are a hallmark of the disease [ 36 ]. Further, microtubule defects have been shown in mesenchymal stromal cells of patients with PSP, suggesting the involvement of microtubule-associated processes in the disease pathology of patients with sporadic disease [ 37 ]. The low number of discriminative terms for the MSA groups might be attributable to the bigger heterogeneity, as well as the lower number of patients in this subgroup: more PD and PSP patients were diagnosed as clinically established according to the respective diagnostic criteria compared to the MSA group. Our study has clear limitations: Because of the relatively low number of samples and the low relative amount of miRNA from each patient, we had to pool individual samples. However, even though this approach did not permit the evaluation on the individual patient level, the PCR-based analysis of the pool was sufficient to identify miRNAs present or absent in each of the studied conditions. Sample pooling in a discovery approach, as in this study, is a valid option with respect to balancing budgetary constraints and low concentration of the studied target molecule, when the signal is close to or below the detection threshold [ 38 , 39 ]. Furthermore, we did not analyse absolute expression levels of the miRNAs but instead converted them into categorical data to circumvent normalization problems due to the lack of established normalization miRNAs. Subsequent studies with a larger numbers of subjects, as well as exploring the possibilities of enhancing sensitivity of the detection methods or the concentration of the samples will be informative for individual analysis and stratification of single patients. Taken together, our study shows differential expression of miRNAs in the TF patients with PD, MSA and PSP. It highlights the potential of TF as an easily accessible, non-invasive biomarker fluid suitable for longitudinal assessments. Further studies in bigger cohorts and individual samples are needed to confirm and expand on our findings. Materials and methods Study design and participants This retrospective, monocentric cohort compiles samples collected at the Department of Neurology of the TUM University Hospital rechts der Isar in Munich, Germany, from September 2019 to February 2021. TF was collected from 46 patients with either PD or atypical Parkinsonian disorders, namely MSA or PSP as well as control patients without signs of neurodegenerative disease. The detailed characteristics of the cohort are given in Table 1 . Patients were included if the disease was at least probable according to the respective Movement Disorder Society clinical diagnostic criteria [ 1 – 3 ]. No other inclusion or exclusion criteria regarding age, sex, disease duration, concomitant diseases or medication were applied. Written informed consent was obtained from all participants. The study complies with the Declaration of Helsinki and was approved by the Ethics Committee of the Technical University of Munich, School of Medicine (approval numbers: 9/15S, 2021-473-S-KH). Tear fluid sampling and sample preparation TF collection was performed as previously published [ 18 ]. In brief, we employed a standardized protocol of the Schirmer test using uncoloured filter strips (Madhu Instruments Pvt. Ltd., New Delhi, India). Strips were inserted in the lower fornix of each eye near the lateral canthus and left in place with eyes closed. No topical anaesthetic was used. After 5 min, the strips were carefully removed and the wetting length (WL) for both eyes was noted. The strips were individually packed in sample storage tubes and immediately frozen at -20°C and transferred to -80°C within one week for further analysis. Previous history regarding eye diseases, eye medications and the use of contact lenses was recorded. RNA Isolation For RNA isolation, TF was initially eluted from the strips. For this, strips were cut into small pieces and wet with 40 µl RNAse free water each. The tube containing the soaked pieces of the strips was placed in a bigger tube and a hole was punched in it. Samples were centrifuged at 16.000 G for 10 min. Next, RNA was isolated using an adapted TRIzol-based protocol. In brief, TRIZol was added, and samples were incubated at room temperature for 5 min. 1-Bromo-3-Chlor-Propane was added to the samples and the tubes were shaken for 20 s followed by incubation at room temperature for 3 min. Phase separation was achieved via centrifugation at 12.000 G at 4°C for 15 min. The aqueous phase was transferred to a new tube. For precipitation, Glycoblue (Invitrogen, Massachusetts, USA) and Isopropanol were added, and samples were subsequently incubated at -20°C overnight. Samples were then centrifuged at 12.000 G at 4°C for 30 min. The pellet was washed twice in 75% ethanol and thereafter dried to remove all ethanol. For solubilization, the pellet was resuspended in RNAse-free water and samples were shaken in an incubator at 55°C for 2 min to facilitate resuspension. miRNA concentration was quantified using the Qubit miRNA assay (Invitrogen, Massachusetts, USA) and purity confirmed by spectrophotometry. Isolation was carried out for each sample separately with pooled stripes (left and right). miRNA RT-qPCR screen The QuantiMir Kit for the human miRNAome (SBI System Biosciences, California, USA) was used for quantification of miRNAs. This kit uses polyA-tailed miRNA real-time quantitative PCR (RT-qPCR) of 1113 miRNAs with 3 internal controls. The analysis was carried out in technical duplicates on 5 pooled samples: one containing all control samples, two containing 19 and 10 - respectively - of the 29 total PD samples, as well as one containing all 10 PSP samples and one containing all 7 MSA samples. cDNA synthesis was carried out as indicated in the protocol on 500 ng of miRNA for each sample pool. For the RT-qPCR the Power SYBR Green PCR Master Mix from Applied Biosystems (Massachusetts, USA) was used for all samples. Melting curve analysis was run for each plate to ensure the quality of the amplification reaction. Literature search We compared our data with previously published literature. For this, we searched PubMed for studies on miRNA as biomarkers in our disease groups. The following search query was employed and adapted for each disease: (((((((microRNAs[Title/Abstract]) OR microRNA[Title/Abstract]) OR miRNA[Title/Abstract]) OR miRNAs[Title/Abstract]) OR MIR[Title/Abstract])) (biomarker [Title/Abstract] OR biomarkers [Title/Abstract]) AND PD/MSA/PSP. The diseases were searched for with the following addition to the query: (Parkinson’s disease[Title/Abstract] OR Parkinson’s[Title/Abstract]), (Progressive supranuclear palsy[Title/Abstract]) and (Multiple system atrophy[Title/Abstract]). Only studies reporting human data from either biofluids or brain tissue were considered. A full listing of all used studies can be found in Table 2 . Statistical and data analysis Statistical analyses were performed using R version 4.4.2 (The R Foundation for statistical Computing, Vienna, Austria). Data was plotted using the packages ggplot2 [ 40 ], pheatmap [ 41 ] and UpSetR [ 42 ]. The significance level was set at alpha = 0.05 (5%). For the overall cohort, categorical data were described by absolute and relative frequencies, and quantitative data by mean with standard deviation (SD) or median with minimum and maximum. The cumulative WL in mm/5 min for both eyes was calculated for each subject. To distinguish the mean values of WL between the different groups, multiple linear regression was performed, taking the confounders age and sex into account. Ordinary one-way ANOVA with Tukey post-hoc testing was used to compare the distribution of relevant variables between groups (age, disease duration, miRNA concentration). Fisher’s exact test was used for categorical variables (sex, eye diseases, eye medication). Pearson’s correlation coefficient was used to estimate the association between miRNA concentration and clinical data. For relevant effect measures, 95% confidence intervals were calculated. For the miRNA quantitative data, raw cycle threshold (CT) values of miRNAs were categorized according to probability of expression: if both technical replicates had a CT value of < 40 and an SD of < 5, miRNAs were considered amplified with high certainty. All other miRNAs were divided in two groups for the overall expression analysis: miRNAs not amplified in both technical replicates were considered as not amplified and any condition in between was considered as amplified with low certainty. For the intersection analysis and the comparison to literature, both of these groups were summarized. For the two PD biological replicates, any miRNA that was considered amplified with high certainty according to these criteria in at least one biological replicate was considered as amplified with high certainty. miRNA names were converted using the miRBaseConverter package. For the over-representation analysis (ORA) we used the DIANA miRPath online tool (v4) [ 43 ]. Standard settings were used: miRTarBase annotated targets without long non-coding targets were used in the Gene Union algorithm with a p-value threshold of 0.05 and FDR correction. The resulting lists were then further filtered for terms that included targets from 5 or more miRNAs and had an FDR < 0.01. In addition, REVIGO [ 44 ] was employed for clustering analysis of the list of terms. This tool allows GO term clustering by hierarchy using semantic similarity, p-adjusted values and term proximity measures. Default settings for a reduction to small size were used and the FDR of the terms added as additional information. Declarations Funding This study was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy – ID 390857198). Competing interests All authors declare no financial or non-financial competing interests. Author contributions A.F.D.: conceptualization, investigation, formal analysis, methodology, writing - original draft, writing - review & editing. L.C.G.: conceptualization, methodology, writing - review & editing. L.W.: investigation, writing - original draft, writing - review & editing. LT: investigation, writing - original draft, writing - review & editing. D.P.: investigation, writing - review & editing. E.L.: investigation, writing - review & editing. L.H.K.: methodology, writing - review & editing. P.L.: conceptualization, formal analysis, methodology, writing - original draft, writing - review & editing. Data availability Original data is available with the investigators upon reasonable request. Ethics approval This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the Technical University Munich (approval numbers: 9/15S, 2021-473-S-KH). Clinical trial number: not applicable. Consent to participate Informed consent was obtained from all individual participants included in the study. Acknowledgements The authors thank the patients, who donated biomaterial, for their participation in the study. References Postuma RB, Berg D, Stern M, Poewe W, Olanow CW, Oertel W, Obeso J, Marek K, Litvan I, Lang AE, Halliday G, Goetz CG, Gasser T, Dubois B, Chan P, Bloem BR, Adler CH, Deuschl G (2015) MDS clinical diagnostic criteria for Parkinson's disease. Mov Disord 30(12):1591–1601. 10.1002/mds.26424 Wenning GK, Stankovic I, Vignatelli L, Fanciulli A, Calandra-Buonaura G, Seppi K, Palma JA, Meissner WG, Krismer F, Berg D, Cortelli P, Freeman R, Halliday G, Höglinger G, Lang A, Ling H, Litvan I, Low P, Miki Y, Panicker J, Pellecchia MT, Quinn N, Sakakibara R, Stamelou M, Tolosa E, Tsuji S, Warner T, Poewe W, Kaufmann H (2022) The Movement Disorder Society Criteria for the Diagnosis of Multiple System Atrophy. Mov Disord. 10.1002/mds.29005 Höglinger GU, Respondek G, Stamelou M, Kurz C, Josephs KA, Lang AE, Mollenhauer B, Müller U, Nilsson C, Whitwell JL, Arzberger T, Englund E, Gelpi E, Giese A, Irwin DJ, Meissner WG, Pantelyat A, Rajput A, Van Swieten JC, Troakes C, Antonini A, Bhatia KP, Bordelon Y, Compta Y, Corvol J-C, Colosimo C, Dickson DW, Dodel R, Ferguson L, Grossman M, Kassubek J, Krismer F, Levin J, Lorenzl S, Morris HR, Nestor P, Oertel WH, Poewe W, Rabinovici G, Rowe JB, Schellenberg GD, Seppi K, Van Eimeren T, Wenning GK, Boxer AL, Golbe LI, Litvan I (2017) Clinical diagnosis of progressive supranuclear palsy: The movement disorder society criteria. Mov Disord 32(6):853–864. 