Clinical Parkinson’s Disease Research Articles

Wavelet-based selection-and-recalibration network for Parkinson’s disease screening in OCT images

Background and Objective:Parkinson’s disease (PD) is one of the most prevalent neurodegenerative brain diseases worldwide. Therefore, accurate PD screening is crucial for early clinical intervention and treatment. Recent clinical research indicates that changes in pathology, such as the texture and thickness of the retinal layers, can serve as biomarkers for clinical PD diagnosis based on optical coherence tomography (OCT) images. However, the pathological manifestations of PD in the retinal layers are subtle compared to the more salient lesions associated with retinal diseases. Methods:Inspired by textural edge feature extraction in frequency domain learning, we aim to explore a potential approach to enhance the distinction between the feature distributions in retinal layers of PD cases and healthy controls. In this paper, we introduce a simple yet novel wavelet-based selection and recalibration module to effectively enhance the feature representations of the deep neural network by aggregating the unique clinical properties, such as the retinal layers in each frequency band. We combine this module with the residual block to form a deep network named Wavelet-based Selection and Recalibration Network (WaveSRNet) for automatic PD screening. Results:The extensive experiments on a clinical PD-OCT dataset and two publicly available datasets demonstrate that our approach outperforms state-of-the-art methods. Visualization analysis and ablation studies are conducted to enhance the explainability of WaveSRNet in the decision-making process. Conclusions:Our results suggest the potential role of the retina as an assessment tool for PD. Visual analysis shows that PD-related elements include not only certain retinal layers but also the location of the fovea in OCT images.

Aggregation-resistant alpha-synuclein tetramers are reduced in the blood of Parkinson’s patients

Synucleinopathies such as Parkinson’s disease (PD) are defined by the accumulation and aggregation of the α-synuclein protein in neurons, glia and other tissues. We have previously shown that destabilization of α-synuclein tetramers is associated with familial PD due to SNCA mutations and demonstrated brain-region specific alterations of α-synuclein multimers in sporadic PD patients following the classical Braak spreading theory. In this study, we assessed relative levels of disordered and higher-ordered multimeric forms of cytosolic α-synuclein in blood from familial PD with G51D mutations and sporadic PD patients. We used an adapted in vitro-cross-linking protocol for human EDTA-whole blood. The relative levels of higher-ordered α-synuclein tetramers were diminished in blood from familial PD and sporadic PD patients compared to controls. Interestingly, the relative amount of α-synuclein tetramers was already decreased in asymptomatic G51D carriers, supporting the hypothesis that α-synuclein multimer destabilization precedes the development of clinical PD. Our data, therefore suggest that measuring α-synuclein tetramers in blood may have potential as a facile biomarker assay for early detection and quantitative tracking of PD progression.

A potential patient stratification biomarker for Parkinson´s disease based on LRRK2 kinase-mediated centrosomal alterations in peripheral blood-derived cells

Parkinson´s disease (PD) is a common neurodegenerative movement disorder and leucine-rich repeat kinase 2 (LRRK2) is a promising therapeutic target for disease intervention. However, the ability to stratify patients who will benefit from such treatment modalities based on shared etiology is critical for the success of disease-modifying therapies. Ciliary and centrosomal alterations are commonly associated with pathogenic LRRK2 kinase activity and can be detected in many cell types. We previously found centrosomal deficits in immortalized lymphocytes from G2019S-LRRK2 PD patients. Here, to investigate whether such deficits may serve as a potential blood biomarker for PD which is susceptible to LRKK2 inhibitor treatment, we characterized patient-derived cells from distinct PD cohorts. We report centrosomal alterations in peripheral cells from a subset of early-stage idiopathic PD patients which is mitigated by LRRK2 kinase inhibition, supporting a role for aberrant LRRK2 activity in idiopathic PD. Centrosomal defects are detected in R1441G-LRRK2 and G2019S-LRRK2 PD patients and in non-manifesting LRRK2 mutation carriers, indicating that they accumulate prior to a clinical PD diagnosis. They are present in immortalized cells as well as in primary lymphocytes from peripheral blood. These findings indicate that analysis of centrosomal defects as a blood-based patient stratification biomarker may help nominate idiopathic PD patients who will benefit from LRRK2-related therapeutics.

