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Povala G, De Bastiani MA, Bellaver B, Ferreira PCL, Ferrari-Souza JP, Lussier FZ, Souza DO, Rosa-Neto P, Pascoal TA, Zatt B, Zimmer ER. Omics-derived biological modules reflect metabolic brain changes in Alzheimer's disease. Alzheimers Dement 2024. [PMID: 39140361 DOI: 10.1002/alz.14095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 05/13/2024] [Accepted: 05/29/2024] [Indexed: 08/15/2024]
Abstract
INTRODUCTION Brain glucose hypometabolism, indexed by the fluorodeoxyglucose positron emission tomography ([18F]FDG-PET) imaging, is a metabolic signature of Alzheimer's disease (AD). However, the underlying biological pathways involved in these metabolic changes remain elusive. METHODS Here, we integrated [18F]FDG-PET images with blood and hippocampal transcriptomic data from cognitively unimpaired (CU, n = 445) and cognitively impaired (CI, n = 749) individuals using modular dimension reduction techniques and voxel-wise linear regression analysis. RESULTS Our results showed that multiple transcriptomic modules are associated with brain [18F]FDG-PET metabolism, with the top hits being a protein serine/threonine kinase activity gene cluster (peak-t(223) = 4.86, P value < 0.001) and zinc-finger-related regulatory units (peak-t(223) = 3.90, P value < 0.001). DISCUSSION By integrating transcriptomics with PET imaging data, we identified that serine/threonine kinase activity-associated genes and zinc-finger-related regulatory units are highly associated with brain metabolic changes in AD. HIGHLIGHTS We conducted an integrated analysis of system-based transcriptomics and fluorodeoxyglucose positron emission tomography ([18F]FDG-PET) at the voxel level in Alzheimer's disease (AD). The biological process of serine/threonine kinase activity was the most associated with [18F]FDG-PET in the AD brain. Serine/threonine kinase activity alterations are associated with brain vulnerable regions in AD [18F]FDG-PET. Zinc-finger transcription factor targets were associated with AD brain [18F]FDG-PET metabolism.
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Affiliation(s)
- Guilherme Povala
- Graduate Program in Biological Sciences: Biochemistry, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- Graduate Program in Computing, Universidade Federal de Pelotas (UFPEL), Porto Alegre, Brazil
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Marco Antônio De Bastiani
- Graduate Program in Biological Sciences: Biochemistry, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
| | - Bruna Bellaver
- Graduate Program in Biological Sciences: Biochemistry, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Pamela C L Ferreira
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - João Pedro Ferrari-Souza
- Graduate Program in Biological Sciences: Biochemistry, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
| | - Firoza Z Lussier
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Diogo O Souza
- Graduate Program in Biological Sciences: Biochemistry, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- Department of Biochemistry, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Montreal, Quebec, Canada
| | - Tharick A Pascoal
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Department of Neurology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Bruno Zatt
- Graduate Program in Computing, Universidade Federal de Pelotas (UFPEL), Porto Alegre, Brazil
| | - Eduardo R Zimmer
- Graduate Program in Biological Sciences: Biochemistry, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- Department of Pharmacology, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- Graduate Program in Biological Sciences: Pharmacology and Therapeutics, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- Brain Institute of Rio Grande do Sul, Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil
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2
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Su C, Hou Y, Xu J, Xu Z, Zhou M, Ke A, Li H, Xu J, Brendel M, Maasch JRMA, Bai Z, Zhang H, Zhu Y, Cincotta MC, Shi X, Henchcliffe C, Leverenz JB, Cummings J, Okun MS, Bian J, Cheng F, Wang F. Identification of Parkinson's disease PACE subtypes and repurposing treatments through integrative analyses of multimodal data. NPJ Digit Med 2024; 7:184. [PMID: 38982243 PMCID: PMC11233682 DOI: 10.1038/s41746-024-01175-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Accepted: 06/21/2024] [Indexed: 07/11/2024] Open
Abstract
Parkinson's disease (PD) is a serious neurodegenerative disorder marked by significant clinical and progression heterogeneity. This study aimed at addressing heterogeneity of PD through integrative analysis of various data modalities. We analyzed clinical progression data (≥5 years) of individuals with de novo PD using machine learning and deep learning, to characterize individuals' phenotypic progression trajectories for PD subtyping. We discovered three pace subtypes of PD exhibiting distinct progression patterns: the Inching Pace subtype (PD-I) with mild baseline severity and mild progression speed; the Moderate Pace subtype (PD-M) with mild baseline severity but advancing at a moderate progression rate; and the Rapid Pace subtype (PD-R) with the most rapid symptom progression rate. We found cerebrospinal fluid P-tau/α-synuclein ratio and atrophy in certain brain regions as potential markers of these subtypes. Analyses of genetic and transcriptomic profiles with network-based approaches identified molecular modules associated with each subtype. For instance, the PD-R-specific module suggested STAT3, FYN, BECN1, APOA1, NEDD4, and GATA2 as potential driver genes of PD-R. It also suggested neuroinflammation, oxidative stress, metabolism, PI3K/AKT, and angiogenesis pathways as potential drivers for rapid PD progression (i.e., PD-R). Moreover, we identified repurposable drug candidates by targeting these subtype-specific molecular modules using network-based approach and cell line drug-gene signature data. We further estimated their treatment effects using two large-scale real-world patient databases; the real-world evidence we gained highlighted the potential of metformin in ameliorating PD progression. In conclusion, this work helps better understand clinical and pathophysiological complexity of PD progression and accelerate precision medicine.
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Grants
- R21 AG083003 NIA NIH HHS
- R01 AG082118 NIA NIH HHS
- R56 AG074001 NIA NIH HHS
- R01AG076448 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- RF1AG072449 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- MJFF-023081 Michael J. Fox Foundation for Parkinson's Research (Michael J. Fox Foundation)
- R01AG080991 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- P30 AG072959 NIA NIH HHS
- 3R01AG066707-01S1 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R21AG083003 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R01AG066707 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R35 AG071476 NIA NIH HHS
- RF1 AG082211 NIA NIH HHS
- R56AG074001 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R01AG082118 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R25 AG083721 NIA NIH HHS
- RF1AG082211 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- U01 NS093334 NINDS NIH HHS
- AG083721-01 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- RF1NS133812 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- P20GM109025 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- RF1 NS133812 NINDS NIH HHS
- R35AG71476 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- U01 AG073323 NIA NIH HHS
- R01 AG066707 NIA NIH HHS
- R01AG053798 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R01AG076234 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R01 AG076448 NIA NIH HHS
- R01 AG080991 NIA NIH HHS
- R01 AG076234 NIA NIH HHS
- U01NS093334 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- P20 GM109025 NIGMS NIH HHS
- P30AG072959 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- RF1 AG072449 NIA NIH HHS
- R01 AG053798 NIA NIH HHS
- 3R01AG066707-02S1 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- U01AG073323 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- ALZDISCOVERY-1051936 Alzheimer's Association
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Affiliation(s)
- Chang Su
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Yu Hou
- Department of Surgery, University of Minnesota, Minneapolis, MN, USA
| | - Jielin Xu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Zhenxing Xu
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Manqi Zhou
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Alison Ke
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Haoyang Li
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Jie Xu
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Matthew Brendel
- Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Jacqueline R M A Maasch
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Department of Computer Science, Cornell Tech, Cornell University, New York, NY, USA
| | - Zilong Bai
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Haotan Zhang
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Yingying Zhu
- Department of Computer Science, University of Texas at Arlington, Arlington, TX, USA
| | - Molly C Cincotta
- Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA
| | - Xinghua Shi
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA
| | - Claire Henchcliffe
- Department of Neurology, University of California Irvine, Irvine, CA, USA
| | - James B Leverenz
- Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Pam Quirk Brain Health and Biomarker Laboratory, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas, Las Vegas, NV, USA
| | - Michael S Okun
- Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA.