10.1002/mds.26987 Rizzo G, Copetti M, Arcuti S, Martino D, Fontana A, Logroscino G (2016) Accuracy of clinical diagnosis of Parkinson disease: A systematic review and meta-analysis. Neurology 86(6):566–576. 10.1212/WNL.0000000000002350 Diener C, Keller A, Meese E (2024) The miRNA–target interactions: An underestimated intricacy. Nucleic Acids Res 52(4):1544–1557. 10.1093/nar/gkad1142 Cao D-D, Li L, Chan W-Y (2016) MicroRNAs: Key Regulators in the Central Nervous System and Their Implication in Neurological Diseases. Int J Mol Sci 17(6):842. 10.3390/ijms17060842 Shaheen N, Shaheen A, Osama M, Nashwan AJ, Bharmauria V, Flouty O (2024) MicroRNAs regulation in Parkinson’s disease, and their potential role as diagnostic and therapeutic targets. npj Parkinson's Disease 10(1). 10.1038/s41531-024-00791-2 Weber JA, Baxter DH, Zhang S, Huang DY, How Huang K, Jen Lee M, Galas DJ, Wang K (2010) The MicroRNA Spectrum in 12 Body Fluids. Clin Chem 56(11):1733–1741. 10.1373/clinchem.2010.147405 Roser AE, Caldi Gomes L, Schünemann J, Maass F, Lingor P (2018) Circulating miRNAs as Diagnostic Biomarkers for Parkinson’s Disease. Front Neurosci 12. 10.3389/fnins.2018.00625 Guévremont D, Roy J, Cutfield NJ, Williams JM (2023) MicroRNAs in Parkinson’s disease: a systematic review and diagnostic accuracy meta-analysis. Sci Rep 13(1). 10.1038/s41598-023-43096-9 Ćwiklińska A, Procyk G, Koziorowski D, Szlufik S (2024) The Role of MicroRNAs in Progressive Supranuclear Palsy—A Systematic Review. Int J Mol Sci 25(15):8243. 10.3390/ijms25158243 Marques TM, Kuiperij HB, Bruinsma IB, Van Rumund A, Aerts MB, Esselink RAJ, Bloem BR, Verbeek MM (2017) MicroRNAs in Cerebrospinal Fluid as Potential Biomarkers for Parkinson’s Disease and Multiple System Atrophy. Mol Neurobiol 54(10):7736–7745. 10.1007/s12035-016-0253-0 Börger M, Funke S, Bähr M, Grus F, Lingor P (2015) /08/01) Biomarker sources for Parkinson's disease: Time to shed tears? Basal Ganglia 5(2–3). 10.1016/j.baga.2015.05.001 Gijs M, Ramakers IHGB, Visser PJ, Verhey FRJ, Van De Waarenburg MPH, Schalkwijk CG, Nuijts RMMA, Webers CAB (2021) Association of tear fluid amyloid and tau levels with disease severity and neurodegeneration. Sci Rep 11(1). 10.1038/s41598-021-01993-x Gijs M, Jorna N, Datson N, Beekman C, Dansokho C, Weiss A, Linden DEJ, Oosterloo M (2024) High levels of mutant huntingtin protein in tear fluid from Huntington’s Disease Gene Expansion Carriers. J Mov Disorders. 10.14802/jmd.24014 Kenny A, Jiménez-Mateos EM, Zea-Sevilla MA, Rábano A, Gili-Manzanaro P, Prehn JHM, Henshall DC, Ávila J, Engel T, Hernández F (2019) Proteins and microRNAs are differentially expressed in tear fluid from patients with Alzheimer’s disease. Sci Rep 9(1). 10.1038/s41598-019-51837-y Hamm-Alvarez SF, Janga SR, Edman MC, Feigenbaum D, Freire D, Mack WJ, Okamoto CT, Lew MF (2019) Levels of oligomeric α-Synuclein in reflex tears distinguish Parkinson's disease patients from healthy controls. Biomark Med 13(17):1447–1457. 10.2217/bmm-2019-0315 Luib E, Demleitner AF, Cordts I, Westenberg E, Rau P, Pürner D, Haller B, Lingor P (2023) Reduced tear fluid production in neurological diseases: a cohort study in 708 patients. J Neurol. 10.1007/s00415-023-12104-3 Maass F, Rikker S, Dambeck V, Warth C, Tatenhorst L, Csoti I, Schmitz M, Zerr I, Leha A, Bähr M, Lingor P (2020) Increased alpha-synuclein tear fluid levels in patients with Parkinson’s disease. Sci Rep 10(1). 10.1038/s41598-020-65503-1 Boerger M, Funke S, Leha A, Roser AE, Wuestemann AK, Maass F, Bahr M, Grus F, Lingor P (2019) Proteomic analysis of tear fluid reveals disease-specific patterns in patients with Parkinson's disease - A pilot study. Parkinsonism Relat Disord 63:3–9. 10.1016/j.parkreldis.2019.03.001 Schmitz M, Silva Correia S, Hermann P, Maass F, Goebel S, Bunck T, Correia A, Lingor P, Fischer A, Zerr I (2023) Detection of Prion Protein Seeding Activity in Tear Fluids. N Engl J Med 388(19):1816–1817. 10.1056/NEJMc2214647 Wijesinghe P, Xi J, Cui J, Campbell M, Pham W, Matsubara JA (2023) MicroRNAs in tear fluids predict underlying molecular changes associated with Alzheimer’s disease. Life Sci Alliance 6(6):e202201757. 10.26508/lsa.202201757 Wang Z, Becker K, Donadio V, Siedlak S, Yuan J, Rezaee M, Incensi A, Kuzkina A, Orrú CD, Tatsuoka C, Liguori R, Gunzler SA, Caughey B, Jimenez-Capdeville ME, Zhu X, Doppler K, Cui L, Chen SG, Ma J, Zou W-Q (2020) Skin α-Synuclein Aggregation Seeding Activity as a Novel Biomarker for Parkinson Disease. JAMA Neurol. 10.1001/jamaneurol.2020.3311 Lazzeri G, Franco G, Difonzo T, Carandina A, Gramegna C, Vergari M, Arienti F, Naci A, Scatà C, Monfrini E, Dias Rodrigues G, Montano N, Comi GP, Saetti MC, Tobaldini E, Di Fonzo A (2022) Cognitive and Autonomic Dysfunction in Multiple System Atrophy Type P and C: A Comparative Study. Front Neurol 13. 10.3389/fneur.2022.912820 Baschieri F, Vitiello M, Cortelli P, Calandra-Buonaura G, Morgante F (2023) Autonomic dysfunction in progressive supranuclear palsy. J Neurol 270(1):109–129. 10.1007/s00415-022-11347-w Gijs M, Arumugam S, Van De Sande N, Webers CAB, Sethu S, Ghosh A, Shetty R, Vehof J, Nuijts RMMA (2023) Pre-analytical sample handling effects on tear fluid protein levels. Sci Rep 13(1). 10.1038/s41598-023-28363-z Chan HW, Yang B, Wong W, Blakeley P, Seah I, Tan QSW, Wang H, Bhargava M, Lin HA, Chai CH, Mangunkusumo EA, Thet N, Yuen YS, Sethi R, Wang S, Hunziker W, Lingam G, Su X (2020) A Pilot Study on MicroRNA Profile in Tear Fluid to Predict Response to Anti-VEGF Treatments for Diabetic Macular Edema. J Clin Med 9(9):2920. 10.3390/jcm9092920 Bougea A (2022) MicroRNA as Candidate Biomarkers in Atypical Parkinsonian Syndromes. Syst Literature Rev Med 58(4):483. 10.3390/medicina58040483 Ravishankar P, Daily A (2022) Tears as the Next Diagnostic Biofluid: A Comparative Study between Ocular Fluid and Blood. Appl Sci 12(6):2884. 10.3390/app12062884 Omais S, Jaafar C, Ghanem N (2018) Till Death Do Us Part: A Potential Irreversible Link Between Aberrant Cell Cycle Control and Neurodegeneration in the Adult Olfactory Bulb. Front Neurosci 12. 10.3389/fnins.2018.00144 Nandakumar S, Rozich E, Buttitta L (2021) Cell Cycle Re-entry in the Nervous System: From Polyploidy to Neurodegeneration. Front Cell Dev Biology 9. 10.3389/fcell.2021.698661 Friedlander RM (2003) Apoptosis and Caspases in Neurodegenerative Diseases. N Engl J Med 348(14). 10.1056/NEJMra022366 Sadlon A, Takousis P, Alexopoulos P, Evangelou E, Prokopenko I, Perneczky R (2019) miRNAs Identify Shared Pathways in Alzheimer's and Parkinson's Diseases. Trends Mol Med 25(8):662–672. 10.1016/j.molmed.2019.05.006 Bohush A, Niewiadomska G, Filipek A (2018) Role of Mitogen Activated Protein Kinase Signaling in Parkinson’s Disease. Int J Mol Sci 19(10):2973. 10.3390/ijms19102973 Blesch A (2006) Neurotrophic factors in neurodegeneration. Brain Pathol 16(4). 10.1111/j.1750-3639.2006.00036.x Boxer AL, Yu J-T, Golbe LI, Litvan I, Lang AE, Höglinger GU (2017) Advances in progressive supranuclear palsy: new diagnostic criteria, biomarkers, and therapeutic approaches. Lancet Neurol 16(7):552–563. 10.1016/s1474-4422(17)30157-6 Calogero AM, Viganò M, Budelli S, Galimberti D, Fenoglio C, Cartelli D, Lazzari L, Lehenkari P, Canesi M, Giordano R, Cappelletti G, Pezzoli G (2018) Microtubule defects in mesenchymal stromal cells distinguish patients with Progressive Supranuclear Palsy. J Cell Mol Med 22(5):2670–2679. 10.1111/jcmm.13545 Kanthida K, Michael N, Christian B, Matthias D, LK R, Armin G (2012) Effects of pooling samples on the performance of classification algorithms: a comparative study. The Scientific World Journal 2012. 10.1100/2012/278352 Schisterman E, Vexler A (2008) To pool or not to pool, from whether to when: applications of pooling to biospecimens subject to a limit of detection - PubMed. Paediatr Perinat Epidemiol 22(5). 10.1111/j.1365-3016.2008.00956.x Wickham H (2016) ggplot2. Use R! 10.1007/978-3 . -319-24277-4 Raivo K (2018) pheatmap: Pretty Heatmaps Conway JR, Lex A, Gehlenborg N (2017) UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics 33(18):2938–2940. 10.1093/bioinformatics/btx364 Tastsoglou S, Skoufos G, Miliotis M, Karagkouni D, Koutsoukos I, Karavangeli A, Kardaras FS, Hatzigeorgiou AG (2023) DIANA-miRPath v4.0: expanding target-based miRNA functional analysis in cell-type and tissue contexts. Nucleic Acids Res 51(W1):W154–W159. 10.1093/nar/gkad431 Supek F, Bošnjak M, Škunca N, Šmuc T (2011) REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms. PLoS ONE 6(7):e21800. 10.1371/journal.pone.0021800 Additional Declarations No competing interests reported. Supplementary Files Demleitneretal.2025Supplementarydata.docx Demleitneretal.2025SupplementarydataS2.xls Cite Share Download PDF Status: Published Journal Publication published 04 Aug, 2025 Read the published version in Molecular Neurobiology → Version 1 posted Editorial decision: Revision requested 03 Jun, 2025 Reviews received at journal 02 Jun, 2025 Reviews received at journal 02 Jun, 2025 Reviewers agreed at journal 24 May, 2025 Reviewers agreed at journal 23 May, 2025 Reviewers invited by journal 23 May, 2025 Editor assigned by journal 17 May, 2025 Submission checks completed at journal 17 May, 2025 First submitted to journal 23 Apr, 2025 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6512717","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":461558280,"identity":"3136d776-d45a-47fe-9344-32e389cf3df6","order_by":0,"name":"Antonia F. Demleitner","email":"","orcid":"","institution":"TUM University Hospital rechts der Isar, Technical University of Munich","correspondingAuthor":false,"prefix":"","firstName":"Antonia","middleName":"F.","lastName":"Demleitner","suffix":""},{"id":461558281,"identity":"1150d878-51a6-4c36-a80c-10f9b80414f1","order_by":1,"name":"Lucas Caldi Gomes","email":"","orcid":"","institution":"TUM University Hospital rechts der Isar, Technical University of Munich","correspondingAuthor":false,"prefix":"","firstName":"Lucas","middleName":"Caldi","lastName":"Gomes","suffix":""},{"id":461558282,"identity":"c68e7825-e7bf-41fb-8fa8-fabcf0cbf63e","order_by":2,"name":"Lara Wenz","email":"","orcid":"","institution":"TUM University Hospital rechts der Isar, Technical University of Munich","correspondingAuthor":false,"prefix":"","firstName":"Lara","middleName":"","lastName":"Wenz","suffix":""},{"id":461558283,"identity":"1d1494fa-4438-4811-bb4d-dacd9caebbee","order_by":3,"name":"Laura Tzeplaeff","email":"","orcid":"","institution":"TUM University Hospital rechts der Isar, Technical University of Munich","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Tzeplaeff","suffix":""},{"id":461558286,"identity":"eaefadc9-719b-4f85-b0cf-d9f194cd966f","order_by":4,"name":"Dominik Pürner","email":"","orcid":"","institution":"TUM University Hospital rechts der Isar, Technical University of Munich","correspondingAuthor":false,"prefix":"","firstName":"Dominik","middleName":"","lastName":"Pürner","suffix":""},{"id":461558287,"identity":"4b67b59b-6242-478c-afda-a56f61799aa0","order_by":5,"name":"Elena Luib","email":"","orcid":"","institution":"TUM University Hospital rechts der Isar, Technical University of Munich","correspondingAuthor":false,"prefix":"","firstName":"Elena","middleName":"","lastName":"Luib","suffix":""},{"id":461558288,"identity":"9ce5873e-c606-4c6a-9498-248f00714390","order_by":6,"name":"Lea H. Kunze","email":"","orcid":"","institution":"TUM University Hospital rechts der Isar, Technical University of Munich","correspondingAuthor":false,"prefix":"","firstName":"Lea","middleName":"H.","lastName":"Kunze","suffix":""},{"id":461558289,"identity":"ddcd2c0e-9068-4bf9-b938-2614aeebf4b9","order_by":7,"name":"Paul