Increased palmitoylation improves estrogen receptor alpha-dependent hippocampal synaptic deficits in a mouse model of synucleinopathy.

Parkinson's disease (PD) is characterized by conversion of soluble α-synuclein (αS) into intraneuronal aggregates and degeneration of neurons and neuronal processes. Indications that women with early-stage PD display milder neurodegenerative features suggest that female sex partially protects against αS pathology. We previously reported that female sex and estradiol improved αS homeostasis and PD-like phenotypes in E46K-amplified (3K) αS mice. Here, we aimed to further dissect mechanisms that drive this sex dimorphism early in disease. We observed that synaptic abnormalities were delayed in females and improved by estradiol, mediated by local estrogen receptor alpha (ERα). Aberrant ERα distribution in 3K compared to wild-type mice was paired with its decreased palmitoylation. Treatment with ML348, a de-palmitoylation inhibitor, increased ERα availability and soluble αS homeostasis, ameliorating synaptic plasticity and cognitive and motor phenotypes. Our finding that sex differences in early-disease αS-induced synaptic impairment in 3KL mice are in part mediated by palmitoylated ERα may have functional and pathogenic implications for clinical PD.

Parkinson’s disease in a patient with GBA and LRRK2 covariants after acute hypoxic insult: a case report

BackgroundThe glucocerebrosidase (GBA) and leucine-rich repeat kinase 2 (LRRK2) genes are associated with the risk of sporadic Parkinson’s disease (PD). As an environmental factor, hypoxic insults may impair dopamine neurons in the substantia nigra and exacerbate PD symptoms. However, covariants of GBA and LRRK2 combined with hypoxic insults in clinical cases of Parkinsonism have not yet been reported.Case presentationA 69-year-old male patient with PD and his relatives were clinically characterized and sequenced using the whole-exome technique. A novel covariant, c.1448 T > C (p. L483P, rs421016) on GBA and c.691 T > C (p. S231P, rs201332859) on LRRK2 were identified in this patient who first developed bradykinesia and rigidity in the neck at one month after an acute hypoxic insult during mountaineering. The patient presented with a mask-like face, festinating gait, asymmetric bradykinesia, and moderate rigidity. These symptoms were treated with levodopa and pramipexole, resulting in a 65% improvement in the Unified Parkinson’s Disease Rating Scale (UPDRS) motor score. These parkinsonian symptoms persisted and developed with hallucinations, constipation, and rapid eye movement sleep behavior disorder. After 4 years, the patient exhibited a wearing-off phenomenon and died from pulmonary infection 8 years after disease onset. His parents, wife, and siblings were not diagnosed with PD, and his son carried p. L483P without Parkinsonism-like symptoms.ConclusionsThis is a case report of PD after hypoxic insult in a patient carrying a covariant of GBA and LRRK2. This study may help us understand the interaction between genetic and environmental factors in clinical PD.

Escalating Bi-Directional Feedback Loops between Proinflammatory Microglia and Mitochondria in Ageing and Post-Diagnosis of Parkinson's Disease.

Parkinson's disease (PD) is a chronic and progressive age-related neurodegenerative disease affecting up to 3% of the global population over 65 years of age. Currently, the underlying physiological aetiology of PD is unknown. However, the diagnosed disorder shares many common non-motor symptoms associated with ageing-related neurodegenerative disease progression, such as neuroinflammation, microglial activation, neuronal mitochondrial impairment, and chronic autonomic nervous system dysfunction. Clinical PD has been linked to many interrelated biological and molecular processes, such as escalating proinflammatory immune responses, mitochondrial impairment, lower adenosine triphosphate (ATP) availability, increasing release of neurotoxic reactive oxygen species (ROS), impaired blood brain barrier integrity, chronic activation of microglia, and damage to dopaminergic neurons consistently associated with motor and cognitive decline. Prodromal PD has also been associated with orthostatic hypotension and many other age-related impairments, such as sleep disruption, impaired gut microbiome, and constipation. Thus, this review aimed to present evidence linking mitochondrial dysfunction, including elevated oxidative stress, ROS, and impaired cellular energy production, with the overactivation and escalation of a microglial-mediated proinflammatory immune response as naturally occurring and damaging interlinked bidirectional and self-perpetuating cycles that share common pathological processes in ageing and PD. We propose that both chronic inflammation, microglial activation, and neuronal mitochondrial impairment should be considered as concurrently influencing each other along a continuum rather than as separate and isolated linear metabolic events that affect specific aspects of neural processing and brain function.

