Potential Tear Biomarkers For The Diagnosis Of Parkinson’s Disease—A Pilot Study Ⅰ
Mar 02, 2023
Abstract: Parkinson’s disease (PD) is the second most common neurodegenerative disease after Alzheimer’s disease. In this study, the tear proteome profile of patients with idiopathic PD (iPD, n = 24), carriers of the E46K-SNCA mutation (n = 3), and healthy control (CT, n = 27) subjects were analyzed to identify candidate biomarkers for the diagnosis of PD. An observational, prospective, and case-control pilot study was carried out, exploring the participant's tear samples by nano-liquid chromatography–mass spectrometry (nLC–MS/MS) and assessing their neurological impairment. The proteomic data obtained are available at ProteomeXchange with identifier 10.6019/PXD028811.
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These analyses led to the identification of 560 tear proteins, some of which were deregulated in PD
patients and that have been implicated in immune responses, inflammation, apoptosis, collagen
degradation, protein synthesis, defense, lipid transport, and altered lysosomal function. Of these
proteins, six were related to neurodegenerative processes and showed an excellent capacity to classify
patients and controls. These findings revealed that certain proteins were upregulated in the tears of
PD patients, mainly proteins involved in lysosomal function. Thus, in this study, tear proteins were
identified as implicated in neurodegeneration that may be related to an aggressive disease
phenotype in PD patients.
Keywords: biomarkers; Parkinson’s disease; tear film; lysosome
1. Introduction
Parkinson’s disease (PD) is the second most common neurodegenerative disease after Alzheimer’s disease (AD), and it is characterized by slowness of movement (bradykinesia), increased muscle tone (rigidity), shaking (tremor), and impaired postural control. In pathologically confirmed PD, there is a variable amount and deposition of misfolded alpha-synuclein (α-syn) in the form of Lewy bodies (LBs) and Lewy neurites in the central nervous system (CNS). This variability in the accumulation of these structures is likely to reflect the clinical heterogeneity in this disease [1–3]. Indeed, several clinical disorders lie in the spectrum of Lewy body diseases (LBDs), including patients with idiopathic (IPD) and genetic PD and patients suffering from dementia with LBs. One of the main challenges in dealing with neurodegenerative diseases is to find clinical markers that allow for the early classification of patients and help to monitor disease progression.
Factors such as oxidative stress, proteolysis, and altered fatty acid or phospholipid concentrations have been associated with structural changes to α-syn, and post-translational modifications can also alter the size, structure, or protein load of α-syn [4]. Deregulating mutations and multiplications in the α-syn gene (SNCA) cause autosomal-dominant familial forms of PD [5]. Perhaps the most pathogenic mutation known to induce PD is the E46K mutation in α-syn (E46K-SNCA), which underlies a group of aggressive, genetic LBDs with clinical and pathological features reminiscent of dementia with LBs [6,7]. Although mutations in α-syn are rare, they provide a unique opportunity to identify patients with PD and to better understand the progression of these pathologies.
It is known that PD patients develop hallucinations, changes in eye and eyelid movement, and altered tear composition in reduced amounts [8,9]. In addition to eye-surface damage, these patients can experience changes in accommodation, reduced visual acuity, scotoma formation (areas where their field of vision is partially diminished or completely degenerated), and a thinning of the retinal layers, particularly due to a reduction in the number of nerve fibers [10,11]. Deficiencies in neurotransmission and monoamine metabolism also influence the pathological changes to the visual system of PD patients [12].
These changes are quite typical since monoamines are involved in the transmission of visual information in the retina [13]. Moreover, PD patients may experience alterations to the innervation of the anterior part of the eye that can ultimately alter the composition of tears. Many neurotransmitters and trophic substances are secreted in tears, originating from the aqueous humor or nerves running through the cornea [14].
However, the cellular and molecular mechanisms that underlie these associations have yet to be fully elucidated. As PD affects several non-motor systems and peripheral nerves, lacrimal secretion could be altered in these patients, and the tear protein composition might exhibit a characteristic profile in these patients that could serve as a diagnostic biomarker. In this sense, analyzing the tear proteins of patients with E46K-SNCA and iPD could help to identify useful candidate biomarkers for the diagnosis and progression of PD.
Therefore, the present study aimed to analyze the tear proteome profile in iPD patients and carriers of the E46K-SNCA mutation to find a biomarker that could help in the future diagnosis of PD, using non-invasive samples.
2. Materials and Methods
2.1. Study Cohort
We designed an observational, prospective, case-control pilot study in which 24 patients with iPD, 3 carriers of the E46K-SNCA mutation, and 27 healthy subjects (CTs) were enrolled. This research was conducted by medically qualified personnel after receiving approval from the OSI Ezkerralde-Enkaterri-Cruces Ethics Committee CEIC E18/47. The study was carried out in strict accordance with the tenets of the Helsinki Declaration on Biomedical Research Involving Human Subjects. Before sample collection, signed informed consent was obtained from all the subjects after having explained the nature and possible consequences of the study. The patients were recruited at the Neurology and Ophthalmology Service Units of the Cruces University Hospital (Barakaldo, Bizkaia, Spain) during outpatient consultations, based on the agreed inclusion and exclusion criteria. Patients with IPD fulfilled the Parkinson’s UK Brain Bank criteria for the diagnosis of PD and did not carry any known mutations.