Lingor","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYHACxgMMBRJgxgMw/wAReg4wGIC1MBscIEELmGaTIEqLOfsZoNkGFnny7r3Pqj+2bWPgO96AX4tlTw5Ii0Sx4ZnjZjcOtt1mkDxDwBqDAxAtiRtnpLGBtRjcSCCg5fwbhJYCsJb7DwhouQG1Zb5EGhsDxBb8OhgsZzwrOJAA1LKB5xizxJlzt3kkzxBwmDl/8sYHHyrqEue3tzF+qCi7Lcd3/AABh4EIkLEGUIU8BJwF1QIC8g0E1Y6CUTAKRsFIBQBNn0q3pZ+jgAAAAABJRU5ErkJggg==","orcid":"","institution":"TUM University Hospital rechts der Isar, Technical University of Munich","correspondingAuthor":true,"prefix":"","firstName":"Paul","middleName":"","lastName":"Lingor","suffix":""}],"badges":[],"createdAt":"2025-04-23 12:38:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6512717/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6512717/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12035-025-05252-2","type":"published","date":"2025-08-04T15:57:56+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83479416,"identity":"c9e36e7f-6188-41d7-940c-7d602711b3d3","added_by":"auto","created_at":"2025-05-27 06:08:58","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":310765,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation of miRNA concentrations in tear fluid with clinical parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea) Overview of the experimental setup (created with Biorender). TF and patient data was collected from patients and controls. After \u0026nbsp;RNA isolation, Real-Time quantitative PCR (RT-qPCR) was performed and expression data analysed. b) Correlation of miRNA concentration and wetting length in the overall cohort. c) Correlation of miRNA concentration and wetting length within the subgroups of controls (black), PD (blue), MSA (orange) and PSP (red). d) Correlation of miRNA concentration and age in the overall cohort. e) Correlation of miRNA concentration and age within the subgroups of controls (black), PD (blue), MSA (orange) and PSP (red). PD = Parkinson’s disease, MSA = multiple system atrophy, PSP = progressive supranuclear palsy. Pearson’s correlation (Coefficient = R) was used for analyses. Data are depicted as a regression line with 95% confidence interval and individual data points. Panel a) was created in BioRender (Demleitner, A. (2025) https://BioRender.com/727vhvc).\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6512717/v1/29dadc37a7106961ee27038e.jpeg"},{"id":83479417,"identity":"e2a26169-e899-4cb3-9b5e-2f18c8d63040","added_by":"auto","created_at":"2025-05-27 06:08:58","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":230114,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression analysis of the miRNA RT-qPCR screen\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea) Heatmap using unsupervised hierarchical clustering showing expression data for all 1113 quantified miRNAs. miRNAs were assigned to different groups of probability of expression according to criteria outlined in the methods section in detail. b) UpSetR plot showing the intersections of miRNAs between the different groups. miRNAs present in the intersects are amplified in both technical replicates with a cycle threshold of \u0026lt;40 and a standard deviation of \u0026lt;5. Intersections representing miRNAs exclusively amplified in each disease group are filled in color, whereas intersections representing miRNAs found in all but the disease group are outlined in color (blue = PD, orange = MSA, red = PSP). c) Stacked bar graph representing the number of miRNAs in each disease intersection (consisting of the exclusively and exclusively not amplified miRNAs). The smaller bar to the right of the grey bar representing all miRNAs in the intersection shows the number and detailed annotation of miRNAs described for the respective disease in literature previously. PD = Parkinson’s disease, MSA = multiple system atrophy, PSP = progressive supranuclear palsy.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6512717/v1/cba5cebbac56b8fa8bd655a9.jpeg"},{"id":83479414,"identity":"2727df06-243a-4316-b6d3-263bdf5625c8","added_by":"auto","created_at":"2025-05-27 06:08:57","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":616073,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverrepresentation analysis of the miRNAs in the PD, MSA and PSP intersections\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTop 5 Terms sorted by enrichment ratio with FDR \u0026lt;0.01 and at least 5 miRNAs with targets in the terms are depicted. For each disease group (PD = a-d (blue box), MSA = e-h (orange box), PSP = i-l (red box)) GO MF, GO BP, GO CC and KEGG are shown (from left to right, top to bottom). Enrichment ratio (indicated below each graph) was calculated by dividing the number of enriched genes in the term by the total number of genes in the term. Negative values were assigned to terms derived from the analysis of the intersection of miRNAs exclusively not found in the disease group, positive values to those terms derived from the analysis of the intersection of exclusively in the disease group found miRNAs. Sizes of the dots represent target genes in the term, color represents the FDR. PD = Parkinson’s disease, MSA = multiple system atrophy, PSP = progressive supranuclear palsy, GO = Gene Ontology, MF = Molecular Function, BP = Biological Process, CC = Cellular Component, KEGG = Kyoto Encyclopedia of Genes and Genomes pathways.\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6512717/v1/4b530252bd3128ed3ea70ba7.jpeg"},{"id":88814194,"identity":"da5dee79-a881-4353-a81d-8a31ebb4ee32","added_by":"auto","created_at":"2025-08-11 16:08:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2148224,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6512717/v1/37f87e40-7754-4f4c-ad48-199ca26fe283.pdf"},{"id":83479598,"identity":"96c7c425-0ec9-43aa-bc09-a7d46ad9af59","added_by":"auto","created_at":"2025-05-27 06:16:58","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1209979,"visible":true,"origin":"","legend":"","description":"","filename":"Demleitneretal.2025Supplementarydata.docx","url":"https://assets-eu.researchsquare.com/files/rs-6512717/v1/1ffe2a10862f57f380cf7728.docx"},{"id":83479415,"identity":"42b9497f-d71d-4b76-bd60-34c2b566c2cc","added_by":"auto","created_at":"2025-05-27 06:08:58","extension":"xls","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":229888,"visible":true,"origin":"","legend":"","description":"","filename":"Demleitneretal.2025SupplementarydataS2.xls","url":"https://assets-eu.researchsquare.com/files/rs-6512717/v1/5cfa3c713fb6a5042d90814f.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"Differential tear fluid miRNAs in patients with Parkinson’s disease and atypical Parkinsonian syndromes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eParkinson\u0026rsquo;s disease (PD) and atypical Parkinsonian syndromes (aPS), including multiple system atrophy (MSA) and progressive supranuclear palsy (PSP), but also corticobasal degeneration (CBD) and dementia with Lewy bodies (DLB), are neurodegenerative disorders characterized by the progressive loss of motor and cognitive functions.\u003c/p\u003e \u003cp\u003eWhile specific diagnostic criteria relying on the clinical history, physical examination as well as the patient\u0026rsquo;s response to levodopa, have been developed for all mentioned diseases [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], an overlap of symptoms is common, especially in early stages of the disease. Furthermore, definite diagnosis is only possible postmortem through neuropathological examination of brain tissue. Therefore, the current diagnostic approaches remain challenging and frequently result in delays and misdiagnosis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This emphasizes the critical need for reliable biomarkers to facilitate early detection and more targeted interventions, ultimately enhancing disease management and patient outcomes.\u003c/p\u003e \u003cp\u003eMicroRNAs (miRNA) are small, non-coding RNAs of about 22 nucleotides length. They can regulate the post-transcriptional gene-expression through binding with the 3\u0026rsquo;UTR of its target mRNA leading to cleavage or translational repression [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. While one miRNA regulates the expression of multiple genes, a single gene can be regulated by multiple miRNAs. Therefore, the impact of miRNAs on a given biological process is complex. In the central nervous system (CNS), miRNAs regulate key processes such as neurite outgrowth, dendritic development, neuronal differentiation and synaptic plasticity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In PD, miRNAs have been shown to play a significant role in key pathomechanisms like mitochondrial dysfunction, protein aggregation, oxidative stress, and neuroinflammation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough typically restricted to tissue, some miRNAs are released in extracellular biofluids [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. There, their composition and levels have been shown to reflect different disease states. Growing evidence suggests that miRNA in cerebrospinal fluid (CSF) can help distinguish PD from healthy controls [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Limited data is available regarding expression patterns in the different aPS [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile most data on miRNAs in biofluids stems from either blood or CSF, miRNAs have also been detected in other biofluids such as saliva and tear fluid (TF) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Particularly, TF has recently gained attention as a potential source of biomarkers for neurological diseases [\u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Although TF is an ultrafiltrate from blood, the lacrimal gland, through the innervating parasympathetic nerves, receives input from regions in the brain stem \u0026ndash; areas commonly affected in most neurodegenerative disorders. Interestingly, reduced TF production has been demonstrated in a range of neurodegenerative diseases, supporting the widespread involvement of the lacrimal system [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. TF could therefore serve as a valuable, non-invasive and easily accessible bioliquid for early detection and monitoring of PD and other neurodegenerative diseases. Indeed, several studies report changes in established biomarkers of neurodegenerative diseases such as PD, Alzheimer\u0026rsquo;s dementia (AD), Creutzfeldt-Jakob-Disease and Huntington\u0026rsquo;s disease [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eData on miRNAs in the TF of patients with neurodegenerative diseases is limited. Changes in the expression of miRNAs associated with amyloid beta production and inflammation in TF of transgenic mice mimicking AD have been linked to concomitant neurodegeneration [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Kenny et al. have demonstrated high concentrations of miRNA in TF with a significant difference in total miRNA levels between AD and healthy controls. Moreover, specific miRNAs have been identified to serve as potential biomarkers [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, the role of miRNA in TF of PD and aPS remains unexplored.\u003c/p\u003e \u003cp\u003eThis study aimed to investigate the expression patterns of miRNA in TF of PD, MSA and PSP patients in comparison to healthy controls to gain insight into miRNAs as possible biomarkers in the diagnosis of Parkinsonian syndromes. For this, we performed an RT-qPCR-based analysis of the miRNAome of PD, MSA and PSP as well as control TF samples using pooled cDNA samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Our results show distinct differences between groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTF was collected using Schirmer test strips from 56 patients. 