PDGes: An Interpretable Detection Model for Parkinson’s Disease Using Smartphones

Parkinson’s disease (PD) is a neurodegenerative disorder that severely affects the motor system of patients. Early PD detection will greatly improve the quality of lives. However, existing automatic PD detection systems either rely on customized sensors or require users to perform special activities, using machine learning models whose prediction process is not understandable by medical professionals. In this article, we develop a non-disruptive PD detection system on smartphones based on interpretable prediction models. We design an application named PDGes to passively collect touchscreen and Inertial Measurement Unit data of users’ tapping and swiping actions on smartphones. Meaningful features that reflect finger dexterity , tremor , stiffness , and hand movement are extracted to build the prediction model. To better comprehend the decisions made by the model, we conduct a systematic analysis of feature importance to help validate the conformity of the model with clinical PD diagnosis. We collected data from 108 volunteers to evaluate the performance of PDGes . The experiment results show that PDGes achieves a detection accuracy of more than 94.5% on different smartphones.

A Combined Panel of Salivary Biomarkers in de novo Parkinson's Disease.

To investigate molecular biomarkers of a-synuclein and tau aggregation, autophagy, and inflammation in the saliva of de novo Parkinson's disease (PD) patients in comparison to healthy subjects (HS), and to correlate molecular data with clinical features of PD patients, in order to establish whether abnormalities of these parameters are associated with specific clusters of de novo PD patients, and their potential diagnostic power in differentiating PD patients from HS. We measured total and oligomeric a-synuclein, total-tau and phosphorylated-tau, microtubule-associated protein light chain 3 beta (MAP-LC3beta), and tumor necrosis factor alpha (TNFalpha) in the saliva of 80 de novo PD patients and 62 HS, using quantitative enzyme-linked immunosorbent Assay analysis. Oligomeric a-synuclein, total-tau, MAP-LC3beta, and TNFalpha levels resulted significantly higher in patients with respect to HS, while no significant differences were detected for total a-synuclein or phosphorylated-tau. Phosphorylated-tau directly correlated with MAP-LC3beta, whereas it inversely correlated with TNFalpha in PD patients. An inverse correlation was detected between MAP-LC3beta and non-motor symptoms severity. Principal Component Analysis showed that molecular and clinical parameters were independent of each other in de novo PD patients. Receiver operating characteristic curve analysis reported an accurate diagnostic performance of oligomeric a-synuclein and MAP-LC3beta. The diagnostic accuracy of total a-synuclein increased when it was combined with other salivary biomarkers targeting different molecular pathways. Our study proposes a novel biomarker panel using saliva, a non-invasive biofluid, in de novo PD patients, with implications in understanding the molecular pathways involved in PD pathogenesis and the relevance of different molecular pathways in determining clinical PD subtypes. ANN NEUROL 2023;93:446-459.

Bioinformatic Analysis of Genetic Factors from Human Blood Samples and Postmortem Brains in Parkinson's Disease.