The inclusion criteria for CTs were to be over 40 years of age, of either gender, and without a clinical history of any neurological disorder. Individuals with (or with a history of) any systemic or ocular disorder/condition were included in the study. The exclusion criteria consisted of eye surgery in the previous three months, chronic eye medication (e.g., for glaucoma), a history of allergies, using any topical medication (other than artificial tears) or oral medication containing corticosteroids, atopy, and patients with Sjögren’s syndrome.
Contact-lens users were also excluded to avoid any possible interference with the interpretation of the results. Preliminary ocular tests and sample collection were performed on the same day, while patients with PD were also seen on a separate day by members of the research team who are experts in movement disorders to assess the Parkinson’s Scales.
2.2. Ophthalmological Examination
The clinical ophthalmological examination included Schirmer’s test I (SCH), which determines whether the eye produces enough tears to keep it moist. The test allows the water in tears to travel along the length of a paper test strip, and normal values are ≥10 mm wetting of the paper after 5 min. Tear-film instability was assessed by using tear breakup time (TBUT) with fluorescein. TBUT is the time taken for the first dry spot to appear on the cornea after a complete blinking. A value less than 10 s usually reflects some tear-film instability. Any systemic disease or any other condition/medication use that could interfere with the interpretation of the results was considered a criterion for exclusion.
2.3. Neurological Examination
A neurologist expert in the field of movement disorders recorded the age of the PD patients at tear collection, their disease duration, and their score on the Unified Parkinson’s Disease Rating Scale (UPDRS) and Hoehn Yahr scale. These tests serve as stratification criteria to track the progression of the disease. The confidentiality of patient data was ensured through the use of the Basque Biobank.
2.4. Tear-Sample Collection
All tear samples were collected by using calibrated 10 µL glass microcapillary tubes (BLAUBRAND intraMark, Wertheim, Germany). Tear samples were obtained from the inferior temporal tear meniscus, minimizing irritation of the ocular surface or lid margin, and without installation of anesthesia. The tear samples were collected from both eyes of each participant and immediately placed in precooled Eppendorf tubes. After tear collection, the samples were stored at −80 ◦C until their analyses.
2.5. Proteomics Analyses
The proteomic analyses were carried out at the CIC bioGUNE Proteomics Service (Derio, Bizkaia, Spain), using the Filter Aided Sample Preparation (FASP) protocol [15] for sample processing and digestion. Trypsin (Sigma Aldrich, St. Louis, MO, USA) was added at a trypsin: protein ratio of 1:50, and the mixture was incubated overnight at 37 ◦C, dried in an RVC2 25 Speedvac concentrator (Christ, Vienna, Austria). The peptides obtained were desalted and resuspended in 0.1% Formic Acid (FA) (Sigma Aldrich, St. Louis, MO, USA) using C18 stage tips (Millipore, St. Louis, MO, USA).
Samples were analyzed in a novel hybrid trapped ion mobility spectrometry quadrupole time of flight mass spectrometer (timsTOF Pro with PASEF, Bruker Daltonics, Bremen, Germany), coupled online to a nanoElute liquid chromatograph (Bruker, Coventry, UK). This mass spectrometer takes advantage of a novel scan mode termed parallel accumulation serial fragmentation (PASEF), which multiplies the sequencing speed without any loss in sensitivity, and it has been proven to provide outstanding analytical speed and sensitivity for proteomics analyses [16].
Samples (200 ng) were directly loaded onto a 15 cm Bruker nano elute FIFTEEN C18 analytical column (Bruker, Coventry, UK) and resolved at 400 nL/min. The column was heated to 50 ◦C in an oven. Protein identification and quantification were carried out by using the PEAKS software (Bioinformatics Solutions Inc., Waterloo, CA, USA) Searches were carried out against a database of canonical human Uniprot/Swissprot entries (no isoforms considerer), with precursor and fragment tolerances of 20 ppm and 0.05 Da.
An area-based label-free protein quantification was performed by using the PEAKS Q module available in the PEAKS software. Only proteins identified with at least two peptides at a False Discovery Rate (FDR) < 1% and present in at least 70% of the samples from one of the experimental groups under analysis were considered for further analysis. The data were loaded onto the Perseus platform [17] and further processed (log2 transformation, imputation) before the application of a Student’s t-test for differential protein-expression analysis. The proteomics data are summarized in Supplementary Materials File S1, and the mass spectrometry proteomics data were deposited to the ProteomeXchange Consortium via the PRIDE [18] partner repository, with the dataset identifiers PXD028811 and 10.6019/PXD028811.
2.6. Statistical Analyses
Statistical analyses were carried out by using the IBM SPSS Statistics package for Windows, 23.0v (IBM-SPSS, Armonk, New York, NY, USA). The normality of continuous data was assessed with a Shapiro–Wilk test. The p-values calculated determine the probability that the association between proteins in the dataset and a given canonical pathway, functional network, or upstream regulator is explained by chance alone, based on a Fisher’s exact test, with a p-value < 0.05 being considered significant.
Univariate logistic regressions were used to identify the statistically significant characteristics associated with PD-patient discrimination. After variable selection, multivariate logistic regressions were used to construct a PD discrimination model and to identify the clinical and proteomic variables relevant to PD identification. Logistic models allow the relationship between a discrete dependent variable and a set of continuous or discrete independent variables to be estimated. The models allow the classification of patients with or without PD, calculating the probability of belonging to each group. The p-value of each covariate describes the statistical significance of the association between the response and each term included in the model.
for more information: Ali.ma@wecistanche.com