10 of these were healthy control patients without evidence of neurodegenerative disease, 29 were patients with probable or clinically established PD (mean Hoehn and Yahr stage 2.2 +/- 1). In addition, we included 7 patients with either possible or probable MSA, of which 4 were of the cerebellar and 3 of the parkinsonian subtype, and 10 patients with either possible or probable PSP were included. All patients were well matched regarding age, sex, concomitant eye diseases or medication (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Disease duration, however, was significantly shorter in both MSA and PSP, compared to PD (mean 8.3 +/- 0.7 years in PD, 1.9 +/- 0.9 years in MSA and 2.7 +/- 1.6 years in PSP, p\u003csub\u003eANOVA\u003c/sub\u003e = 0.005, p\u003csub\u003ePosthoc\u0026minus;PSP\u003c/sub\u003e = 0.02, p\u003csub\u003ePosthoc\u0026minus;MSA\u003c/sub\u003e = 0.03). Importantly, wetting length (WL) of the Schirmer test strips was significantly shorter in the PD but not the PSP and MSA group compared to the control group when correcting for age in a multiple linear regression model (37 +/- 22 mm/ 5 min in control, 21 +/- 17 mm/ 5 min in PD, 20 +/- 21 mm/ 5 min in MSA, 18 +/- 9 mm/ 5 min in PSP) (p\u0026thinsp;=\u0026thinsp;0.02, estimate \u0026minus;\u0026thinsp;10.73, 95% confidence interval [-19.39, -2.07]).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eCharacteristics of the study population.\u003c/b\u003e Continuous data are presented as either median (minimum - maximum) or mean (+/- standard deviation). Categorical data are presented as absolute numbers (percentages). PD\u0026thinsp;=\u0026thinsp;Parkinson\u0026rsquo;s disease, MSA\u0026thinsp;=\u0026thinsp;multiple system atrophy, PSP\u0026thinsp;=\u0026thinsp;progressive supranuclear palsy. \u003csup\u003eA\u003c/sup\u003e Fisher\u0026rsquo;s Exact test, \u003csup\u003eB\u003c/sup\u003e One-way ANOVA with Tukey post-hoc testing, \u003csup\u003eC\u003c/sup\u003e multiple linear regression correcting for age.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMSA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePatients\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex (female)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.89\u003c/b\u003e\u003csup\u003e\u003cb\u003eA\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (y)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMean +/- SD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 +/- 8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 +/- 9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67 +/- 13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72 +/- 5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.20\u003c/b\u003e\u003csup\u003e\u003cb\u003eB\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMedian (min - max)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (57\u0026ndash;86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 (53\u0026ndash;82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64 (45\u0026ndash;85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72 (64\u0026ndash;79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDisease duration (y)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMean +/- SD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.3 +/- 0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9 +/- 0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.7 +/- 1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003csup\u003e\u003cb\u003eB\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMedian (min - max)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.4 (0.1\u0026ndash;23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5 (0.9\u0026ndash;3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.0 (0.3\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHoehn \u0026amp; Yahr Stage\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMean +/- SD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.2 +/- 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOphthalmic data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEye disease (yes)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.91\u003c/b\u003e\u003csup\u003e\u003cb\u003eA\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEye medication (yes)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.49\u003c/b\u003e\u003csup\u003e\u003cb\u003eA\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eWetting length (mm/ 5 min)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMean +/- SD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 +/- 22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 +/- 17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 +/- 21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18 +/- 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003csup\u003e\u003cb\u003eC\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMedian (min - max)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (10\u0026ndash;65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (3\u0026ndash;70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (5\u0026ndash;62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18 (3\u0026ndash;35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003emiRNA concentration (ng/\u0026micro;l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMean +/- SD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 +/- 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 +/- 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 +/- 13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17 +/- 11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.52\u003c/b\u003e\u003csup\u003e\u003cb\u003eA\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMedian (min - max)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (2.1\u0026ndash;46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.7 (3.5\u0026ndash;38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.5 (1.3\u0026ndash;31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14 (4.8\u0026ndash;42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003emiRNA concentration correlates with wetting length\u003c/h2\u003e \u003cp\u003eWe next isolated miRNA from the TF eluate and quantified its concentration. No significant difference between the groups was observed (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Correlating the miRNA concentration to clinical parameters, a significant and strong correlation with WL was observed in the overall cohort (R\u003csub\u003eP\u003c/sub\u003e = 0.68, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 95% CI [0.50, 0.80]) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). This effect was persistently observed in each of the individual patient groups, as well (control R\u003csub\u003eP\u003c/sub\u003e = 0.68, p\u0026thinsp;=\u0026thinsp;0.029, 95% CI [0.10, 0.92], PD R\u003csub\u003eP\u003c/sub\u003e = 0.58, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 95% CI [0.27, 0.78], MSA R\u003csub\u003eP\u003c/sub\u003e = 0.91, p\u0026thinsp;=\u0026thinsp;0.004, 95% CI [0.50, 0.99], PSP R\u003csub\u003eP\u003c/sub\u003e = 0.74, p\u0026thinsp;=\u0026thinsp;0.013, 95% CI [0.22, 0.94]) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Age at sampling and miRNA concentration did not show any significant correlation in the overall cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). However, a strong negative correlation was found in the control group (R = -0.76, p\u0026thinsp;=\u0026thinsp;0.011, 95% CI [-0.94, -0.24]), whereas no trend was observed for PD, MSA, and PSP (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). Lastly, we evaluated the relationship of disease duration and miRNA concentration. Combining all disease groups, PD, MSA, and PSP, no significant correlation was observed. Exploring the effect in the subgroups, no correlation was seen for PD and PSP whereas a strong positive correlation was seen for MSA (R\u0026thinsp;=\u0026thinsp;0.84, p\u0026thinsp;=\u0026thinsp;0.038, 95% CI [0.08, 0.98]) (Supp. Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePCR-profiling of miRNAs in the tear fluid of patients with PD and aPD\u003c/h3\u003e\n\u003cp\u003eAfter isolation of miRNAs, we quantified the miRNA levels of the pooled control, PD, MSA and PSP samples using an RT-qPCR-based miRNA profiling kit. Of all 1113 quantifiable miRNAs, 286 were found in all groups, whereas 244 miRNAs were not found in any group. Unsupervised hierarchical clustering of the expression status of all quantified miRNAs revealed a clustering of the PD and PSP groups, as well as the MSA and control groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Next, we aimed to identify miRNAs that were uniquely identified in each of the disease groups. For this, all miRNAs exclusively amplified with high certainty in the respective groups or exclusively amplified in all other groups, but not the disease group itself, were analysed using an UpSetR plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). 55 miRNAs were exclusively amplified with high certainty in the PD group, whereas 4 miRNAs were exclusively amplified with high certainty in all groups but the PD group. In the MSA group, 14 miRNAs were exclusively amplified with high certainty and 41 miRNAs were exclusively amplified with high certainty in all other groups except for the MSA group. 35 miRNAs were exclusively amplified with high certainty in the PSP group, whereas 27 miRNAs were exclusively amplified with high certainty in all groups but the PSP group (a detailed listing of all miRNAs in the intersections is available in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The annotated names from the profiling kit were converted to the current annotation in miRBase (v22) and only currently annotated miRNAs were used for further analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003ePresence of TF miRNA in other biofluids\u003c/h3\u003e\n\u003cp\u003eTo understand whether the miRNAs identified have been previously described in other biomaterials or are potentially specific for tear fluid, we compared our findings to data obtained in other biofluids. A detailed listing of all literature used for this comparison can be found in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (for a detailed description of methodological aspects of the search see the Methods Section \u0026ldquo;Literature search\u0026rdquo;). While approximately one third of the miRNAs did not show any differential expression in the respective disease groups in other biofluids before, some miRNAs have been shown to be differentially regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). For the PD intersections, 56 of the 59 miRNAs identified as being either exclusively present or absent in PD with the profiling kit were annotated in the current version of miRBase (v22). Of those, 40 were not previously described as significantly altered in PD. 12 were previously described in blood, hsa-miR-542-5p was previously described in CSF, hsa-miR-516a-5p in brain tissue. Hsa-miR-95-3p and hsa-miR-374a-5p have been identified to be altered in CSF and brain tissue, while the latter was also described in blood. 53 of the 55 miRNAs found in the MSA intersections are currently annotated. Of these, 46 have not been shown to be altered in MSA before. Hsa-miR-130a-3p, hsa-miR-29c-3p, hsa-miR-92a-1-5p and hsa-miR-93-5p have been depicted differently expressed in blood, while hsa-miR-1203 and hsa-miR-1909-5p were discriminative in brain tissue. Hsa-miR-24-1-5p was shown to be altered in blood and CSF. Lastly, while we identified 62 miRNAs in the PSP intersects, only 60 of them were annotated in miRBase. Of these, 56 have not been described as altered in other biofluids. Hsa-miR-425-5p and hsa-miR-99b-5p were previously shown to be altered in blood. Hsa-miR-423-5p was identified to be altered in CSF and hsa-miR-132-3p in brain tissue. Taken together, most previously described miRNAs have been shown in blood, while only two and four have been identified in CSF and brain tissue, respectively. Only three miRNAs, two of them belonging to the group of miRNAs identified in PD, have been described in more than one biomaterial. The majority of miRNAs, namely 16, have been described in PD, while only seven and four have been identified in MSA and PSP, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMicroRNAs in the intersections of the cohort.