Parkinson's disease (PD) is one of the most prevalent neurodegenerative disorders characterized by motor and nonmotor symptoms due to the selective loss of midbrain dopaminergic neurons. Pharmacological and surgical interventions have not been possible to cure PD; however, the cause of neurodegeneration remains unclear. Here, we performed and tested a multitiered bioinformatic analysis using the GEO and Proteinexchange database to investigate the gene expression involved in the pathogenesis of PD. Then we further validated individual differences in gene expression in whole blood samples that we collected in the clinic. We also made an interaction analysis and prediction for these genetic factors. There were in all 1045 genes expressing differently in PD compared with the healthy control group. Protein-protein interaction (PPI) networks showed 10 top hub genes: ACO2, MDH2, SDHA, ATP5A1, UQCRC2, PDHB, SUCLG1, NDUFS3, UQCRC1, and ATP5C1. We validated the ten hub gene expression in clinical PD patients and showed the expression of MDH2 was significantly different compared with healthy control. Besides, we also identified the expression of G6PD, GRID2, RIPK2, CUL4B, BCL6, MRPS31, GPI, and MAP 2 K1 were all significantly increased, and levels of MAPK, ELAVL1, RAB14, KLF9, ARF1, ARFGAP1, ATG7, ABCA7, SFT2D2, E2F2, MAPK7, and UHRF1 were all significantly decreased in PD. Among them, to our knowledge, we presently have the most recent and conclusive evidence that GRID2, RIPK2, CUL4B, E2F2, and ABCA7 are possible PD indicators. We confirmed several genetic factors which may be involved in the pathogenesis of PD. They could be promising markers for discriminating the PD and potential factors that may affect PD development.

Adaptive structural changes in the motor cortex and white matter in Parkinson’s disease

Parkinson’s disease (PD) is a movement disorder characterized by the early loss of nigrostriatal dopaminergic pathways producing significant network changes impacting motor coordination. Recently three motor stages of PD have been proposed (a silent period when nigrostriatal loss begins, a prodromal motor period with subtle focal manifestations, and clinical PD) with evidence that motor cortex abnormalities occur to produce clinical PD[8]. We directly assess structural changes in the primary motor cortex and corticospinal tract using parallel analyses of longitudinal clinical and cross-sectional pathological cohorts thought to represent different stages of PD. 18F-FP-CIT positron emission tomography and subtle motor features identified patients with idiopathic rapid-eye-movement sleep behaviour disorder (n = 8) that developed prodromal motor signs of PD. Longitudinal diffusion tensor imaging before and after the development of prodromal motor PD showed higher fractional anisotropy in motor cortex and corticospinal tract compared to controls, indicating adaptive structural changes in motor networks in concert with nigrostriatal dopamine loss. Histological analyses of the white matter underlying the motor cortex showed progressive disorientation of axons with segmental replacement of neurofilaments with α-synuclein, enlargement of myelinating oligodendrocytes and increased density of their precursors. There was no loss of neurons in the motor cortex in early or late pathologically confirmed motor PD compared to controls, although there were early cortical increases in neuronal neurofilament light chain and myelin proteins in association with α-synuclein accumulation. Our results collectively provide evidence of a direct impact of PD on primary motor cortex and its output pathways that begins in the prodromal motor stage of PD with structural changes confirmed in early PD. These adaptive structural changes become considerable as the disease advances potentially contributing to motor PD.

Intermuscular coherence as a biomarker of subthalamic nucleus deep brain stimulation efficacy in Parkinson’s disease

ObjectiveDeep brain stimulation of the subthalamic nucleus (STN-DBS) is an established treatment in advanced Parkinson’s disease (PD). However, the clinical outcome after STN-DBS is variable. The aim of this study was to explore the coherence of antagonistic muscles measured with electromyography (EMG) as novel biomarker of STN-DBS efficacy in PD. MethodsEMG of bilateral wrist and upper arm antagonistic muscles of 21 PD patients was recorded during three standardized motor tasks. Patients were measured one day prior to DBS surgery (pre-DBS) and 6 months afterwards (post-DBS). Coherence analyses were performed on the antagonistic muscle pairs. Pearson correlations between intermuscular coherence and clinical performance were calculated. ResultsIntermuscular coherence during each of the different co-contraction tasks significantly correlated to UPDRS-III bradykinesia scores (p < 0.01). In other words, higher intermuscular coherence is associated with more severe PD symptoms. Moreover, coherence changes (pre-DBS – post-DBS coherence) correlated to clinical score changes after DBS (p < 0.01) and pre-DBS coherence correlated to this clinical score change as well (p < 0.01). ConclusionsHigher pre-DBS coherence of antagonistic arm muscles is correlated to worsening of clinical PD state and higher intermuscular coherence predicts enhanced clinical improvement. SignificanceWe propose that pre-DBS intermuscular coherence could be developed into a predictor of STN-DBS clinical outcome. It could aid patient selection and adaptive stimulation algorithms for DBS.

