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Yang YL, Lin TK, Huang YH. MiR-29a efficiently suppresses the generation of reactive oxygen species and α-synuclein in a cellular model of Parkinson's disease by potentially targeting GSK-3β. Eur J Pharmacol 2024; 974:176615. [PMID: 38685306 DOI: 10.1016/j.ejphar.2024.176615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 04/04/2024] [Accepted: 04/25/2024] [Indexed: 05/02/2024]
Abstract
MicroRNA-29a (miR-29a) has been suggested to serve a potential protective function against Parkinson's disease (PD); however, the exact molecular mechanisms remain elusive. This study explored the protective role of miR-29a in a cellular model of PD using SH-SY5Y cell lines through iTRAQ-based quantitative proteomic and biochemistry analysis. The findings showed that using a miR-29a mimic in SH-SY5Y cells treated with 1-methyl-4-phenylpyridinium (MPP+) significantly decreased cell death and increased mitochondrial membrane potential. It also reduced mitochondrial reactive oxygen species (ROS) and the production of α-synuclein. Subsequent heatmap analysis using iTRAQ-based quantitative proteomics revealed remarkably contrasting protein expression profiles for 882 genes when comparing the groups treated with miR-29a mimic plus MPP + against the control group treated solely with MPP+. The KEGG pathway analysis of these 882 genes indicated the substantial role of miR-29a in the PD pathway (P = 1.58x10-5) and highlighted its function in mitochondrial genes. Furthermore, treatment with a miR-29a mimic in SH-SY5Y cells reduced the levels of GSK-3β, phosphorylated GSK-3β, and cleaved caspase-7 following exposure to MPP+. The miR-29a mimic also upregulated the expressions of α-synuclein clearance proteins FYCO1 and Rab7 in this cellular PD model, thereby inhibiting the production of α-synuclein. Luciferase activity analysis confirmed the specific binding of miR-29a to the 3' untranslated region (3'UTR) of GSK-3β, leading to its repression. Our findings demonstrated miR-29a's neuroprotective role in mitochondrial function and highlighted its potential to inhibit ROS and α-synuclein production, offering possible therapeutic avenues for PD treatment.
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Affiliation(s)
- Ya-Ling Yang
- Department of Anesthesiology, Kaohsiung Chang Gung Memorial Hospital, and Chang Gung University College of Medicine, Kaohsiung, 83301, Taiwan
| | - Tsu-Kung Lin
- Center for Mitochondrial Research and Medicine, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, 83301, Taiwan; Department of Neurology, Kaohsiung Chang Gung Memorial Hospital, and Chang Gung University College of Medicine, Kaohsiung, 83301, Taiwan; Center of Parkinson's Disease, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, 83301, Taiwan
| | - Ying-Hsien Huang
- Kawasaki Disease Center, Kaohsiung Chang Gung Memorial Hospital, and Chang, Gung University College of Medicine, Kaohsiung, 83301, Taiwan; Department of Pediatrics, Kaohsiung Chang Gung Memorial Hospital, and Chang, Gung University College of Medicine, Kaohsiung, 83301, Taiwan.
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Abstract
The cerebellum plays an important role in movement disorders, specifically in symptoms of ataxia, tremor, and dystonia. Understanding the physiological signals of the cerebellum contributes to insights into the pathophysiology of these movement disorders and holds promise in advancing therapeutic development. Non-invasive techniques such as electroencephalogram and magnetoencephalogram can record neural signals with high temporal resolution at the millisecond level, which is uniquely suitable to interrogate cerebellar physiology. These techniques have recently been implemented to study cerebellar physiology in healthy subjects as well as individuals with movement disorders. In the present review, we focus on the current understanding of cerebellar physiology using these techniques to study movement disorders.
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Affiliation(s)
- Ami Kumar
- Department of Neurology, Columbia University Irving Medical Center and the New York Presbyterian Hospital, 650 W 168thStreet, Room 305, New York, NY, 10032, USA
- Initiative for Columbia Ataxia and Tremor, Columbia University Irving Medical Center, New York, NY, USA
| | - Chih-Chun Lin
- Department of Neurology, Columbia University Irving Medical Center and the New York Presbyterian Hospital, 650 W 168thStreet, Room 305, New York, NY, 10032, USA
- Initiative for Columbia Ataxia and Tremor, Columbia University Irving Medical Center, New York, NY, USA
| | - Sheng-Han Kuo
- Department of Neurology, Columbia University Irving Medical Center and the New York Presbyterian Hospital, 650 W 168thStreet, Room 305, New York, NY, 10032, USA.
- Initiative for Columbia Ataxia and Tremor, Columbia University Irving Medical Center, New York, NY, USA.
| | - Ming-Kai Pan
- Cerebellar Research Center, National Taiwan University Hospital, Yun-Lin Branch, Yun-Lin, 64041, Taiwan.