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emiRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlood\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCSF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBrain Tissue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-106b-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eXie 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-128-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRavanidis 2020, Braunger 2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-193a-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDong 2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-199a-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMartins 2011, Li 2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-410-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRavanidis 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-487b-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKern 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-505-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKhoo 2012, Yao 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-193b-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBaghi 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-103a-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSchwienbacher 2017, Serafin 2015, Soto 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-454-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCardo 2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-654-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCai 2021, Hou 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-671-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUwatoko 2019, Khoo 2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-542-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMo 2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-516a-6p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHoss 2016, Chatterjee 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-95-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edos Santos 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBriggs 2015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-374a-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMartins 2011, Tong 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTong 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBriggs 2015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-93-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026eacute;rez-Soriano 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-92a-1-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKume 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-29c-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVallelunga 2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-130a-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKume 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-1203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWakabayashi 2016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-1909-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWakabayashi 2016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-24-1-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVallelunga 2014, Kume 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMarques 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-425-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eManna 2021, Ramaswamy 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-99b-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRamaswamy 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-423-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNonaka 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa-miR-132-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmith 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003emiRNAs that have been reported to be significantly changed between the disease groups and control in other biofluid studies sorted by group as indicated in the first column and by colour (blue\u0026thinsp;=\u0026thinsp;PD, orange\u0026thinsp;=\u0026thinsp;MSA, red\u0026thinsp;=\u0026thinsp;PSP). PD\u0026thinsp;=\u0026thinsp;Parkinson\u0026rsquo;s disease, MSA\u0026thinsp;=\u0026thinsp;multiple system atrophy, PSP\u0026thinsp;=\u0026thinsp;progressive supranuclear palsy, CSF\u0026thinsp;=\u0026thinsp;cerebrospinal fluid.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eOverrepresentation analysis of the unique intersects of miRNAs\u003c/h3\u003e\n\u003cp\u003eTo better inform about the function of the miRNAs identified in TF, we performed an overrepresentation analysis (ORA) using the DIANA miRPath online tool (v4). miRNAs that were exclusively identified in the respective disease groups were analysed separately from those exclusively absent in one of the respective groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Negative enrichment ratio values were assigned to exclusively absent miRNAs and positive values to exclusively present miRNAs. Semantic similarity analysis (using REVIGO) was then performed to reduce redundancy and summarize the complete list of terms.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter filtering, the GO term analysis for the PD intersections revealed only significantly enriched terms pointing to the \u003cem\u003emitogen-activated protein kinase (MAPK) pathway\u003c/em\u003e in the terms annotated to the molecular function (MF) category supported by the REVIGO analysis, which summarized the enriched terms under \u003cem\u003eprotein kinase activity\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, Supplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, Supplementary Data S2). The MAPK pathway has been associated with cell proliferation, differentiation, and survival. Exploring the REVIGO analysis in detail, several terms belonging to in-utero embryonic development are listed in the biological process (BP) category, e.g. nervous system development, axon guidance and generation of neurons (Supplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, Supplementary Data S2). Additionally, several other terms are summarized under the terms \u003cem\u003eregulation of cell population proliferation\u003c/em\u003e and \u003cem\u003eapoptotic process\u003c/em\u003e (Supplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, Supplementary Data S2).\u003c/p\u003e \u003cp\u003eFor the miRNAs exclusively found in MSA, several apoptosis-related terms were enriched in the BP category, such as \u003cem\u003enegative regulation of cell growth\u003c/em\u003e and \u003cem\u003enegative regulation of extrinsic apoptotic signaling pathway\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef, h), which was also complemented by the REVIGO analysis (Supplementary Fig. S3, Supplementary Data S2). Similar to the analysis in the PD group, \u003cem\u003eheart development\u003c/em\u003e was another overarching term in the semantic similarity analysis of these BP terms, which contained many terms belonging to developmental pathways such as \u003cem\u003enervous system development\u003c/em\u003e (Supplementary Fig. S3, Supplementary Data S2).\u003c/p\u003e \u003cp\u003eLastly, we analysed the annotated terms of the ORA of the PSP miRNA intersections. Here, looking into the MF terms, \u003cem\u003eneurotrophin TrkA receptor binding\u003c/em\u003e - a term belonging to a family of terms encompassing many neurotrophic pathways associated with neuronal development -, was one of the most enriched ones among the MF terms enriched in the exclusively absent miRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ei). Interestingly, some terms associated with immune system-related functions were among the top terms enriched in the exclusively present miRNAs, such as \u003cem\u003eMHC class II protein complex binding\u003c/em\u003e in the MF terms and \u003cem\u003ecellular response to interleukin-7\u003c/em\u003e in the BP terms (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ej). Semantic similarity analysis revealed apoptotic pathways summarized under the term of \u003cem\u003eapoptotic process\u003c/em\u003e in the BP category of the exclusively present miRNAs (Supplementary Fig. S4, Supplementary Data S2). Interestingly, also \u003cem\u003emicrotubule-based process\u003c/em\u003e was among the terms enriched in this category (Supplementary Fig. S4, Supplementary Data S2).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this pilot study, we provide a first description of the miRNAome in the TF of a cohort of patients with PD and the atypical Parkinsonian syndromes MSA and PSP. The WL of the Schirmer test strips was significantly reduced in the PD group compared to controls and this finding was previously observed in other cohorts [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Predominantly postganglionic autonomic dysfunction is part of the pathology in PD [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. While predominantly preganglionic autonomic dysfunction is a hallmark symptom in MSA [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], the impairment of the autonomic nervous system in PSP is less understood [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Thus, while WL did not differ significantly in the MSA and PSP groups compared to the control group, it is less apparent as to whether this might be due to the small sample size of these groups or other contributing factors.\u003c/p\u003e \u003cp\u003eWe show a strong positive correlation of miRNA concentration with WL across all disease groups, while no difference in concentration between the disease groups was observed. A positive correlation between protein concentration in TF and WL has been observed in several studies [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Interestingly, another study looking into the miRNA profiles of TF in patients with AD describes significantly increased miRNA concentration in AD while the WL was not significantly different from the control group [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Dysregulation of specific miRNAs has been shown in a wide range of pathological processes. Whether these effects stem from a particular group of miRNAs with major differences in expression or from a global effect in miRNA production is yet unexplored.\u003c/p\u003e \u003cp\u003eWe further explored the specific miRNAs expressed in the TF of our cohort. For this, we used an RT-qPCR-based approach and pooled samples to account for the relatively low number of TF samples. Normalization in TF samples is challenging, as commonly used normalization targets are unavailable. We therefore chose to categorize the data based on raw CT values into 3 groups: amplified with high certainty, amplified with low certainty, and not amplified. In total, our TF analyses revealed 286 miRNAs in all conditions, which is in line with previous findings exploring the total miRNAome in TF [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. We searched literature to identify miRNAs already described in the conditions. In contrast to previous literature, we identified 40 miRNAs that have not yet been described in PD, and 46 and 56 miRNAs that are described for the first time in MSA and PSP, respectively. The fact that more miRNAs in the PD cohort have been previously described might result from more available studies concerning PD. The large number of miRNAs that have been previously reported from blood samples may also reflect a publication bias as most of the studies so far have looked into miRNAs in blood [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, as TF is mainly an ultrafiltrate from blood and studies have shown similarities between the miRNAome of TF and plasma when comparing biofluids from the same individual [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], the comparably large overlap of differentially expressed miRNAs found in TF and blood could also result from the close connection between both biofluids. The second largest overlap was observed with miRNAs previously described in brain tissue. As the lacrimal gland receives parasympathetic innervation from the brain stem, these miRNAs could have entered the TF through neuronal vesicles via anterograde synaptic transport.