Application of α-Syn Real-Time Quaking-Induced Conversion for Brain and Skin Specimens of the Chinese Patients With Parkinson's Disease.

The real-time quaking-induced conversion (RT-QuIC) assay has been developed and used as an in vitro diagnostic tool for Parkinson’s disease (PD). In this study, we established α-Syn RT-QuIC using recombinant human α-Syn as the substrate. All 5 brain homogenates of neuropathological PD cases and 13 skin homogenates of clinical PD cases showed positive results, whereas all the samples of negative controls remain negative. Meantime, randomly selected 6 skin samples of PD cases and 6 skin samples of sCJD cases showed negative in opposite prion RT-QuIC and α-Syn RT-QuIC. Our α-Syn RT-QuIC showed dose-dependent manner between the lag times and peak ThT fluorescent values. Additionally, the detecting limitation was about 10–7 dilution for brain tissues and 10–6 for skins. Those data indicate a reliable specificity and good sensitivity of the established α-Syn RT-QuIC in identifying and amplifying the misfolded α-Syn in brain and skin tissues of patients with PD.

Diagnostic classification of Parkinson’s disease based on non-motor manifestations and machine learning strategies

Non-motor manifestations of Parkinson’s disease (PD) appear early and have a significant impact on the quality of life of patients, but few studies have evaluated their predictive potential with machine learning algorithms. We evaluated 9 algorithms for discriminating PD patients from controls using a wide collection of non-motor clinical PD features from two databases: Biocruces (96 subjects) and PPMI (687 subjects). In addition, we evaluated whether the combination of both databases could improve the individual results. For each database 2 versions with different granularity were created and a feature selection process was performed. We observed that most of the algorithms were able to detect PD patients with high accuracy (>80%). Support Vector Machine and Multi-Layer Perceptron obtained the best performance, with an accuracy of 86.3% and 84.7%, respectively. Likewise, feature selection led to a significant reduction in the number of variables and to better performance. Besides, the enrichment of Biocruces database with data from PPMI moderately benefited the performance of the classification algorithms, especially the recall and to a lesser extent the accuracy, while the precision worsened slightly. The use of interpretable rules obtained by the RIPPER algorithm showed that simply using two variables (autonomic manifestations and olfactory dysfunction), it was possible to achieve an accuracy of 84.4%. Our study demonstrates that the analysis of non-motor parameters of PD through machine learning techniques can detect PD patients with high accuracy and recall, and allows us to select the most discriminative non-motor variables to create potential tools for PD screening.

Blood SSR1: A Possible Biomarker for Early Prediction of Parkinson's Disease.