- Department and Graduate Institute of Pharmacology, National Taiwan University College of Medicine, Taipei, 10051, Taiwan.
- Department of Medical Research, National Taiwan University Hospital, Taipei, 10002, Taiwan.
- Institute of Biomedical Sciences, Academia Sinica, Taipei City, 11529, Taiwan.
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King JT, John AR, Wang YK, Shih CK, Zhang D, Huang KC, Lin CT. Brain Connectivity Changes During Bimanual and Rotated Motor Imagery. IEEE J Transl Eng Health Med 2022; 10:2100408. [PMID: 35492507 PMCID: PMC9041539 DOI: 10.1109/jtehm.2022.3167552] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 01/24/2022] [Accepted: 04/03/2022] [Indexed: 11/10/2022]
Abstract
Motor imagery-based brain-computer interface (MI-BCI) currently represents a new trend in rehabilitation. However, individual differences in the responsive frequency bands and a poor understanding of the communication between the ipsilesional motor areas and other regions limit the use of MI-BCI therapy. Objective: Bimanual training has recently attracted attention as it achieves better outcomes as compared to repetitive one-handed training. This study compared the effects of three MI tasks with different visual feedback. Methods: Fourteen healthy subjects performed single hand motor imagery tasks while watching single static hand (traditional MI), single hand with rotation movement (rmMI), and bimanual coordination with a hand pedal exerciser (bcMI). Functional connectivity is estimated by Transfer Entropy (TE) analysis for brain information flow. Results: Brain connectivity of conducting three MI tasks showed that the bcMI demonstrated increased communications from the parietal to the bilateral prefrontal areas and increased contralateral connections between motor-related zones and spatial processing regions. Discussion/Conclusion: The results revealed bimanual coordination operation events increased spatial information and motor planning under the motor imagery task. And the proposed bimanual coordination MI-BCI (bcMI-BCI) can also achieve the effect of traditional motor imagery tasks and promotes more effective connections with different brain regions to better integrate motor-cortex functions for aiding the development of more effective MI-BCI therapy. Clinical and Translational Impact Statement The proposed bcMI-BCI provides more effective connections with different brain areas and integrates motor-cortex functions to promote motor imagery rehabilitation for patients’ impairment.
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Affiliation(s)
- Jung-Tai King
- Brain Research Center, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Alka Rachel John
- CIBCI Laboratory, Australian AI Institute, FEIT, University of Technology Sydney, Ultimo, NSW, Australia
| | - Yu-Kai Wang
- CIBCI Laboratory, Australian AI Institute, FEIT, University of Technology Sydney, Ultimo, NSW, Australia
| | - Chun-Kai Shih
- Brain Research Center, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Dingguo Zhang
- Department of Electronic and Electrical Engineering, University of Bath, Bath, U.K
| | - Kuan-Chih Huang
- Brain Research Center, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Chin-Teng Lin
- Brain Research Center, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
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San-Segundo R, Zhang A, Cebulla A, Panev S, Tabor G, Stebbins K, Massa RE, Whitford A, de la Torre F, Hodgins J. Parkinson's Disease Tremor Detection in the Wild Using Wearable Accelerometers. Sensors (Basel) 2020; 20:E5817. [PMID: 33066691 PMCID: PMC7602495 DOI: 10.3390/s20205817] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 09/27/2020] [Accepted: 10/05/2020] [Indexed: 12/18/2022]
Abstract
Continuous in-home monitoring of Parkinson's Disease (PD) symptoms might allow improvements in assessment of disease progression and treatment effects. As a first step towards this goal, we evaluate the feasibility of a wrist-worn wearable accelerometer system to detect PD tremor in the wild (uncontrolled scenarios). We evaluate the performance of several feature sets and classification algorithms for robust PD tremor detection in laboratory and wild settings. We report results for both laboratory data with accurate labels and wild data with weak labels. The best performance was obtained using a combination of a pre-processing module to extract information from the tremor spectrum (based on non-negative factorization) and a deep neural network for learning relevant features and detecting tremor segments. We show how the proposed method is able to predict patient self-report measures, and we propose a new metric for monitoring PD tremor (i.e., percentage of tremor over long periods of time), which may be easier to estimate the start and end time points of each tremor event while still providing clinically useful information.