\u003c/p\u003e \u003cp\u003eAlthough the miRNAs we identified in TF are extracellular and likely do not exert their known regulatory functions on RNA, overrepresentation analysis for predicted target genes could shed light on disease mechanisms regulated by these miRNAs. Consequently, it is important to point out that these miRNAs are not directly serving as intercellular regulators but rather potential markers of these processes. Terms comprising functions in \u003cem\u003ecell death and differentiation\u003c/em\u003e such as \u003cem\u003eangiogenesis\u003c/em\u003e and \u003cem\u003ein utero embryonic development\u003c/em\u003e are shared among the disease groups. They all encompass important cell cycle-related proteins that play a crucial role in the survival of mature neurons and neuronal apoptotic processes [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Cell death is an important hallmark of all neurodegenerative diseases, including PD, MSA, and PSP, and is closely linked to the activation of apoptotic processes [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Importantly, all these processes were identified in all studied disease groups, highlighting their importance for neurodegeneration in general. Some processes, however, were only associated with specific entities. For example, terms belonging to the MAPK pathway were enriched in the PD group. Although the MAPK pathway has also been implicated in cell death and apoptosis, evidence also suggests roles in axonal growth and guidance as well as oxidative stress in neurodegenerative diseases in general, including PD [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In the analysis of the PSP-associated miRNAs, neurotrophin signaling-related terms were highlighted. Neurotrophic factors have been linked to the survival of neurons in other neurodegenerative diseases [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Interestingly, the semantic similarity analysis identified \u003cem\u003emicrotubule-based process\u003c/em\u003e as an overarching term enriched in the miRNAs exclusively found in PSP. Notably, mutations in microtubule-associated protein tau (MAPT) are found in cases of familial PSP and aggregations of MAPT are a hallmark of the disease [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Further, microtubule defects have been shown in mesenchymal stromal cells of patients with PSP, suggesting the involvement of microtubule-associated processes in the disease pathology of patients with sporadic disease [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The low number of discriminative terms for the MSA groups might be attributable to the bigger heterogeneity, as well as the lower number of patients in this subgroup: more PD and PSP patients were diagnosed as clinically established according to the respective diagnostic criteria compared to the MSA group.\u003c/p\u003e \u003cp\u003eOur study has clear limitations: Because of the relatively low number of samples and the low relative amount of miRNA from each patient, we had to pool individual samples. However, even though this approach did not permit the evaluation on the individual patient level, the PCR-based analysis of the pool was sufficient to identify miRNAs present or absent in each of the studied conditions. Sample pooling in a discovery approach, as in this study, is a valid option with respect to balancing budgetary constraints and low concentration of the studied target molecule, when the signal is close to or below the detection threshold [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Furthermore, we did not analyse absolute expression levels of the miRNAs but instead converted them into categorical data to circumvent normalization problems due to the lack of established normalization miRNAs. Subsequent studies with a larger numbers of subjects, as well as exploring the possibilities of enhancing sensitivity of the detection methods or the concentration of the samples will be informative for individual analysis and stratification of single patients.\u003c/p\u003e \u003cp\u003eTaken together, our study shows differential expression of miRNAs in the TF patients with PD, MSA and PSP. It highlights the potential of TF as an easily accessible, non-invasive biomarker fluid suitable for longitudinal assessments. Further studies in bigger cohorts and individual samples are needed to confirm and expand on our findings.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThis retrospective, monocentric cohort compiles samples collected at the Department of Neurology of the TUM University Hospital rechts der Isar in Munich, Germany, from September 2019 to February 2021. TF was collected from 46 patients with either PD or atypical Parkinsonian disorders, namely MSA or PSP as well as control patients without signs of neurodegenerative disease. The detailed characteristics of the cohort are given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Patients were included if the disease was at least probable according to the respective Movement Disorder Society clinical diagnostic criteria [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. No other inclusion or exclusion criteria regarding age, sex, disease duration, concomitant diseases or medication were applied. Written informed consent was obtained from all participants. The study complies with the Declaration of Helsinki and was approved by the Ethics Committee of the Technical University of Munich, School of Medicine (approval numbers: 9/15S, 2021-473-S-KH).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTear fluid sampling and sample preparation\u003c/h3\u003e\n\u003cp\u003eTF collection was performed as previously published [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In brief, we employed a standardized protocol of the Schirmer test using uncoloured filter strips (Madhu Instruments Pvt. Ltd., New Delhi, India). Strips were inserted in the lower fornix of each eye near the lateral canthus and left in place with eyes closed. No topical anaesthetic was used. After 5 min, the strips were carefully removed and the wetting length (WL) for both eyes was noted. The strips were individually packed in sample storage tubes and immediately frozen at -20\u0026deg;C and transferred to -80\u0026deg;C within one week for further analysis. Previous history regarding eye diseases, eye medications and the use of contact lenses was recorded.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRNA Isolation\u003c/h2\u003e \u003cp\u003eFor RNA isolation, TF was initially eluted from the strips. For this, strips were cut into small pieces and wet with 40 \u0026micro;l RNAse free water each. The tube containing the soaked pieces of the strips was placed in a bigger tube and a hole was punched in it. Samples were centrifuged at 16.000 G for 10 min.\u003c/p\u003e \u003cp\u003eNext, RNA was isolated using an adapted TRIzol-based protocol. In brief, TRIZol was added, and samples were incubated at room temperature for 5 min. 1-Bromo-3-Chlor-Propane was added to the samples and the tubes were shaken for 20 s followed by incubation at room temperature for 3 min. Phase separation was achieved via centrifugation at 12.000 G at 4\u0026deg;C for 15 min. The aqueous phase was transferred to a new tube. For precipitation, Glycoblue (Invitrogen, Massachusetts, USA) and Isopropanol were added, and samples were subsequently incubated at -20\u0026deg;C overnight. Samples were then centrifuged at 12.000 G at 4\u0026deg;C for 30 min. The pellet was washed twice in 75% ethanol and thereafter dried to remove all ethanol.\u003c/p\u003e \u003cp\u003eFor solubilization, the pellet was resuspended in RNAse-free water and samples were shaken in an incubator at 55\u0026deg;C for 2 min to facilitate resuspension. miRNA concentration was quantified using the Qubit miRNA assay (Invitrogen, Massachusetts, USA) and purity confirmed by spectrophotometry. Isolation was carried out for each sample separately with pooled stripes (left and right).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003emiRNA RT-qPCR screen\u003c/h2\u003e \u003cp\u003eThe QuantiMir Kit for the human miRNAome (SBI System Biosciences, California, USA) was used for quantification of miRNAs. This kit uses polyA-tailed miRNA real-time quantitative PCR (RT-qPCR) of 1113 miRNAs with 3 internal controls. The analysis was carried out in technical duplicates on 5 pooled samples: one containing all control samples, two containing 19 and 10 - respectively - of the 29 total PD samples, as well as one containing all 10 PSP samples and one containing all 7 MSA samples. cDNA synthesis was carried out as indicated in the protocol on 500 ng of miRNA for each sample pool. For the RT-qPCR the \u003cem\u003ePower\u003c/em\u003e SYBR Green PCR Master Mix from Applied Biosystems (Massachusetts, USA) was used for all samples. Melting curve analysis was run for each plate to ensure the quality of the amplification reaction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLiterature search\u003c/h2\u003e \u003cp\u003eWe compared our data with previously published literature. For this, we searched PubMed for studies on miRNA as biomarkers in our disease groups. The following search query was employed and adapted for each disease: (((((((microRNAs[Title/Abstract]) OR microRNA[Title/Abstract]) OR miRNA[Title/Abstract]) OR miRNAs[Title/Abstract]) OR MIR[Title/Abstract])) (biomarker [Title/Abstract] OR biomarkers [Title/Abstract]) AND PD/MSA/PSP. The diseases were searched for with the following addition to the query: (Parkinson\u0026rsquo;s disease[Title/Abstract] OR Parkinson\u0026rsquo;s[Title/Abstract]), (Progressive supranuclear palsy[Title/Abstract]) and (Multiple system atrophy[Title/Abstract]). Only studies reporting human data from either biofluids or brain tissue were considered. A full listing of all used studies can be found in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical and data analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using R version 4.4.2 (The R Foundation for statistical Computing, Vienna, Austria). Data was plotted using the packages ggplot2 [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], pheatmap [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] and UpSetR [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The significance level was set at alpha\u0026thinsp;=\u0026thinsp;0.05 (5%). For the overall cohort, categorical data were described by absolute and relative frequencies, and quantitative data by mean with standard deviation (SD) or median with minimum and maximum. The cumulative WL in mm/5 min for both eyes was calculated for each subject. To distinguish the mean values of WL between the different groups, multiple linear regression was performed, taking the confounders age and sex into account. Ordinary one-way ANOVA with Tukey post-hoc testing was used to compare the distribution of relevant variables between groups (age, disease duration, miRNA concentration). Fisher\u0026rsquo;s exact test was used for categorical variables (sex, eye diseases, eye medication). Pearson\u0026rsquo;s correlation coefficient was used to estimate the association between miRNA concentration and clinical data. For relevant effect measures, 95% confidence intervals were calculated.