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA.
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Shulskaya MV, Semenova EI, Rudenok MM, Partevian SA, Lukashevich MV, Karabanov AV, Fedotova EY, Illarioshkin SN, Slominsky PA, Shadrina MI, Alieva AK. Analysis of LRRN3, MEF2C, SLC22A, and P2RY12 Gene Expression in the Peripheral Blood of Patients in the Early Stages of Parkinson's Disease. Biomedicines 2024; 12:1391. [PMID: 39061965 PMCID: PMC11273708 DOI: 10.3390/biomedicines12071391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 05/24/2024] [Accepted: 06/11/2024] [Indexed: 07/28/2024] Open
Abstract
Parkinson's disease (PD) is one of the most common human neurodegenerative diseases. Belated diagnoses of PD and late treatment are caused by its elongated prodromal phase. Thus, searching for new candidate genes participating in the development of the pathological process in the early stages of the disease in patients who have not yet received therapy is relevant. Changes in mRNA and protein levels have been described both in the peripheral blood and in the brain of patients with PD. Thus, analysis of changes in the mRNA expression in peripheral blood is of great importance in studying the early stages of PD. This work aimed to analyze the changes in MEF2C, SLC22A4, P2RY12, and LRRN3 gene expression in the peripheral blood of patients in the early stages of PD. We found a statistically relevant and PD-specific change in the expression of the LRRN3 gene, indicating a disruption in the processes of neuronal regeneration and the functioning of synapses. The data obtained during the study indicate that this gene can be considered a potential biomarker of the early stages of PD.
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Affiliation(s)
- Marina V Shulskaya
- Laboratory of Molecular Genetics of Hereditary Diseases, National Research Center "Kurchatov Institute", Kurchatova pl., 2, Moscow 123082, Russia
| | - Ekaterina I Semenova
- Laboratory of Molecular Genetics of Hereditary Diseases, National Research Center "Kurchatov Institute", Kurchatova pl., 2, Moscow 123082, Russia
| | - Margarita M Rudenok
- Laboratory of Molecular Genetics of Hereditary Diseases, National Research Center "Kurchatov Institute", Kurchatova pl., 2, Moscow 123082, Russia
| | - Suzanna A Partevian
- Laboratory of Molecular Genetics of Hereditary Diseases, National Research Center "Kurchatov Institute", Kurchatova pl., 2, Moscow 123082, Russia
| | - Maria V Lukashevich
- Laboratory of Molecular Genetics of Hereditary Diseases, National Research Center "Kurchatov Institute", Kurchatova pl., 2, Moscow 123082, Russia
| | - Alexei V Karabanov
- Federal State Scientific Institution, Scientific Center of Neurology, Russian Academy of Sciences (RAS), Volokolamskoye sh., 80, Moscow 125367, Russia
| | - Ekaterina Yu Fedotova
- Federal State Scientific Institution, Scientific Center of Neurology, Russian Academy of Sciences (RAS), Volokolamskoye sh., 80, Moscow 125367, Russia
| | - Sergey N Illarioshkin
- Federal State Scientific Institution, Scientific Center of Neurology, Russian Academy of Sciences (RAS), Volokolamskoye sh., 80, Moscow 125367, Russia
| | - Petr A Slominsky
- Laboratory of Molecular Genetics of Hereditary Diseases, National Research Center "Kurchatov Institute", Kurchatova pl., 2, Moscow 123082, Russia
| | - Maria I Shadrina
- Laboratory of Molecular Genetics of Hereditary Diseases, National Research Center "Kurchatov Institute", Kurchatova pl., 2, Moscow 123082, Russia
| | - Anelya Kh Alieva
- Laboratory of Molecular Genetics of Hereditary Diseases, National Research Center "Kurchatov Institute", Kurchatova pl., 2, Moscow 123082, Russia
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4
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Chew G, Mai AS, Ouyang JF, Qi Y, Chao Y, Wang Q, Petretto E, Tan EK. Transcriptomic imputation of genetic risk variants uncovers novel whole-blood biomarkers of Parkinson's disease. NPJ Parkinsons Dis 2024; 10:99. [PMID: 38719867 PMCID: PMC11078960 DOI: 10.1038/s41531-024-00698-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Accepted: 03/28/2024] [Indexed: 05/12/2024] Open
Abstract
Blood-based gene expression signatures could potentially be used as biomarkers for PD. However, it is unclear whether genetically-regulated transcriptomic signatures can provide novel gene candidates for use as PD biomarkers. We leveraged on the Genotype-Tissue Expression (GTEx) database to impute whole-blood transcriptomic expression using summary statistics of three large-scale PD GWAS. A random forest classifier was used with the consensus whole-blood imputed gene signature (IGS) to discriminate between cases and controls. Outcome measures included Area under the Curve (AUC) of Receiver Operating Characteristic (ROC) Curve. We demonstrated that the IGS (n = 37 genes) is conserved across PD GWAS studies and brain tissues. IGS discriminated between cases and controls in an independent whole-blood RNA-sequencing study (1176 PD, 254 prodromal, and 860 healthy controls) with mean AUC and accuracy of 64.8% and 69.4% for PD cohort, and 78.8% and 74% for prodromal cohort. PATL2 was the top-performing imputed gene in both PD and prodromal PD cohorts, whose classifier performance varied with biological sex (higher performance for males and females in the PD and prodromal PD, respectively). Single-cell RNA-sequencing studies (scRNA-seq) of healthy humans and PD patients found PATL2 to be enriched in terminal effector CD8+ and cytotoxic CD4+ cells, whose proportions are both increased in PD patients. We demonstrated the utility of GWAS transcriptomic imputation in identifying novel whole-blood transcriptomic signatures which could be leveraged upon for PD biomarker derivation. We identified PATL2 as a potential biomarker in both clinical and prodromic PD.
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Affiliation(s)
- Gabriel Chew
- Duke-National University of Singapore Medical School, Singapore, Singapore
- Department of Neurology, National Neuroscience Institute, Singapore, Singapore
| | - Aaron Shengting Mai
- Department of Neurology, National Neuroscience Institute, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - John F Ouyang
- Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Yueyue Qi
- Duke-National University of Singapore Medical School, Singapore, Singapore
- Department of Neurology, National Neuroscience Institute, Singapore, Singapore
| | - Yinxia Chao
- Duke-National University of Singapore Medical School, Singapore, Singapore
- Department of Neurology, National Neuroscience Institute, Singapore, Singapore
- Department of Neurology, Singapore General Hospital, Singapore, Singapore
| | - Qing Wang
- Department of Neurology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Enrico Petretto
- Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Eng-King Tan
- Duke-National University of Singapore Medical School, Singapore, Singapore.
- Department of Neurology, National Neuroscience Institute, Singapore, Singapore.
- Department of Neurology, Singapore General Hospital, Singapore, Singapore.
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5
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Sahu M, Vashishth S, Kukreti N, Gulia A, Russell A, Ambasta RK, Kumar P. Synergizing drug repurposing and target identification for neurodegenerative diseases. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:111-169. [PMID: 38789177 DOI: 10.1016/bs.pmbts.2024.03.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
Despite dedicated research efforts, the absence of disease-curing remedies for neurodegenerative diseases (NDDs) continues to jeopardize human society and stands as a challenge. Drug repurposing is an attempt to find new functionality of existing drugs and take it as an opportunity to discourse the clinically unmet need to treat neurodegeneration. However, despite applying this approach to rediscover a drug, it can also be used to identify the target on which a drug could work. The primary objective of target identification is to unravel all the possibilities of detecting a new drug or repurposing an existing drug. Lately, scientists and researchers have been focusing on specific genes, a particular site in DNA, a protein, or a molecule that might be involved in the pathogenesis of the disease. However, the new era discusses directing the signaling mechanism involved in the disease progression, where receptors, ion channels, enzymes, and other carrier molecules play a huge role. This review aims to highlight how target identification can expedite the whole process of drug repurposing. Here, we first spot various target-identification methods and drug-repositioning studies, including drug-target and structure-based identification studies. Moreover, we emphasize various drug repurposing approaches in NDDs, namely, experimental-based, mechanism-based, and in silico approaches. Later, we draw attention to validation techniques and stress on drugs that are currently undergoing clinical trials in NDDs. Lastly, we underscore the future perspective of synergizing drug repurposing and target identification in NDDs and present an unresolved question to address the issue.