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Affiliation(s)
- Rubén San-Segundo
- Center for Information Processing and Telecommunications, Universidad Politécnica de Madrid, 28040 Madrid, Spain
| | - Ada Zhang
- Human Sensing Laboratory, Carnegie Mellon University, Pittsburgh, PA 15213, USA; (A.Z.); (A.C.); (S.P.); (G.T.); (K.S.); (A.W.); (F.d.l.T.); (J.H.)
| | - Alexander Cebulla
- Human Sensing Laboratory, Carnegie Mellon University, Pittsburgh, PA 15213, USA; (A.Z.); (A.C.); (S.P.); (G.T.); (K.S.); (A.W.); (F.d.l.T.); (J.H.)
| | - Stanislav Panev
- Human Sensing Laboratory, Carnegie Mellon University, Pittsburgh, PA 15213, USA; (A.Z.); (A.C.); (S.P.); (G.T.); (K.S.); (A.W.); (F.d.l.T.); (J.H.)
| | - Griffin Tabor
- Human Sensing Laboratory, Carnegie Mellon University, Pittsburgh, PA 15213, USA; (A.Z.); (A.C.); (S.P.); (G.T.); (K.S.); (A.W.); (F.d.l.T.); (J.H.)
| | - Katelyn Stebbins
- Human Sensing Laboratory, Carnegie Mellon University, Pittsburgh, PA 15213, USA; (A.Z.); (A.C.); (S.P.); (G.T.); (K.S.); (A.W.); (F.d.l.T.); (J.H.)
| | | | - Andrew Whitford
- Human Sensing Laboratory, Carnegie Mellon University, Pittsburgh, PA 15213, USA; (A.Z.); (A.C.); (S.P.); (G.T.); (K.S.); (A.W.); (F.d.l.T.); (J.H.)
| | - Fernando de la Torre
- Human Sensing Laboratory, Carnegie Mellon University, Pittsburgh, PA 15213, USA; (A.Z.); (A.C.); (S.P.); (G.T.); (K.S.); (A.W.); (F.d.l.T.); (J.H.)
| | - Jessica Hodgins
- Human Sensing Laboratory, Carnegie Mellon University, Pittsburgh, PA 15213, USA; (A.Z.); (A.C.); (S.P.); (G.T.); (K.S.); (A.W.); (F.d.l.T.); (J.H.)
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Boscolo Galazzo I, Magrinelli F, Pizzini FB, Storti SF, Agosta F, Filippi M, Marotta A, Mansueto G, Menegaz G, Tinazzi M. Voxel-based morphometry and task functional magnetic resonance imaging in essential tremor: evidence for a disrupted brain network. Sci Rep 2020; 10:15061. [PMID: 32934259 PMCID: PMC7493988 DOI: 10.1038/s41598-020-69514-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Accepted: 07/13/2020] [Indexed: 11/09/2022] Open
Abstract
The pathophysiology of essential tremor (ET) is controversial and might be further elucidated by advanced neuroimaging. Focusing on homogenous ET patients diagnosed according to the 2018 consensus criteria, this study aimed to: (1) investigate whether task functional MRI (fMRI) can identify networks of activated and deactivated brain areas, (2) characterize morphometric and functional modulations, relative to healthy controls (HC). Ten ET patients and ten HC underwent fMRI while performing two motor tasks with their upper limb: (1) maintaining a posture (both groups); (2) simulating tremor (HC only). Activations/deactivations were obtained from General Linear Model and compared across groups/tasks. Voxel-based morphometry and linear regressions between clinical and fMRI data were also performed. Few cerebellar clusters of gray matter loss were found in ET. Conversely, widespread fMRI alterations were shown. Tremor in ET (task 1) was associated with extensive deactivations mainly involving the cerebellum, sensory-motor cortex, and basal ganglia compared to both tasks in HC, and was negatively correlated with clinical tremor scales. Homogeneous ET patients demonstrated deactivation patterns during tasks triggering tremor, encompassing a network of cortical and subcortical regions. Our results point towards a marked cerebellar involvement in ET pathophysiology and the presence of an impaired cerebello-thalamo-cortical tremor network.
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Affiliation(s)
- Ilaria Boscolo Galazzo
- Department of Computer Science, University of Verona, Strada Le Grazie 15, Ca' Vignal 2, 37134, Verona, Italy.
| | - Francesca Magrinelli
- Department of Neurosciences, Biomedicine and Movement Sciences, Neurology Section, University of Verona, Piazzale L.A. Scuro 10, 37134, Verona, Italy.