\u003c/p\u003e \u003cp\u003eFor the miRNA quantitative data, raw cycle threshold (CT) values of miRNAs were categorized according to probability of expression: if both technical replicates had a CT value of \u0026lt;\u0026thinsp;40 and an SD of \u0026lt;\u0026thinsp;5, miRNAs were considered amplified with high certainty. All other miRNAs were divided in two groups for the overall expression analysis: miRNAs not amplified in both technical replicates were considered as not amplified and any condition in between was considered as amplified with low certainty. For the intersection analysis and the comparison to literature, both of these groups were summarized. For the two PD biological replicates, any miRNA that was considered amplified with high certainty according to these criteria in at least one biological replicate was considered as amplified with high certainty.\u003c/p\u003e \u003cp\u003emiRNA names were converted using the miRBaseConverter package. For the over-representation analysis (ORA) we used the DIANA miRPath online tool (v4) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Standard settings were used: miRTarBase annotated targets without long non-coding targets were used in the Gene Union algorithm with a p-value threshold of 0.05 and FDR correction. The resulting lists were then further filtered for terms that included targets from 5 or more miRNAs and had an FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01. In addition, REVIGO [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] was employed for clustering analysis of the list of terms. This tool allows GO term clustering by hierarchy using semantic similarity, p-adjusted values and term proximity measures. Default settings for a reduction to small size were used and the FDR of the terms added as additional information.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Deutsche Forschungsgemeinschaft (DFG,\u2028German Research Foundation) under Germany\u0026rsquo;s Excellence Strategy within\u2028 the framework of the Munich Cluster for Systems Neurology (EXC 2145\u2028SyNergy \u0026ndash; ID 390857198).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no financial or non-financial competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.F.D.: conceptualization, investigation, formal analysis, methodology, writing - original draft, writing - review \u0026amp; editing. L.C.G.: conceptualization, methodology, writing - review \u0026amp; editing. L.W.: investigation, writing - original draft, writing - review \u0026amp; editing. LT: investigation, writing - original draft, writing - review \u0026amp; editing. D.P.: investigation, writing - review \u0026amp; editing.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eE.L.: investigation, writing - review \u0026amp; editing. L.H.K.: methodology, writing - review \u0026amp; editing. P.L.: conceptualization, formal analysis, methodology, writing - original draft, writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOriginal data is available with the investigators upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the Technical University Munich (approval numbers: 9/15S, 2021-473-S-KH). Clinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the patients, who donated biomaterial, for their participation in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePostuma RB, Berg D, Stern M, Poewe W, Olanow CW, Oertel W, Obeso J, Marek K, Litvan I, Lang AE, Halliday G, Goetz CG, Gasser T, Dubois B, Chan P, Bloem BR, Adler CH, Deuschl G (2015) MDS clinical diagnostic criteria for Parkinson's disease. Mov Disord 30(12):1591\u0026ndash;1601. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/mds.26424\u003c/span\u003e\u003cspan address=\"10.1002/mds.26424\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWenning GK, Stankovic I, Vignatelli L, Fanciulli A, Calandra-Buonaura G, Seppi K, Palma JA, Meissner WG, Krismer F, Berg D, Cortelli P, Freeman R, Halliday G, H\u0026ouml;glinger G, Lang A, Ling H, Litvan I, Low P, Miki Y, Panicker J, Pellecchia MT, Quinn N, Sakakibara R, Stamelou M, Tolosa E, Tsuji S, Warner T, Poewe W, Kaufmann H (2022) The Movement Disorder Society Criteria for the Diagnosis of Multiple System Atrophy. Mov Disord. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/mds.29005\u003c/span\u003e\u003cspan address=\"10.1002/mds.29005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH\u0026ouml;glinger GU, Respondek G, Stamelou M, Kurz C, Josephs KA, Lang AE, Mollenhauer B, M\u0026uuml;ller U, Nilsson C, Whitwell JL, Arzberger T, Englund E, Gelpi E, Giese A, Irwin DJ, Meissner WG, Pantelyat A, Rajput A, Van Swieten JC, Troakes C, Antonini A, Bhatia KP, Bordelon Y, Compta Y, Corvol J-C, Colosimo C, Dickson DW, Dodel R, Ferguson L, Grossman M, Kassubek J, Krismer F, Levin J, Lorenzl S, Morris HR, Nestor P, Oertel WH, Poewe W, Rabinovici G, Rowe JB, Schellenberg GD, Seppi K, Van Eimeren T, Wenning GK, Boxer AL, Golbe LI, Litvan I (2017) Clinical diagnosis of progressive supranuclear palsy: The movement disorder society criteria. Mov Disord 32(6):853\u0026ndash;864. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/mds.26987\u003c/span\u003e\u003cspan address=\"10.1002/mds.26987\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRizzo G, Copetti M, Arcuti S, Martino D, Fontana A, Logroscino G (2016) Accuracy of clinical diagnosis of Parkinson disease: A systematic review and meta-analysis. Neurology 86(6):566\u0026ndash;576. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1212/WNL.0000000000002350\u003c/span\u003e\u003cspan address=\"10.1212/WNL.0000000000002350\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiener C, Keller A, Meese E (2024) The miRNA\u0026ndash;target interactions: An underestimated intricacy. Nucleic Acids Res 52(4):1544\u0026ndash;1557. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkad1142\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkad1142\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao D-D, Li L, Chan W-Y (2016) MicroRNAs: Key Regulators in the Central Nervous System and Their Implication in Neurological Diseases. Int J Mol Sci 17(6):842. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms17060842\u003c/span\u003e\u003cspan address=\"10.3390/ijms17060842\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShaheen N, Shaheen A, Osama M, Nashwan AJ, Bharmauria V, Flouty O (2024) MicroRNAs regulation in Parkinson\u0026rsquo;s disease, and their potential role as diagnostic and therapeutic targets. npj Parkinson's Disease 10(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41531-024-00791-2\u003c/span\u003e\u003cspan address=\"10.1038/s41531-024-00791-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeber JA, Baxter DH, Zhang S, Huang DY, How Huang K, Jen Lee M, Galas DJ, Wang K (2010) The MicroRNA Spectrum in 12 Body Fluids. Clin Chem 56(11):1733\u0026ndash;1741. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1373/clinchem.2010.147405\u003c/span\u003e\u003cspan address=\"10.1373/clinchem.2010.147405\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoser AE, Caldi Gomes L, Sch\u0026uuml;nemann J, Maass F, Lingor P (2018) Circulating miRNAs as Diagnostic Biomarkers for Parkinson\u0026rsquo;s Disease. Front Neurosci 12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnins.2018.00625\u003c/span\u003e\u003cspan address=\"10.3389/fnins.2018.00625\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu\u0026eacute;vremont D, Roy J, Cutfield NJ, Williams JM (2023) MicroRNAs in Parkinson\u0026rsquo;s disease: a systematic review and diagnostic accuracy meta-analysis. Sci Rep 13(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-023-43096-9\u003c/span\u003e\u003cspan address=\"10.1038/s41598-023-43096-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eĆwiklińska A, Procyk G, Koziorowski D, Szlufik S (2024) The Role of MicroRNAs in Progressive Supranuclear Palsy\u0026mdash;A Systematic Review. Int J Mol Sci 25(15):8243. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms25158243\u003c/span\u003e\u003cspan address=\"10.3390/ijms25158243\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarques TM, Kuiperij HB, Bruinsma IB, Van Rumund A, Aerts MB, Esselink RAJ, Bloem BR, Verbeek MM (2017) MicroRNAs in Cerebrospinal Fluid as Potential Biomarkers for Parkinson\u0026rsquo;s Disease and Multiple System Atrophy. Mol Neurobiol 54(10):7736\u0026ndash;7745. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12035-016-0253-0\u003c/span\u003e\u003cspan address=\"10.1007/s12035-016-0253-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eB\u0026ouml;rger M, Funke S, B\u0026auml;hr M, Grus F, Lingor P (2015) /08/01) Biomarker sources for Parkinson's disease: Time to shed tears? Basal Ganglia 5(2\u0026ndash;3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.baga.2015.05.001\u003c/span\u003e\u003cspan address=\"10.1016/j.baga.2015.05.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGijs M, Ramakers IHGB, Visser PJ, Verhey FRJ, Van De Waarenburg MPH, Schalkwijk CG, Nuijts RMMA, Webers CAB (2021) Association of tear fluid amyloid and tau levels with disease severity and neurodegeneration. Sci Rep 11(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-021-01993-x\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-01993-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGijs M, Jorna N, Datson N, Beekman C, Dansokho C, Weiss A, Linden DEJ, Oosterloo M (2024) High levels of mutant huntingtin protein in tear fluid from Huntington\u0026rsquo;s Disease Gene Expansion Carriers. J Mov Disorders. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.14802/jmd.24014\u003c/span\u003e\u003cspan address=\"10.14802/jmd.24014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKenny A, Jim\u0026eacute;nez-Mateos EM, Zea-Sevilla MA, R\u0026aacute;bano A, Gili-Manzanaro P, Prehn JHM, Henshall DC, \u0026Aacute;vila J, Engel T, Hern\u0026aacute;ndez F (2019) Proteins and microRNAs are differentially expressed in tear fluid from patients with Alzheimer\u0026rsquo;s disease. Sci Rep 9(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-019-51837-y\u003c/span\u003e\u003cspan address=\"10.1038/s41598-019-51837-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamm-Alvarez SF, Janga SR, Edman MC, Feigenbaum D, Freire D, Mack WJ, Okamoto CT, Lew MF (2019) Levels of oligomeric α-Synuclein in reflex tears distinguish Parkinson's disease patients from healthy controls. Biomark Med 13(17):1447\u0026ndash;1457. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2217/bmm-2019-0315\u003c/span\u003e\u003cspan address=\"10.2217/bmm-2019-0315\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuib E, Demleitner AF, Cordts I, Westenberg E, Rau P, P\u0026uuml;rner D, Haller B, Lingor P (2023) Reduced tear fluid production in neurological diseases: a cohort study in 708 patients. J Neurol. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00415-023-12104-3\u003c/span\u003e\u003cspan address=\"10.1007/s00415-023-12104-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaass F, Rikker S, Dambeck V, Warth C, Tatenhorst L, Csoti I, Schmitz M, Zerr I, Leha A, B\u0026auml;hr M, Lingor P (2020) Increased alpha-synuclein tear fluid levels in patients with Parkinson\u0026rsquo;s disease. Sci Rep 10(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-020-65503-1\u003c/span\u003e\u003cspan address=\"10.1038/s41598-020-65503-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoerger M, Funke S, Leha A, Roser AE, Wuestemann AK, Maass F, Bahr M, Grus F, Lingor P (2019) Proteomic analysis of tear fluid reveals disease-specific patterns in patients with Parkinson's disease - A pilot study. Parkinsonism Relat Disord 63:3\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.parkreldis.2019.03.001\u003c/span\u003e\u003cspan address=\"10.1016/j.parkreldis.2019.03.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmitz M, Silva Correia S, Hermann P, Maass F, Goebel S, Bunck T, Correia A, Lingor P, Fischer A, Zerr I (2023) Detection of Prion Protein Seeding Activity in Tear Fluids. N Engl J Med 388(19):1816\u0026ndash;1817. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMc2214647\u003c/span\u003e\u003cspan address=\"10.1056/NEJMc2214647\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWijesinghe P, Xi J, Cui J, Campbell M, Pham W, Matsubara JA (2023) MicroRNAs in tear fluids predict underlying molecular changes associated with Alzheimer\u0026rsquo;s disease. Life Sci Alliance 6(6):e202201757. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.26508/lsa.202201757\u003c/span\u003e\u003cspan address=\"10.26508/lsa.202201757\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Z, Becker K, Donadio V, Siedlak S, Yuan J, Rezaee M, Incensi A, Kuzkina A, Orr\u0026uacute; CD, Tatsuoka C, Liguori R, Gunzler SA, Caughey B, Jimenez-Capdeville ME, Zhu X, Doppler K, Cui L, Chen SG, Ma J, Zou W-Q (2020) Skin α-Synuclein Aggregation Seeding Activity as a Novel Biomarker for Parkinson Disease. JAMA Neurol. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamaneurol.2020.3311\u003c/span\u003e\u003cspan address=\"10.1001/jamaneurol.2020.3311\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLazzeri G, Franco G, Difonzo T, Carandina A, Gramegna C, Vergari M, Arienti F, Naci A, Scat\u0026agrave; C, Monfrini E, Dias Rodrigues G, Montano N, Comi GP, Saetti MC, Tobaldini E, Di Fonzo A (2022) Cognitive and Autonomic Dysfunction in Multiple System Atrophy Type P and C: A Comparative Study. Front Neurol 13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fneur.2022.912820\u003c/span\u003e\u003cspan address=\"10.3389/fneur.2022.912820\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaschieri F, Vitiello M, Cortelli P, Calandra-Buonaura G, Morgante F (2023) Autonomic dysfunction in progressive supranuclear palsy. J Neurol 270(1):109\u0026ndash;129. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00415-022-11347-w\u003c/span\u003e\u003cspan address=\"10.1007/s00415-022-11347-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGijs M, Arumugam S, Van De Sande N, Webers CAB, Sethu S, Ghosh A, Shetty R, Vehof J, Nuijts RMMA (2023) Pre-analytical sample handling effects on tear fluid protein levels. Sci Rep 13(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-023-28363-z\u003c/span\u003e\u003cspan address=\"10.1038/s41598-023-28363-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan HW, Yang B, Wong W, Blakeley P, Seah I, Tan QSW, Wang H, Bhargava M, Lin HA, Chai CH, Mangunkusumo EA, Thet N, Yuen YS, Sethi R, Wang S, Hunziker W, Lingam G, Su X (2020) A Pilot Study on MicroRNA Profile in Tear Fluid to Predict Response to Anti-VEGF Treatments for Diabetic Macular Edema. J Clin Med 9(9):2920. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/jcm9092920\u003c/span\u003e\u003cspan address=\"10.3390/jcm9092920\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBougea A (2022) MicroRNA as Candidate Biomarkers in Atypical Parkinsonian Syndromes. Syst Literature Rev Med 58(4):483. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/medicina58040483\u003c/span\u003e\u003cspan address=\"10.3390/medicina58040483\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRavishankar P, Daily A (2022) Tears as the Next Diagnostic Biofluid: A Comparative Study between Ocular Fluid and Blood. Appl Sci 12(6):2884. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/app12062884\u003c/span\u003e\u003cspan address=\"10.3390/app12062884\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOmais S, Jaafar C, Ghanem N (2018) Till Death Do Us Part: A Potential Irreversible Link Between Aberrant Cell Cycle Control and Neurodegeneration in the Adult Olfactory Bulb. Front Neurosci 12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnins.2018.00144\u003c/span\u003e\u003cspan address=\"10.3389/fnins.2018.00144\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNandakumar S, Rozich E, Buttitta L (2021) Cell Cycle Re-entry in the Nervous System: From Polyploidy to Neurodegeneration. Front Cell Dev Biology 9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcell.2021.698661\u003c/span\u003e\u003cspan address=\"10.3389/fcell.2021.698661\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriedlander RM (2003) Apoptosis and Caspases in Neurodegenerative Diseases. N Engl J Med 348(14). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMra022366\u003c/span\u003e\u003cspan address=\"10.1056/NEJMra022366\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSadlon A, Takousis P, Alexopoulos P, Evangelou E, Prokopenko I, Perneczky R (2019) miRNAs Identify Shared Pathways in Alzheimer's and Parkinson's Diseases. Trends Mol Med 25(8):662\u0026ndash;672. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.molmed.2019.05.006\u003c/span\u003e\u003cspan address=\"10.1016/j.molmed.2019.05.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBohush A, Niewiadomska G, Filipek A (2018) Role of Mitogen Activated Protein Kinase Signaling in Parkinson\u0026rsquo;s Disease. Int J Mol Sci 19(10):2973. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms19102973\u003c/span\u003e\u003cspan address=\"10.3390/ijms19102973\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlesch A (2006) Neurotrophic factors in neurodegeneration. Brain Pathol 16(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1750-3639.2006.00036.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1750-3639.2006.00036.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoxer AL, Yu J-T, Golbe LI, Litvan I, Lang AE, H\u0026ouml;glinger GU (2017) Advances in progressive supranuclear palsy: new diagnostic criteria, biomarkers, and therapeutic approaches. Lancet Neurol 16(7):552\u0026ndash;563. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s1474-4422(17)30157-6\u003c/span\u003e\u003cspan address=\"10.1016/s1474-4422(17)30157-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCalogero AM, Vigan\u0026ograve; M, Budelli S, Galimberti D, Fenoglio C, Cartelli D, Lazzari L, Lehenkari P, Canesi M, Giordano R, Cappelletti G, Pezzoli G (2018) Microtubule defects in mesenchymal stromal cells distinguish patients with Progressive Supranuclear Palsy. J Cell Mol Med 22(5):2670\u0026ndash;2679. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jcmm.13545\u003c/span\u003e\u003cspan address=\"10.1111/jcmm.13545\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanthida K, Michael N, Christian B, Matthias D, LK R, Armin G (2012) Effects of pooling samples on the performance of classification algorithms: a comparative study. The Scientific World Journal 2012. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1100/2012/278352\u003c/span\u003e\u003cspan address=\"10.1100/2012/278352\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchisterman E, Vexler A (2008) To pool or not to pool, from whether to when: applications of pooling to biospecimens subject to a limit of detection - PubMed. Paediatr Perinat Epidemiol 22(5). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1365-3016.2008.00956.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-3016.2008.00956.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWickham H (2016) ggplot2. Use R! \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-3\u003c/span\u003e\u003cspan address=\"10.1007/978-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. -319-24277-4\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaivo K (2018) pheatmap: Pretty Heatmaps\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConway JR, Lex A, Gehlenborg N (2017) UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics 33(18):2938\u0026ndash;2940. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/btx364\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btx364\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTastsoglou S, Skoufos G, Miliotis M, Karagkouni D, Koutsoukos I, Karavangeli A, Kardaras FS, Hatzigeorgiou AG (2023) DIANA-miRPath v4.0: expanding target-based miRNA functional analysis in cell-type and tissue contexts. Nucleic Acids Res 51(W1):W154\u0026ndash;W159. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkad431\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkad431\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSupek F, Bošnjak M, Škunca N, Šmuc T (2011) REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms. PLoS ONE 6(7):e21800. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0021800\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0021800\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-neurobiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"moln","sideBox":"Learn more about [Molecular Neurobiology](https://www.springer.com/journal/12035)","snPcode":"12035","submissionUrl":"https://submission.nature.com/new-submission/12035/3","title":"Molecular Neurobiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"tear fluid, miRNA, biomarker, Parkinson’s syndrome, Progressive supranuclear palsy, Multiple system atrophy","lastPublishedDoi":"10.21203/rs.3.rs-6512717/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6512717/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eParkinson\u0026rsquo;s disease (PD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP), are neurodegenerative disorders diagnosed by clinical criteria with limited diagnostic specificity in early stages. Diagnostic biomarkers facilitating early and precise diagnosis are needed. Tear fluid (TF) is an easily accessible body fluid reflecting pathophysiological changes in ocular and systemic diseases. This study explores TF as a non-invasive source of disease-specific miRNAs for PD, MSA, and PSP. We demonstrate reduced TF production in PD patients. Using a real-time quantitative PCR-based array targeting 1113 miRNAs, we identified 55 miRNAs exclusively expressed in PD, 35 miRNAs in PSP, and 14 in MSA, respectively. Several of these have previously been identified in other biofluids. Overrepresentation analysis of target genes showed apoptotic and cell differentiation pathways as common targets. These findings suggest that miRNA alterations in TF might reflect disease mechanisms in PD and atypical Parkinsonian syndromes, warranting further exploration as potential biomarkers.\u003c/p\u003e","manuscriptTitle":"Differential tear fluid miRNAs in patients with Parkinson’s disease and atypical Parkinsonian syndromes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-27 06:08:46","doi":"10.21203/rs.3.rs-6512717/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-03T09:02:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-02T20:02:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-02T06:58:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11298155029851581091865944062865251167","date":"2025-05-24T07:48:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"88881195354894059306086268468884148554","date":"2025-05-23T13:36:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-23T09:38:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-17T09:32:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-17T09:30:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Neurobiology","date":"2025-04-23T12:33:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-neurobiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"moln","sideBox":"Learn more about [Molecular Neurobiology](https://www.springer.com/journal/12035)","snPcode":"12035","submissionUrl":"https://submission.nature.com/new-submission/12035/3","title":"Molecular Neurobiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"222e12b7-f361-4a54-ba98-1596d9f47ab4","owner":[],"postedDate":"May 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-11T16:03:33+00:00","versionOfRecord":{"articleIdentity":"rs-6512717","link":"https://doi.org/10.1007/s12035-025-05252-2","journal":{"identity":"molecular-neurobiology","isVorOnly":false,"title":"Molecular Neurobiology"},"publishedOn":"2025-08-04 15:57:56","publishedOnDateReadable":"August 4th, 2025"},"versionCreatedAt":"2025-05-27 06:08:46","video":"","vorDoi":"10.1007/s12035-025-05252-2","vorDoiUrl":"https://doi.org/10.1007/s12035-025-05252-2","workflowStages":[]},"version":"v1","identity":"rs-6512717","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6512717","identity":"rs-6512717","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}