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Affiliation(s)
- Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Shrutikirti Vashishth
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Neha Kukreti
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Ashima Gulia
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Ashish Russell
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Rashmi K Ambasta
- Department of Biotechnology and Microbiology, SRM University, Sonepat, Haryana, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India.
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Xu Q, Jiang S, Kang R, Wang Y, Zhang B, Tian J. Deciphering the molecular pathways underlying dopaminergic neuronal damage in Parkinson's disease associated with SARS-CoV-2 infection. Comput Biol Med 2024; 171:108200. [PMID: 38428099 DOI: 10.1016/j.compbiomed.2024.108200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 01/24/2024] [Accepted: 02/18/2024] [Indexed: 03/03/2024]
Abstract
BACKGROUND The COVID-19 pandemic caused by SARS-CoV-2 has led to significant global morbidity and mortality, with potential neurological consequences, such as Parkinson's disease (PD). However, the underlying mechanisms remain elusive. METHODS To address this critical question, we conducted an in-depth transcriptome analysis of dopaminergic (DA) neurons in both COVID-19 and PD patients. We identified common pathways and differentially expressed genes (DEGs), performed enrichment analysis, constructed protein‒protein interaction networks and gene regulatory networks, and employed machine learning methods to develop disease diagnosis and progression prediction models. To further substantiate our findings, we performed validation of hub genes using a single-cell sequencing dataset encompassing DA neurons from PD patients, as well as transcriptome sequencing of DA neurons from a mouse model of MPTP(1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine)-induced PD. Furthermore, a drug-protein interaction network was also created. RESULTS We gained detailed insights into biological functions and signaling pathways, including ion transport and synaptic signaling pathways. CD38 was identified as a potential key biomarker. Disease diagnosis and progression prediction models were specifically tailored for PD. Molecular docking simulations and molecular dynamics simulations were employed to predict potential therapeutic drugs, revealing that genistein holds significant promise for exerting dual therapeutic effects on both PD and COVID-19. CONCLUSIONS Our study provides innovative strategies for advancing PD-related research and treatment in the context of the ongoing COVID-19 pandemic by elucidating the common pathogenesis between COVID-19 and PD in DA neurons.
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Affiliation(s)
- Qiuhan Xu
- Department of Neurology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, People's Republic of China
| | - Sisi Jiang
- Department of Neurology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, People's Republic of China
| | - Ruiqing Kang
- Department of Neurology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, People's Republic of China
| | - Yiling Wang
- Department of Neurology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, People's Republic of China
| | - Baorong Zhang
- Department of Neurology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, People's Republic of China.
| | - Jun Tian
- Department of Neurology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, People's Republic of China.
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7
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Li W, Wu H, Li J, Wang Z, Cai M, Liu X, Liu G. Transcriptomic analysis reveals associations of blood-based A-to-I editing with Parkinson's disease. J Neurol 2024; 271:976-985. [PMID: 37902879 DOI: 10.1007/s00415-023-12053-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 10/07/2023] [Accepted: 10/09/2023] [Indexed: 11/01/2023]
Abstract
BACKGROUND Adenosine-to-inosine (A-to-I) editing is the most common type of RNA editing in humans and the role of A-to-I RNA editing remains unclear in Parkinson's disease (PD). OBJECTIVE We aimed to explore the potential causal association between A-to-I editing and PD, and to assess whether changes in A-to-I editing were associated with cognitive progression in PD. METHODS The RNA-seq data from 380 PD patients and 178 healthy controls in the Parkinson's Progression Marker Initiative cohort was used to quantify A-to-I editing sites. We performed cis-RNA editing quantitative trait loci analysis and a two-sample Mendelian Randomization (MR) study by integrating genome-wide association studies to infer the potential causality between A-to-I editing and PD pathogenesis. The potential causal A-to-I editing sites were further confirmed by Summary-data-based MR analysis. Spearman's correlation analysis was performed to characterize the association between longitudinal A-to-I editing and cognitive progression in patients with PD. RESULTS We identified 17 potential causal A-to-I editing sites for PD and indicated that genetic risk variants may contribute to the risk of PD through A-to-I editing. These A-to-I editing sites were located in genes NCOR1, KANSL1 and BST1. Moreover, we observed 57 sites whose longitudinal A-to-I editing levels correlated with cognitive progression in PD. CONCLUSIONS We found potential causal A-to-I editing sites for PD onset and longitudinal changes of A-to-I editing were associated with cognitive progression in PD. We anticipate this study will provide new biological insights and drive the discovery of the epitranscriptomic role underlying Parkinson's disease.
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Affiliation(s)
- Weimin Li
- Shenzhen Key Laboratory of Systems Medicine in Inflammatory Diseases, School of Medicine, Shenzhen Campus of Sun Yat-Sen University, No.66, Gongchang Road, Guangming District, Shenzhen, 518107, Guangdong, People's Republic of China
- Neurobiology Research Center, School of Medicine, Shenzhen Campus of Sun Yat-Sen University, No.66, Gongchang Road, Guangming District, Shenzhen, 518107, Guangdong, People's Republic of China
| | - Hao Wu
- Shenzhen Key Laboratory of Systems Medicine in Inflammatory Diseases, School of Medicine, Shenzhen Campus of Sun Yat-Sen University, No.66, Gongchang Road, Guangming District, Shenzhen, 518107, Guangdong, People's Republic of China
- Neurobiology Research Center, School of Medicine, Shenzhen Campus of Sun Yat-Sen University, No.66, Gongchang Road, Guangming District, Shenzhen, 518107, Guangdong, People's Republic of China
| | - Jinxia Li
- Shenzhen Key Laboratory of Systems Medicine in Inflammatory Diseases, School of Medicine, Shenzhen Campus of Sun Yat-Sen University, No.66, Gongchang Road, Guangming District, Shenzhen, 518107, Guangdong, People's Republic of China
- Neurobiology Research Center, School of Medicine, Shenzhen Campus of Sun Yat-Sen University, No.66, Gongchang Road, Guangming District, Shenzhen, 518107, Guangdong, People's Republic of China
| | - Zhuo Wang
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, 518172, Guangdong, People's Republic of China
| | - Miao Cai
- Neurology Department, Zhejiang Hospital, Hangzhou, 310013, People's Republic of China
| | - Xiaoli Liu
- Neurology Department, Zhejiang Hospital, Hangzhou, 310013, People's Republic of China
| | - Ganqiang Liu
- Shenzhen Key Laboratory of Systems Medicine in Inflammatory Diseases, School of Medicine, Shenzhen Campus of Sun Yat-Sen University, No.66, Gongchang Road, Guangming District, Shenzhen, 518107, Guangdong, People's Republic of China.
- Neurobiology Research Center, School of Medicine, Shenzhen Campus of Sun Yat-Sen University, No.66, Gongchang Road, Guangming District, Shenzhen, 518107, Guangdong, People's Republic of China.