Parkinson’s disease (PD) is the second most common neurodegenerative disease associated with age. Early diagnosis of PD is key to preventing the loss of dopamine neurons. Peripheral-blood biomarkers have shown their value in recent years because of their easy access and long-term monitoring advantages. However, few peripheral-blood biomarkers have proven useful. This study aims to explore potential peripheral-blood biomarkers for the early diagnosis of PD. Three substantia nigra (SN) transcriptome datasets from the Gene Expression Omnibus (GEO) database were divided into a training cohort and a test cohort. We constructed a protein–protein interaction (PPI) network and a weighted gene co-expression network analysis (WGCNA) network, found their overlapping differentially expressed genes and studied them as the key genes. Analysis of the peripheral-blood transcriptome datasets of PD patients from GEO showed that three key genes were upregulated in PD over healthy participants. Analysis of the relationship between their expression and survival and analysis of their brain expression suggested that these key genes could become biomarkers. Then, animal models were studied to validate the expression of the key genes, and only SSR1 (the signal sequence receptor subunit1) was significantly upregulated in both animal models in peripheral blood. Correlation analysis and logistic regression analysis were used to analyze the correlation between brain dopaminergic neurons and SSR1 expression, and it was found that SSR1 expression was negatively correlated with dopaminergic neuron survival. The upregulation of SSR1 expression in peripheral blood was also found to precede the abnormal behavior of animals. In addition, the application of artificial intelligence technology further showed the value of SSR1 in clinical PD prediction. The three classifiers all showed that SSR1 had high predictability for PD. The classifier with the best prediction accuracy was selected through AUC and MCC to construct a prediction model. In short, this research not only provides potential biomarkers for the early diagnosis of PD but also establishes a possible artificial intelligence model for predicting PD.

Antemortem detection of Parkinson\u2019s disease pathology in peripheral biopsies using artificial intelligence

The diagnosis of Parkinson’s disease (PD) is challenging at all stages due to variable symptomatology, comorbidities, and mimicking conditions. Postmortem assessment remains the gold standard for a definitive diagnosis. While it is well recognized that PD manifests pathologically in the central nervous system with aggregation of α-synuclein as Lewy bodies and neurites, similar Lewy-type synucleinopathy (LTS) is additionally found in the peripheral nervous system that may be useful as an antemortem biomarker. We have previously found that detection of LTS in submandibular gland (SMG) biopsies is sensitive and specific for advanced PD; however, the sensitivity is suboptimal especially for early-stage disease. Further, visual microscopic assessment of biopsies by a neuropathologist to identify LTS is impractical for large-scale adoption. Here, we trained and validated a convolutional neural network (CNN) for detection of LTS on 283 digital whole slide images (WSI) from 95 unique SMG biopsies. A total of 8,450 LTS and 35,066 background objects were annotated following an inter-rater reliability study with Fleiss Kappa = 0.72. We used transfer learning to train a CNN model to classify image patches (151 × 151 pixels at 20× magnification) with and without the presence of LTS objects. The trained CNN model showed the following performance on image patches: sensitivity: 0.99, specificity: 0.99, precision: 0.81, accuracy: 0.99, and F-1 score: 0.89. We further tested the trained network on 1230 naïve WSI from the same cohort of research subjects comprising 42 PD patients and 14 controls. Logistic regression models trained on features engineered from the CNN predictions on the WSI resulted in sensitivity: 0.71, specificity: 0.65, precision: 0.86, accuracy: 0.69, and F-1 score: 0.76 in predicting clinical PD status, and 0.64 accuracy in predicting PD stage, outperforming expert neuropathologist LTS density scoring in terms of sensitivity but not specificity. These findings demonstrate the practical utility of a CNN detector in screening for LTS, which can translate into a computational tool to facilitate the antemortem tissue-based diagnosis of PD in clinical settings.

Blood neurofilament light, Parkinson’s disease, Parkinsonism, and physical function in a longitudinal population study