| | | | - Silvia Francesca Storti
- Department of Computer Science, University of Verona, Strada Le Grazie 15, Ca' Vignal 2, 37134, Verona, Italy
| | - Federica Agosta
- Neuroimaging Research Unit, Division of Neuroscience, Institute of Experimental Neurology, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, Institute of Experimental Neurology, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Angela Marotta
- Department of Neurosciences, Biomedicine and Movement Sciences, Neurology Section, University of Verona, Piazzale L.A. Scuro 10, 37134, Verona, Italy
| | - Giancarlo Mansueto
- Department of Diagnostics and Pathology, University of Verona, Verona, Italy
| | - Gloria Menegaz
- Department of Computer Science, University of Verona, Strada Le Grazie 15, Ca' Vignal 2, 37134, Verona, Italy
| | - Michele Tinazzi
- Department of Neurosciences, Biomedicine and Movement Sciences, Neurology Section, University of Verona, Piazzale L.A. Scuro 10, 37134, Verona, Italy
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Gutiérrez-de Pablo V, Gómez C, Poza J, Maturana-Candelas A, Martins S, Gomes I, Lopes AM, Pinto N, Hornero R. Relationship between the Presence of the ApoE ε4 Allele and EEG Complexity along the Alzheimer's Disease Continuum. Sensors (Basel) 2020; 20:E3849. [PMID: 32664228 PMCID: PMC7411888 DOI: 10.3390/s20143849] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Revised: 06/29/2020] [Accepted: 07/08/2020] [Indexed: 12/15/2022]
Abstract
Alzheimer's disease (AD) is the most prevalent cause of dementia, being considered a major health problem, especially in developed countries. Late-onset AD is the most common form of the disease, with symptoms appearing after 65 years old. Genetic determinants of AD risk are vastly unknown, though, ε 4 allele of the ApoE gene has been reported as the strongest genetic risk factor for AD. The objective of this study was to analyze the relationship between brain complexity and the presence of ApoE ε 4 alleles along the AD continuum. For this purpose, resting-state electroencephalography (EEG) activity was analyzed by computing Lempel-Ziv complexity (LZC) from 46 healthy control subjects, 49 mild cognitive impairment subjects, 45 mild AD patients, 44 moderate AD patients and 33 severe AD patients, subdivided by ApoE status. Subjects with one or more ApoE ε 4 alleles were included in the carriers subgroups, whereas the ApoE ε 4 non-carriers subgroups were formed by subjects without any ε 4 allele. Our results showed that AD continuum is characterized by a progressive complexity loss. No differences were observed between AD ApoE ε 4 carriers and non-carriers. However, brain activity from healthy subjects with ApoE ε 4 allele (carriers subgroup) is more complex than from non-carriers, mainly in left temporal, frontal and posterior regions (p-values < 0.05, FDR-corrected Mann-Whitney U-test). These results suggest that the presence of ApoE ε 4 allele could modify the EEG complexity patterns in different brain regions, as the temporal lobes. These alterations might be related to anatomical changes associated to neurodegeneration, increasing the risk of suffering dementia due to AD before its clinical onset. This interesting finding might help to advance in the development of new tools for early AD diagnosis.
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Affiliation(s)
- Víctor Gutiérrez-de Pablo
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, Universidad de Valladolid, 47011 Valladolid, Spain; (V.G.-d.P.); (J.P.); (A.M.-C.); (R.H.)
| | - Carlos Gómez
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, Universidad de Valladolid, 47011 Valladolid, Spain; (V.G.-d.P.); (J.P.); (A.M.-C.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), 28029 Madrid, Spain
| | - Jesús Poza
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, Universidad de Valladolid, 47011 Valladolid, Spain; (V.G.-d.P.); (J.P.); (A.M.-C.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), 28029 Madrid, Spain
- Instituto de Investigación en Matemáticas (IMUVA), Universidad de Valladolid, 47011 Valladolid, Spain
| | - Aarón Maturana-Candelas
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, Universidad de Valladolid, 47011 Valladolid, Spain; (V.G.-d.P.); (J.P.); (A.M.-C.); (R.H.)
| | - Sandra Martins
- Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), 4200-135 Porto, Portugal; (S.M.); (I.G.); (A.M.L.); (N.P.)
- Institute of Research and Innovation in Health (i3S), University of Porto, 4200-135 Porto, Portugal
| | - Iva Gomes
- Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), 4200-135 Porto, Portugal; (S.M.); (I.G.); (A.M.L.); (N.P.)
- Institute of Research and Innovation in Health (i3S), University of Porto, 4200-135 Porto, Portugal
| | - Alexandra M. Lopes
- Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), 4200-135 Porto, Portugal; (S.M.); (I.G.); (A.M.L.); (N.P.)
- Institute of Research and Innovation in Health (i3S), University of Porto, 4200-135 Porto, Portugal
| | - Nádia Pinto
- Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), 4200-135 Porto, Portugal; (S.M.); (I.G.); (A.M.L.); (N.P.)
- Institute of Research and Innovation in Health (i3S), University of Porto, 4200-135 Porto, Portugal
- Center of Mathematics of the University of Porto (CMUP), 4169-007 Porto, Portugal
| | - Roberto Hornero
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, Universidad de Valladolid, 47011 Valladolid, Spain; (V.G.-d.P.); (J.P.); (A.M.-C.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), 28029 Madrid, Spain
- Instituto de Investigación en Matemáticas (IMUVA), Universidad de Valladolid, 47011 Valladolid, Spain
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