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8
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Whittle BJ, Izuogu OG, Lowes H, Deen D, Pyle A, Coxhead J, Lawson RA, Yarnall AJ, Jackson MS, Santibanez-Koref M, Hudson G. Early-stage idiopathic Parkinson's disease is associated with reduced circular RNA expression. NPJ Parkinsons Dis 2024; 10:25. [PMID: 38245550 PMCID: PMC10799891 DOI: 10.1038/s41531-024-00636-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 01/08/2024] [Indexed: 01/22/2024] Open
Abstract
Neurodegeneration in Parkinson's disease (PD) precedes diagnosis by years. Early neurodegeneration may be reflected in RNA levels and measurable as a biomarker. Here, we present the largest quantification of whole blood linear and circular RNAs (circRNA) in early-stage idiopathic PD, using RNA sequencing data from two cohorts (PPMI = 259 PD, 161 Controls; ICICLE-PD = 48 PD, 48 Controls). We identified a replicable increase in TMEM252 and LMNB1 gene expression in PD. We identified novel differences in the expression of circRNAs from ESYT2, BMS1P1 and CCDC9, and replicated trends of previously reported circRNAs. Overall, using circRNA as a diagnostic biomarker in PD did not show any clear improvement over linear RNA, minimising its potential clinical utility. More interestingly, we observed a general reduction in circRNA expression in both PD cohorts, accompanied by an increase in RNASEL expression. This imbalance implicates the activation of an innate antiviral immune response and suggests a previously unknown aspect of circRNA regulation in PD.
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Affiliation(s)
- Benjamin J Whittle
- Wellcome Centre for Mitochondrial Research, Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Osagie G Izuogu
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Hannah Lowes
- Wellcome Centre for Mitochondrial Research, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Dasha Deen
- Wellcome Centre for Mitochondrial Research, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Angela Pyle
- Wellcome Centre for Mitochondrial Research, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Jon Coxhead
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Rachael A Lawson
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Alison J Yarnall
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Michael S Jackson
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | | | - Gavin Hudson
- Wellcome Centre for Mitochondrial Research, Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK.
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9
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Geng L, Gao W, Saiyin H, Li Y, Zeng Y, Zhang Z, Li X, Liu Z, Gao Q, An P, Jiang N, Yu X, Chen X, Li S, Chen L, Lu B, Li A, Chen G, Shen Y, Zhang H, Tian M, Zhang Z, Li J. MLKL deficiency alleviates neuroinflammation and motor deficits in the α-synuclein transgenic mouse model of Parkinson's disease. Mol Neurodegener 2023; 18:94. [PMID: 38041169 PMCID: PMC10693130 DOI: 10.1186/s13024-023-00686-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 11/25/2023] [Indexed: 12/03/2023] Open
Abstract
Parkinson's disease (PD), one of the most devastating neurodegenerative brain disorders, is characterized by the progressive loss of dopaminergic neurons in the substantia nigra (SN) and deposits of α-synuclein aggregates. Currently, pharmacological interventions for PD remain inadequate. The cell necroptosis executor protein MLKL (Mixed-lineage kinase domain-like) is involved in various diseases, including inflammatory bowel disease and neurodegenerative diseases; however, its precise role in PD remains unclear. Here, we investigated the neuroprotective role of MLKL inhibition or ablation against primary neuronal cells and human iPSC-derived midbrain organoids induced by toxic α-Synuclein preformed fibrils (PFFs). Using a mouse model (Tg-Mlkl-/-) generated by crossbreeding the SNCA A53T synuclein transgenic mice with MLKL knockout (KO)mice, we assessed the impact of MLKL deficiency on the progression of Parkinsonian traits. Our findings demonstrate that Tg-Mlkl-/- mice exhibited a significant improvement in motor symptoms and reduced phosphorylated α-synuclein expression compared to the classic A53T transgenic mice. Furthermore, MLKL deficiency alleviated tyrosine hydroxylase (TH)-positive neuron loss and attenuated neuroinflammation by inhibiting the activation of microglia and astrocytes. Single-cell RNA-seq (scRNA-seq) analysis of the SN of Tg-Mlkl-/- mice revealed a unique cell type-specific transcriptome profile, including downregulated prostaglandin D synthase (PTGDS) expression, indicating reduced microglial cells and dampened neuron death. Thus, MLKL represents a critical therapeutic target for reducing neuroinflammation and preventing motor deficits in PD.
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Affiliation(s)
- Lu Geng
- State Key Laboratory of Genetic Engineering, Department of Neurology, Huashan Hospital and School of Life Sciences, MOE Engineering Research Center of Gene Technology, Shanghai Engineering Research Center of Industrial Microorganisms, Fudan University, Shanghai, 200438, China
| | - Wenqing Gao
- State Key Laboratory of Genetic Engineering, Department of Neurology, Huashan Hospital and School of Life Sciences, MOE Engineering Research Center of Gene Technology, Shanghai Engineering Research Center of Industrial Microorganisms, Fudan University, Shanghai, 200438, China
| | - Hexige Saiyin
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, 200438, China
| | - Yuanyuan Li
- State Key Laboratory of Genetic Engineering, Department of Neurology, Huashan Hospital and School of Life Sciences, MOE Engineering Research Center of Gene Technology, Shanghai Engineering Research Center of Industrial Microorganisms, Fudan University, Shanghai, 200438, China
| | - Yu Zeng
- Insitute of Immunology, School of Medicine, Shanghai Jiaotong University, Shanghai, 200025, China
| | - Zhifei Zhang
- State Key Laboratory of Genetic Engineering, Department of Neurology, Huashan Hospital and School of Life Sciences, MOE Engineering Research Center of Gene Technology, Shanghai Engineering Research Center of Industrial Microorganisms, Fudan University, Shanghai, 200438, China
| | - Xue Li
- Insitute of Immunology, School of Medicine, Shanghai Jiaotong University, Shanghai, 200025, China
| | - Zuolong Liu
- State Key Laboratory of Genetic Engineering, Department of Neurology, Huashan Hospital and School of Life Sciences, MOE Engineering Research Center of Gene Technology, Shanghai Engineering Research Center of Industrial Microorganisms, Fudan University, Shanghai, 200438, China
| | - Qiang Gao
- State Key Laboratory of Genetic Engineering, Department of Neurology, Huashan Hospital and School of Life Sciences, MOE Engineering Research Center of Gene Technology, Shanghai Engineering Research Center of Industrial Microorganisms, Fudan University, Shanghai, 200438, China
| | - Ping An
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, 200438, China
| | - Ning Jiang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, 200438, China
| | - Xiaofei Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, 200438, China
| | - Xiangjun Chen
- Department of Neurology, Huashan Hospital and Institute of Neurology, Fudan University, Shanghai, 200040, China
| | - Suhua Li
- Division of Natural Science, Duke Kunshan University, Jiangsu, 215316, China
| | - Lei Chen
- Insitute of Immunology, School of Medicine, Shanghai Jiaotong University, Shanghai, 200025, China
| | - Boxun Lu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, 200438, China
| | - Aiqun Li
- Levi Regenerative Medicine Technologies, Zhuhai, 519085, China
| | - Guoyuan Chen
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yidong Shen
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Haibing Zhang
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Mei Tian
- Human Phenome Institute, Fudan University, Shanghai, 200438, China
| | - Zhuohua Zhang
- Institute of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, 410078, Hunan, China.
- Department of Neurosciences, Hengyang Medical College, University of South China, Hengyang, 421001, Hunan, China.
| | - Jixi Li
- State Key Laboratory of Genetic Engineering, Department of Neurology, Huashan Hospital and School of Life Sciences, MOE Engineering Research Center of Gene Technology, Shanghai Engineering Research Center of Industrial Microorganisms, Fudan University, Shanghai, 200438, China.