AbstractBackgroundBlood biomarkers of parkinsonism and Parkinson’s disease (PD) may allow for earlier identification of these conditions and improved longitudinal investigation of PD pathogenesis. Neurofilament light (Nf‐L), a neuronal cytoplasmic protein, is a biomarker of neurodegeneration measurable in CSF or blood. We examined the longitudinal association of serum Nf‐L with clinically diagnosed PD, parkinsonian signs, and physical functioning change over 16 years in a population‐based sample of Black and White older adults.MethodsData came from 1,327 older participants from the Chicago Health and Aging Project (CHAP), a longitudinal population‐based study of common chronic conditions of aging. Clinical evaluations included assessing parkinsonian signs in four domains, bradykinesia, parkinsonian gait, rigidity, and tremors using a structured version of the United Parkinson’s Disease Rating Scale. PD was diagnosed by board certified neurologists. Parkinsonian signs were reported as the four domain scores and a global parkinsonian score (combination of domain scores). Physical function was assessed by performance‐based tests. Serum Nf‐L was measured with an ultrasensitive immunoassay.ResultsAfter adjusting for demographic characteristics, the APOE‐e4 allele, and cognitive function, a 2‐fold higher concentration of serum Nf‐L was associated with the development of clinical PD (OR = 2.54, 95% CI: 1.70, 3.81), and global parkinsonian scores (OR = 2.44, 95% CI: 1.94, 2.94). This association was significant over five years before diagnosis. Compared to participants below 18.5 pg/mL of serum Nf‐L at baseline, participants in between 18.5–25.4 pg/mL, 25.4–37.3 pg/mL, and above 37.3 pg/mL had a higher odds ratio of clinical PD at all time intervals ranging from the time of diagnosis to greater than five years before diagnosis. A higher concentration of serum Nf‐L was associated with a faster decline in physical functioning over the duration of the study. In participants with 2‐fold higher concentrations of serum Nf‐L, the annual rate of decline in physical functioning increased by 0.15 units (95% CI: 0.21, 0.08).ConclusionsSerum Nf‐L predicted the development of clinically diagnosed PD, parkinsonian signs, and physical functioning decline in a population‐based sample. Our findings suggest that Nf‐L may serve as a potential biomarker for physical functioning outcomes in older adults.

LncRNA RMST Regulates Neuronal Apoptosis and Inflammatory Response via Sponging miR-150-5p in Parkinson’s Disease

Introduction: LncRNA rhabdomyosarcoma 2-associated transcript (RMST) serves as a key regulator in neural stem cell fate and is involved in the progression of different neurological diseases. In this research, the serum level and clinical value of RMST in Parkinson’s disease (PD) patients were detected, and the underlying mechanism was explored. Methods: Ninety-nine PD patients and 93 healthy individuals were collected for clinical experiments. SH-SY5Y cells were treated with the dopaminergic neurotoxin 1-methyl-4-phenylpyridinium (MPP⁺) to establish PD cell models. qRT-PCR was used for the detection of mRNA levels. CCK-8 and flow cytometry were used to detect neuronal viability and apoptosis. The target relationship of RMST with miR-15a-5p was confirmed applying luciferase reporter assay. Results: RMST was present at high levels in both serum of PD patients and PD cell models. Serum RMST had a certain clinical value for the diagnosis of PD with the AUC of 0.892 at a cutoff value of 1.225. Serum RMST was positively associated with the levels of TNF-α (r = 0.421, p < 0.001) and IL-1β (r = 0.567, p < 0.001) in PD patients. Knockdown of RMST alleviated the apoptosis and inflammatory response of SH-SY5Y cells induced by MPP⁺. miR-150-5p was the target gene of RMST and less expressed in the clinical serum samples and PD cell models. Conclusion: Serum RMST serves as a promising biomarker for the diagnosis of PD. RMST downregulation may regulate the occurrence and development of PD through inhibiting neuron cell apoptosis and the release of inflammatory cytokines via targeting miR-150-5p.

Further Characterization of Intrastriatal Lipopolysaccharide Model of Parkinson's Disease in C57BL/6 Mice.