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10
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Mondal T, Smith CI, Loffredo CA, Quartey R, Moses G, Howell CD, Korba B, Kwabi-Addo B, Nunlee-Bland G, R. Rucker L, Johnson J, Ghosh S. Transcriptomics of MASLD Pathobiology in African American Patients in the Washington DC Area †. Int J Mol Sci 2023; 24:16654. [PMID: 38068980 PMCID: PMC10706626 DOI: 10.3390/ijms242316654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/17/2023] [Accepted: 11/18/2023] [Indexed: 12/18/2023] Open
Abstract
Metabolic-dysfunction-associated steatotic liver disease (MASLD) is becoming the most common chronic liver disease worldwide and is of concern among African Americans (AA) in the United States. This pilot study evaluated the differential gene expressions and identified the signature genes in the disease pathways of AA individuals with MASLD. Blood samples were obtained from MASLD patients (n = 23) and non-MASLD controls (n = 24) along with their sociodemographic and medical details. Whole-blood transcriptomic analysis was carried out using Affymetrix Clarion-S Assay. A validation study was performed utilizing TaqMan Arrays coupled with Ingenuity Pathway Analysis (IPA) to identify the major disease pathways. Out of 21,448 genes in total, 535 genes (2.5%) were significantly (p < 0.05) and differentially expressed when we compared the cases and controls. A significant overlap in the predominant differentially expressed genes and pathways identified in previous studies using hepatic tissue was observed. Of note, TGFB1 and E2F1 genes were upregulated, and HMBS was downregulated significantly. Hepatic fibrosis signaling is the top canonical pathway, and its corresponding biofunction contributes to the development of hepatocellular carcinoma. The findings address the knowledge gaps regarding how signature genes and functional pathways can be detected in blood samples ('liquid biopsy') in AA MASLD patients, demonstrating the potential of the blood samples as an alternative non-invasive source of material for future studies.
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Affiliation(s)
- Tanmoy Mondal
- Department of Biology, Howard University, Washington, DC 20059, USA; (T.M.); (G.M.); (J.J.)
| | - Coleman I. Smith
- MedStar-Georgetown Transplantation Institute, Georgetown University School of Medicine, Washington, DC 20007, USA;
| | | | - Ruth Quartey
- Department of Internal Medicine, College of Medicine, Howard University, Washington, DC 20007, USA; (R.Q.); (C.D.H.)
| | - Gemeyel Moses
- Department of Biology, Howard University, Washington, DC 20059, USA; (T.M.); (G.M.); (J.J.)
| | - Charles D. Howell
- Department of Internal Medicine, College of Medicine, Howard University, Washington, DC 20007, USA; (R.Q.); (C.D.H.)
| | - Brent Korba
- Department of Microbiology & Immunology, Georgetown University, Washington, DC 20007, USA;
| | - Bernard Kwabi-Addo
- Department of Biochemistry, College of Medicine, Howard University, Washington, DC 20059, USA;
| | - Gail Nunlee-Bland
- Departments of Pediatrics and Child Health, College of Medicine, Howard University, Washington, DC 20059, USA;
| | - Leanna R. Rucker
- Department of Internal Medicine, MedStar Georgetown University Hospital, Washington, DC 20007, USA;
| | - Jheannelle Johnson
- Department of Biology, Howard University, Washington, DC 20059, USA; (T.M.); (G.M.); (J.J.)
| | - Somiranjan Ghosh
- Department of Biology, Howard University, Washington, DC 20059, USA; (T.M.); (G.M.); (J.J.)
- Departments of Pediatrics and Child Health, College of Medicine, Howard University, Washington, DC 20059, USA;
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11
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Semenova EI, Partevian SA, Shulskaya MV, Rudenok MM, Lukashevich MV, Baranova NM, Doronina OB, Doronina KS, Rosinskaya AV, Fedotova EY, Illarioshkin SN, Slominsky PA, Shadrina MI, Alieva AK. Analysis of ADORA2A, MTA1, PTGDS, PTGS2, NSF, and HNMT Gene Expression Levels in Peripheral Blood of Patients with Early Stages of Parkinson's Disease. BIOMED RESEARCH INTERNATIONAL 2023; 2023:9412776. [PMID: 38027039 PMCID: PMC10681775 DOI: 10.1155/2023/9412776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 10/16/2023] [Accepted: 10/24/2023] [Indexed: 12/01/2023]
Abstract
Parkinson's disease (PD) is a common chronic, age-related neurodegenerative disease. This disease is characterized by a long prodromal period. In this context, it is important to search for the genes and mechanisms that are involved in the development of the pathological process in the earliest stages of the disease. Published data suggest that blood cells, particularly lymphocytes, may be a model for studying the processes that occur in the brain in PD. Thus, in the present work, we performed an analysis of changes in the expression of the genes ADORA2A, MTA1, PTGDS, PTGS2, NSF, and HNMT in the peripheral blood of patients with early stages of PD (stages 1 and 2 of the Hoehn-Yahr scale). We found significant and PD-specific expression changes of four genes, i.e., MTA1, PTGS2, NSF, and HNMT, in the peripheral blood of patients with early stages of PD. These genes may be associated with PD pathogenesis in the early clinical stages and can be considered as potential candidate genes for this disease. Altered expression of the ADORA2A gene in treated PD patients may indicate that this gene is involved in processes affected by antiparkinsonian therapy.
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Affiliation(s)
- Ekaterina I. Semenova
- National Research Centre “Kurchatov Institute”, 2 Kurchatova Sq., 123182 Moscow, Russia
| | - Suzanna A. Partevian
- National Research Centre “Kurchatov Institute”, 2 Kurchatova Sq., 123182 Moscow, Russia
| | - Marina V. Shulskaya
- National Research Centre “Kurchatov Institute”, 2 Kurchatova Sq., 123182 Moscow, Russia
| | - Margarita M. Rudenok
- National Research Centre “Kurchatov Institute”, 2 Kurchatova Sq., 123182 Moscow, Russia
| | - Maria V. Lukashevich
- National Research Centre “Kurchatov Institute”, 2 Kurchatova Sq., 123182 Moscow, Russia
| | - Nina M. Baranova
- Peoples' Friendship University of Russia (RUDN University), 6, Miklukho-Maklaya Str., 117198 Moscow, Russia
| | - Olga B. Doronina
- Novosibirsk State Medical University, 52, Krasnyy Ave., 630091 Novosibirsk, Russia
| | - Kseniya S. Doronina
- Novosibirsk State Medical University, 52, Krasnyy Ave., 630091 Novosibirsk, Russia
| | - Anna V. Rosinskaya
- State Public Health Institution Primorsk Regional Clinical Hospital No. 1, 57 Aleutskaya St., 690091 Vladivostok, Russia
| | | | | | - Petr A. Slominsky
- National Research Centre “Kurchatov Institute”, 2 Kurchatova Sq., 123182 Moscow, Russia
| | - Maria I. Shadrina
- National Research Centre “Kurchatov Institute”, 2 Kurchatova Sq., 123182 Moscow, Russia
| | - Anelya Kh. Alieva
- National Research Centre “Kurchatov Institute”, 2 Kurchatova Sq., 123182 Moscow, Russia
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12
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Lu H, Zhang B, Yin T, Hua Y, Cao C, Ge M, Shen D, Zhou YL, Jia Z. Ferroptosis-Related Immune Genes in Hematological Diagnosis of Parkinson's Diseases. Mol Neurobiol 2023; 60:6395-6409. [PMID: 37452932 DOI: 10.1007/s12035-023-03468-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Accepted: 06/24/2023] [Indexed: 07/18/2023]
Abstract
Emerging evidence suggested that ferroptosis and immune activation, as well as their interactions, played a crucial role in the occurrence and progression of Parkinson's disease (PD). However, whether this interaction could serve as the basis for a hematological diagnosis of PD remained poorly understood. This study aimed to construct a novel hematological model for PD diagnosis based on the ferroptosis-related immune genes. The brain imaging of PD patients was obtained from the Affiliated Hospital of Nantong University. We used least absolute shrinkage and selection operator (LASSO) to identify the optimal signature ferroptosis-related immune genes based on six gene expression profile datasets of substantia nigra (SN) and peripheral blood of PD patients. Then we used the support vector machine (SVM) classifier to construct the hematological diagnostic model named Ferr.Sig for PD. Gene set enrichment analysis was utilized to execute gene functional annotation. The brain imaging and functional annotation analysis revealed prominent iron deposition and immune activation in the SN region of PD patients. We identified a total of 17 signature ferroptosis-related immune genes using LASSO method and imported them to SVM classifier. The Ferr.Sig model exhibited a high diagnostic accuracy, and its area under the curve (AUC) for distinguishing PD patients from healthy controls in the training and internal validation cohort reached 0.856 and 0.704, respectively. We also used the Ferr.Sig into other external validation cohorts, and a comparable AUC with the internal cohort was obtained, with the AUC of 0.727 in Scherzer's cohort, 0.745 in Roncagli's cohort, and 0.778 in Meiklejohn's cohort. Furthermore, the diagnostic performance of Ferr.Sig was not interfered by the other neurodegenerative diseases. This study revealed the value of ferroptosis-related immune genes in PD diagnosis, which may provide a novel direction and strategy for the development of novel biomarkers with less invasiveness, low cost, and high accuracy for PD screening and diagnosis.
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Affiliation(s)
- Heyue Lu
- Department of Medical Imaging, Affiliated Hospital and Medical School of Nantong University, NO.20, Xisi Road, Nantong, 226001, People's Republic of China
| | - Bo Zhang
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, NO.20, Xisi Road, Nantong, 226001, People's Republic of China
| | - Tingting Yin
- Department of Medical Imaging, Affiliated Hospital and Medical School of Nantong University, NO.20, Xisi Road, Nantong, 226001, People's Republic of China
| | - Ye Hua
- Department of Medical Imaging, Affiliated Hospital and Medical School of Nantong University, NO.20, Xisi Road, Nantong, 226001, People's Republic of China
| | - Chenyang Cao
- Department of Medical Imaging, Affiliated Hospital and Medical School of Nantong University, NO.20, Xisi Road, Nantong, 226001, People's Republic of China
| | - Min Ge
- Department of Medical Imaging, Affiliated Hospital and Medical School of Nantong University, NO.20, Xisi Road, Nantong, 226001, People's Republic of China
| | - Dandan Shen
- Department of Medical Imaging, Affiliated Hospital and Medical School of Nantong University, NO.20, Xisi Road, Nantong, 226001, People's Republic of China
| | - You Lang Zhou
- Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, NO.20, Xisi Road, Nantong, 226001, People's Republic of China.
| | - Zhongzheng Jia
- Department of Medical Imaging, Affiliated Hospital and Medical School of Nantong University, NO.20, Xisi Road, Nantong, 226001, People's Republic of China.
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13
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Kelly J, Moyeed R, Carroll C, Luo S, Li X. Blood biomarker-based classification study for neurodegenerative diseases. Sci Rep 2023; 13:17191. [PMID: 37821485 PMCID: PMC10567903 DOI: 10.1038/s41598-023-43956-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 09/30/2023] [Indexed: 10/13/2023] Open
Abstract
As the population ages, neurodegenerative diseases are becoming more prevalent, making it crucial to comprehend the underlying disease mechanisms and identify biomarkers to allow for early diagnosis and effective screening for clinical trials. Thanks to advancements in gene expression profiling, it is now possible to search for disease biomarkers on an unprecedented scale.Here we applied a selection of five machine learning (ML) approaches to identify blood-based biomarkers for Alzheimer's (AD) and Parkinson's disease (PD) with the application of multiple feature selection methods. Based on ROC AUC performance, one optimal random forest (RF) model was discovered for AD with 159 gene markers (ROC-AUC = 0.886), while one optimal RF model was discovered for PD (ROC-AUC = 0.743). Additionally, in comparison to traditional ML approaches, deep learning approaches were applied to evaluate their potential applications in future works. We demonstrated that convolutional neural networks perform consistently well across both the Alzheimer's (ROC AUC = 0.810) and Parkinson's (ROC AUC = 0.715) datasets, suggesting its potential in gene expression biomarker detection with increased tuning of their architecture.
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Affiliation(s)
- Jack Kelly
- Faculty of Medicine, Biology and Health, Centre for Biostatistics, School of Health Sciences, University of Manchester, Manchester, UK.
- Faculty of Health, University of Plymouth, Plymouth, PL6 8BU, UK.
| | - Rana Moyeed
- Faculty of Science and Engineering, University of Plymouth, Plymouth, PL6 8BU, UK
| | - Camille Carroll
- Faculty of Health, University of Plymouth, Plymouth, PL6 8BU, UK
| | - Shouqing Luo
- Faculty of Health, University of Plymouth, Plymouth, PL6 8BU, UK
| | - Xinzhong Li
- School of Health and Life Sciences, Teesside University, Middlesbrough, TS1 3BX, UK.
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14
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Wang H, Dou S, Wang C, Gao W, Cheng B, Yan F. Identification and Experimental Validation of Parkinson's Disease with Major Depressive Disorder Common Genes. Mol Neurobiol 2023; 60:6092-6108. [PMID: 37418066 DOI: 10.1007/s12035-023-03451-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 06/17/2023] [Indexed: 07/08/2023]
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disease that affects about 10 million people worldwide. Non-motor and motor symptoms usually accompany PD. Major depressive disorder (MDD) is one of the non-motor manifestations of PD it remains unrecognized and undertreated effectively. MDD in PD has complicated pathophysiologies and remains unclear. The study aimed to explore the candidate genes and molecular mechanisms of PD with MDD. PD (GSE6613) and MDD (GSE98793) gene expression profiles were downloaded from Gene Expression Omnibus (GEO). Above all, the data of the two datasets were standardized separately, and differentially expressed genes (DEGs) were obtained by using the Limma package of R. Take the intersection of the two differential genes and remove the genes with inconsistent expression trends. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were investigated to explore the function of the common DEGs. Additionally, the construction of the protein-protein interaction (PPI) network was to search the hub genes, and then the least absolute shrinkage and selection operator (LASSO) regression was used to further identify the key genes. GSE99039 for PD and GSE201332 for MDD were performed to validate the hub genes by the violin plot and receiver operating characteristic (ROC) curve. Last but not least, immune cell dysregulation in PD was investigated by immune cell infiltration. As a result, a total of 45 common genes with the same trend. Functional analysis revealed that they were enriched in neutrophil degranulation, secretory granule membrane, and leukocyte activation. LASSO was performed on 8 candidate hub genes after CytoHubba filtered 14 node genes. Finally, AQP9, SPI1, and RPH3A were validated by GSE99039 and GSE201332. Additionally, the three genes were also detected by the qPCR in vivo model and all increased compared to the control. The co-occurrence of PD and MDD can be attributed to AQP9, SPI1, and RPH3A genes. Neutrophils and monocyte infiltration play important roles in the development of PD and MDD. Novel insights may be gained from the findings for the study of mechanisms.
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Affiliation(s)
- Huiqing Wang
- School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, People's Republic of China
| | - Shanshan Dou
- College of Basic Medicine, Jining Medical University, Jining, 272067, People's Republic of China
| | - Chunmei Wang
- Neurobiology Institute, Jining Medical University, Jining, 272067, China
| | - Wenming Gao
- College of Basic Medicine, Jining Medical University, Jining, 272067, People's Republic of China
| | - Baohua Cheng
- College of Basic Medicine, Jining Medical University, Jining, 272067, People's Republic of China.