Parkinson’s disease (PD) is the most common movement disorder, characterized by progressive degeneration of the nigrostriatal pathway, which consists of dopaminergic cell bodies in substantia nigra and their neuronal projections to the striatum. Moreover, PD is associated with an array of non-motor symptoms such as olfactory dysfunction, gastrointestinal dysfunction, impaired regulation of the sleep-wake cycle, anxiety, depression, and cognitive impairment. Inflammation and concomitant oxidative stress are crucial in the pathogenesis of PD. Thus, this study aimed to model PD via intrastriatal injection of the inflammagen lipopolysaccharide (LPS)to investigate if the lesion causes olfactory and motor impairments, inflammation, oxidative stress, and alteration in synaptic proteins in the olfactory bulb, striatum, and colon. Ten µg of LPS was injected unilaterally into the striatum of 27 male C57BL/6 mice, and behavioural assessment was conducted at 4 and 8 weeks post-treatment, followed by tissue collection. Intrastriatal LPS induced motor impairment in C57BL/6 mice at 8 weeks post-treatment evidenced by reduced latency time in the rotarod test. LPS also induced inflammation in the striatum characterized by increased expression of microglial marker Iba-1 and astrocytic marker GFAP, with degeneration of dopaminergic neuronal fibres (reduced tyrosine hydroxylase immunoreactivity), and reduction of synaptic proteins and DJ-1 protein. Additionally, intrastriatal LPS induced inflammation, oxidative stress and alterations in synaptic proteins within the olfactory bulb, although this did not induce a significant impairment in olfactory function. Intrastriatal LPS induced mild inflammatory changes in the distal colon, accompanied by increased protein expression of 3-nitrotyrosine-modified proteins. This model recapitulated the major features of PD such as motor impairment and degeneration of dopaminergic neuronal fibres in the striatum, as well as some pathological changes in the olfactory bulb and colon; thus, this model could be suitable for understanding clinical PD and testing neuroprotective strategies.

Acute MPTP Treatment Impairs Dendritic Spine Density in the Mouse Hippocampus.

Among the animal models of Parkinson’s disease (PD), the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-lesioned mouse model has shown both dopaminergic (DA) damage and related motor control defects, as observed in patients with PD. Recent studies have suggested that the DA system interacts with the synaptic plasticity of the hippocampus in PD. However, little is known about how alterations in the hippocampal structural plasticity are affected by the DA damage in MPTP-lesioned models. In the present study, we investigated alterations in dendritic complexity and spine density in the mouse hippocampus following acute MPTP treatment (22 mg/kg, intraperitoneally, four times/day, 2-h intervals). We confirmed that acute MPTP treatment significantly decreased initial motor function and persistently reduced the number of tyrosine hydroxylase-positive DA neurons in the substantia nigra. Golgi staining showed that acute MPTP treatment significantly reduced the spine density of neuronal dendrites in the cornu ammonis 1 (CA1) apical/basal and dentate gyrus (DG) subregions of the mouse hippocampus at 8 and 16 days after treatment, although it did not affect dendritic complexity (e.g., number of crossing dendrites, total dendritic length, and branch points per neuron) in both CA1 and DG subregions at all time points after treatment. Therefore, the present study provides anatomical evidence that acute MPTP treatment affects synaptic structure in the hippocampus during the late phase after acute MPTP treatment in mice, independent of any changes in the dendritic arborization of hippocampal neurons. These findings offer data for the ability of the acute MPTP-lesioned mouse model to replicate the non-nigrostriatal lesions of clinical PD.

Neuroimaging Assessment of Nigrosome 1 with a Multiecho Gre Magnetic Resonance Sequence in the Differentiation Between Parkinsons Disease from Essential Tremor and Healthy Individuals.

Background and purpose:Parkinsonism is commonly seen in many clinical conditions, and the establishment of its etiology may take many years. The possible development of neuroprotective treatments for Parkinsons disease (PD) in the near future will require correct and early diagnosis. This study aims to analyze the accuracy of a low-cost MRI sequence to differentiate PD from patients with essential tremor (ET) and healthy control (HC) individuals.Material and methods:We recruited 70 individuals with clinical diagnoses of PD (38 patients), ET (11 patients) and healthy volunteers (21 individuals), all of whom underwent 3T MRI multiecho GRE sequence. Two blinded neuroradiologists independently evaluated the presence or absence of nigrosome-1(N1). We considered the unilateral or bilateral absence of nigrosome 1 signal as indicative of PD.Results:The absence of at least one N1 could differentiate with 98% accuracy patients with clinical established PD from healthy controls. The presence of both nigrosomes was 96% accurate as a sign to differentiate PD from ET patients.Conclusion:The 3T MRI with multiecho GRE is a simple and universally available technique and it can be used as a good auxiliary tool to differentiate PD from ET patients and controls.