- Neurobiology Institute, Jining Medical University, Jining, 272067, China.
| | - Fuling Yan
- Department of Neurology, School of Medicine, Affiliated ZhongDa Hospital, Southeast University, No. 87 Dingjiaqiao Road, Nanjing, 210009, People's Republic of China.
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15
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Angelakis A, Soulioti I, Filippakis M. Diagnosis of acute myeloid leukaemia on microarray gene expression data using categorical gradient boosted trees. Heliyon 2023; 9:e20530. [PMID: 37860531 PMCID: PMC10582309 DOI: 10.1016/j.heliyon.2023.e20530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 10/21/2023] Open
Abstract
We define an iterative method for dimensionality reduction using categorical gradient boosted trees and Shapley values and created four machine learning models which potentially could be used as diagnostic tests for acute myeloid leukaemia (AML). For the final Catboost model we use a dataset of 2177 individuals using as features 16 probe sets and the age in order to classify if someone has AML or is healthy. The dataset is multicentric and consists of data from 27 organizations, 25 cities, 15 countries and 4 continents. The performance of our last model is specificity: 0.9909, sensitivity: 0.9985, F1-score: 0.9976 and its ROC-AUC: 0.9962 using ten fold cross validation. On an inference dataset the perormance is: specificity: 0.9909, sensitivity: 0.9969, F1-score: 0.9969 and its ROC-AUC: 0.9939. To the best of our knowledge the performance of our model is the best one in the literature, as regards the diagnosis of AML using similar or not data. Moreover, there has not been any bibliographic reference which associates AML or any other type of cancer with the 16 probe sets we used as features in our final model.
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Affiliation(s)
- Athanasios Angelakis
- Department of Epidemiology and Data Science, Amsterdam University Medical Centers, Amsterdam Public Health Research Institute, University of Amsterdam Data Science Center, Netherlands
| | - Ioanna Soulioti
- Department of Biology, National and Kapodistrian University of Athens, Greece
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16
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Bhandari N, Walambe R, Kotecha K, Kaliya M. Integrative gene expression analysis for the diagnosis of Parkinson's disease using machine learning and explainable AI. Comput Biol Med 2023; 163:107140. [PMID: 37315380 DOI: 10.1016/j.compbiomed.2023.107140] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 05/29/2023] [Accepted: 06/04/2023] [Indexed: 06/16/2023]
Abstract
Parkinson's disease (PD) is a progressive neurodegenerative disorder. Various symptoms and diagnostic tests are used in combination for the diagnosis of PD; however, accurate diagnosis at early stages is difficult. Blood-based markers can support physicians in the early diagnosis and treatment of PD. In this study, we used Machine Learning (ML) based methods for the diagnosis of PD by integrating gene expression data from different sources and applying explainable artificial intelligence (XAI) techniques to find the significant set of gene features contributing to diagnosis. We utilized the Least Absolute Shrinkage and Selection Operator (LASSO), and Ridge regression for the feature selection process. We utilized state-of-the-art ML techniques for the classification of PD cases and healthy controls. Logistic regression and Support Vector Machine showed the highest diagnostic accuracy. SHapley Additive exPlanations (SHAP) based global interpretable model-agnostic XAI method was utilized for the interpretation of the Support Vector Machine model. A set of significant biomarkers that contributed to the diagnosis of PD were identified. Some of these genes are associated with other neurodegenerative diseases. Our results suggest that the utilization of XAI can be useful in making early therapeutic decisions for the treatment of PD. The integration of datasets from different sources made this model robust. We believe that this research article will be of interest to clinicians as well as computational biologists in translational research.
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Affiliation(s)
- Nikita Bhandari
- Computer Science Department, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, MH, India; Symbiosis Center for Applied Artificial Intelligence (SCAAI), Symbiosis International Deemed University, Pune, Maharashtra, India
| | - Rahee Walambe
- Electronics and Telecommunication Department, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, Maharashtra, India; Symbiosis Center for Applied Artificial Intelligence (SCAAI), Symbiosis International Deemed University, Pune, Maharashtra, India.
| | - Ketan Kotecha
- Computer Science Department, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, MH, India; Electronics and Telecommunication Department, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, Maharashtra, India.
| | - Mehul Kaliya
- Department of General Medicine, AIIMS, Rajkot, Gujrat, India
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Odongo R, Bellur O, Abdik E, Çakır T. Brain-wide transcriptome-based metabolic alterations in Parkinson's disease: human inter-region and human-experimental model correlations. Mol Omics 2023; 19:522-537. [PMID: 36928892 DOI: 10.1039/d2mo00343k] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Abstract
Alterations in brain metabolism are closely associated with the molecular hallmarks of Parkinson's disease (PD). A clear understanding of the main metabolic perturbations in PD is therefore important. Here, we retrospectively analysed the expression of metabolic genes from 34 PD-control post-mortem human brain transcriptome data comparisons from literature, spanning multiple brain regions. We found high metabolic correlations between the Substantia nigra (SN)- and cerebral cortex-derived tissues. Moreover, three clusters of PD patient cohorts were identified based on perturbed metabolic processes in the SN - each characterised by perturbations in (a) bile acid metabolism (b) omega-3 fatty acid metabolism, and (c) lipoic acid and androgen metabolism - metabolic themes not comprehensively addressed in PD. These perturbations were supported by concurrence between transcriptome and proteome changes in the expression patterns for CBR1, ECI2, BDH2, CYP27A1, ALDH1B1, ALDH9A1, ADH5, ALDH7A1, L1CAM, and PLXNB3 genes, providing a valuable resource for drug targeting and diagnosis. Also, we analysed 58 PD-control transcriptome data comparisons from in vivo/in vitro disease models and identified experimental PD models with significant correlations to matched human brain regions. Collectively, our findings suggest metabolic alterations in several brain regions, heterogeneity in metabolic alterations between study cohorts for the SN tissues and the need to optimize current experimental models to advance research on metabolic aspects of PD.
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Affiliation(s)
- Regan Odongo
- Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey.
| | - Orhan Bellur
- Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey.
| | - Ecehan Abdik
- Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey.
| | - Tunahan Çakır
- Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey.
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Nour M, Senturk U, Polat K. Diagnosis and classification of Parkinson's disease using ensemble learning and 1D-PDCovNN. Comput Biol Med 2023; 161:107031. [PMID: 37211002 DOI: 10.1016/j.compbiomed.2023.107031] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/05/2023] [Accepted: 05/10/2023] [Indexed: 05/23/2023]
Abstract
In this paper, we proposed a novel approach to diagnose and classify Parkinson's Disease (PD) using ensemble learning and 1D-PDCovNN, a novel deep learning technique. PD is a neurodegenerative disorder; early detection and correct classification are essential for better disease management. The primary aim of this study is to develop a robust approach to diagnosing and classifying PD using EEG signals. As the dataset, we have used the San Diego Resting State EEG dataset to evaluate our proposed method. The proposed method mainly consists of three stages. In the first stage, the Independent Component Analysis (ICA) method has been used as the pre-processing method to filter out the blink noises from the EEG signals. Also, the effect of the band showing motor cortex activity in the 7-30 Hz frequency band of EEG signals in diagnosing and classifying Parkinson's disease from EEG signals has been investigated. In the second stage, the Common Spatial Pattern (CSP) method has been used as the feature extraction to extract useful information from EEG signals. Finally, an ensemble learning approach, Dynamic Classifier Selection (DCS) in Modified Local Accuracy (MLA), has been employed in the third stage, consisting of seven different classifiers. As the classifier method, DCS in MLA, XGBoost, and 1D-PDCovNN classifier has been used to classify the EEG signals as the PD and healthy control (HC). We first used dynamic classifier selection to diagnose and classify Parkinson's disease (PD) from EEG signals, and promising results have been obtained. The performance of the proposed approach has been evaluated using the classification accuracy, F-1 score, kappa score, Jaccard score, ROC curve, recall, and precision values in the classification of PD with the proposed models. In the classification of PD, the combination of DCS in MLA achieved an accuracy of 99,31%. The results of this study demonstrate that the proposed approach can be used as a reliable tool for early diagnosis and classification of PD.
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Affiliation(s)
- Majid Nour
- Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
| | - Umit Senturk
- Department of Computer Engineering, Bolu Abant Izzet Baysal University, Bolu, Turkey.
| | - Kemal Polat
- Department of Electrical and Electronics Engineering, Bolu Abant Izzet Baysal University, Bolu, Turkey.
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Tassone A, Meringolo M, Ponterio G, Bonsi P, Schirinzi T, Martella G. Mitochondrial Bioenergy in Neurodegenerative Disease: Huntington and Parkinson. Int J Mol Sci 2023; 24:ijms24087221. [PMID: 37108382 PMCID: PMC10138549 DOI: 10.3390/ijms24087221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 04/10/2023] [Accepted: 04/11/2023] [Indexed: 04/29/2023] Open
Abstract
Strong evidence suggests a correlation between degeneration and mitochondrial deficiency. Typical cases of degeneration can be observed in physiological phenomena (i.e., ageing) as well as in neurological neurodegenerative diseases and cancer. All these pathologies have the dyshomeostasis of mitochondrial bioenergy as a common denominator. Neurodegenerative diseases show bioenergetic imbalances in their pathogenesis or progression. Huntington's chorea and Parkinson's disease are both neurodegenerative diseases, but while Huntington's disease is genetic and progressive with early manifestation and severe penetrance, Parkinson's disease is a pathology with multifactorial aspects. Indeed, there are different types of Parkinson/Parkinsonism. Many forms are early-onset diseases linked to gene mutations, while others could be idiopathic, appear in young adults, or be post-injury senescence conditions. Although Huntington's is defined as a hyperkinetic disorder, Parkinson's is a hypokinetic disorder. However, they both share a lot of similarities, such as neuronal excitability, the loss of striatal function, psychiatric comorbidity, etc. In this review, we will describe the start and development of both diseases in relation to mitochondrial dysfunction. These dysfunctions act on energy metabolism and reduce the vitality of neurons in many different brain areas.
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Affiliation(s)
- Annalisa Tassone
- Laboratory of Neurophysiology and Plasticity, IRCCS Fondazione Santa Lucia, 00143 Rome, Italy
| | - Maria Meringolo
- Laboratory of Neurophysiology and Plasticity, IRCCS Fondazione Santa Lucia, 00143 Rome, Italy
- Saint Camillus International University of Health and Medical Sciences, 00131 Rome, Italy
| | - Giulia Ponterio
- Laboratory of Neurophysiology and Plasticity, IRCCS Fondazione Santa Lucia, 00143 Rome, Italy
| | - Paola Bonsi
- Laboratory of Neurophysiology and Plasticity, IRCCS Fondazione Santa Lucia, 00143 Rome, Italy
| | - Tommaso Schirinzi
- Unit of Neurology, Department of Systems Medicine, Tor Vergata University of Rome, 00133 Rome, Italy
| | - Giuseppina Martella
- Laboratory of Neurophysiology and Plasticity, IRCCS Fondazione Santa Lucia, 00143 Rome, Italy
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Parkinson's Disease, Parkinsonisms, and Mitochondria: the Role of Nuclear and Mitochondrial DNA. Curr Neurol Neurosci Rep 2023; 23:131-147. [PMID: 36881253 DOI: 10.1007/s11910-023-01260-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/16/2023] [Indexed: 03/08/2023]
Abstract
PURPOSE OF REVIEW Overwhelming evidence indicates that mitochondrial dysfunction is a central factor in Parkinson's disease (PD) pathophysiology. This paper aims to review the latest literature published, focusing on genetic defects and expression alterations affecting mitochondria-associated genes, in support of their key role in PD pathogenesis. RECENT FINDINGS Thanks to the use of new omics approaches, a growing number of studies are discovering alterations affecting genes with mitochondrial functions in patients with PD and parkinsonisms. These genetic alterations include pathogenic single-nucleotide variants, polymorphisms acting as risk factors, and transcriptome modifications, affecting both nuclear and mitochondrial genes. We will focus on alterations of mitochondria-associated genes described by studies conducted on patients or on animal/cellular models of PD or parkinsonisms. We will comment how these findings can be taken into consideration for improving the diagnostic procedures or for deepening our knowledge on the role of mitochondrial dysfunctions in PD.
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Li W, Shen J, Wu H, Lin L, Liu Y, Pei Z, Liu G. Transcriptome Analysis Reveals a Two-Gene Signature Links to Motor Progression and Alterations of Immune Cells in Parkinson's Disease. JOURNAL OF PARKINSON'S DISEASE 2023; 13:25-38. [PMID: 36591658 PMCID: PMC9912738 DOI: 10.3233/jpd-223454] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
BACKGROUND The motor impairment in Parkinson's disease (PD) can be managed but effective treatments for stopping or slowing the disease process are lacking. The advent of transcriptomics studies in PD shed light on the development of promising measures to predict disease progression and discover novel therapeutic strategies. OBJECTIVE To reveal the potential role of transcripts in the motor impairment progression of patients with PD via transcriptome analysis. METHODS We separately analyzed the differentially expressed genes (DEGs) between PD cases and healthy controls in two cohorts using whole blood bulk transcriptome data. Based on the intersection of DEGs, we established a prognostic signature by regularized regression and Cox proportional hazards analysis. We further performed immune cell analysis and single-cell RNA sequencing analysis to study the biological features of this signature. RESULTS We identified a two-gene-based prognostic signature that links to PD motor progression and the two-gene signature-derived risk score was associated with several types of immune cells in blood. Notably, the fraction of neutrophils increased 5% and CD4+ T cells decreased 7% in patients with high-risk scores compared to that in patients with low-risk scores, suggesting these two types of immune cells might play key roles in the prognosis of PD. We also observed the downregulated genes in PD patients with high-risk scores that enriched in PD-associated pathways from iPSC-derived dopaminergic neurons single-cell RNA sequencing analysis. CONCLUSION We identified a two-gene signature linked to the motor progression in PD, which provides new insights into the motor prognosis of PD.
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Affiliation(s)
- Weimin Li
- Neurobiology Research Center, School of Medicine, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China,Shenzhen Key Laboratory of Systems Medicine in Inflammatory Diseases, School of Medicine, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Jiaqi Shen
- Neurobiology Research Center, School of Medicine, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China,Shenzhen Key Laboratory of Systems Medicine in Inflammatory Diseases, School of Medicine, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Hao Wu
- Neurobiology Research Center, School of Medicine, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China,Shenzhen Key Laboratory of Systems Medicine in Inflammatory Diseases, School of Medicine, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Lishan Lin
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yanmei Liu
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Zhong Pei
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Ganqiang Liu
- Neurobiology Research Center, School of Medicine, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China,Shenzhen Key Laboratory of Systems Medicine in Inflammatory Diseases, School of Medicine, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China,Correspondence to: Ganqiang Liu, PhD, School of Medicine, Sun Yat-sen University, No. 66, Gongchang Road, Guangming District, Shenzhen, Guangdong, 518107, China. Tel.: +86 13695956858; E-mail:
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Li Y, Pang J, Wang J, Dai G, Bo Q, Wang X, Wang W. Knockdown of PDCD4 ameliorates neural cell apoptosis and mitochondrial injury through activating the PI3K/AKT/mTOR signal in Parkinson's disease. J Chem Neuroanat 2023; 129:102239. [PMID: 36736747 DOI: 10.1016/j.jchemneu.2023.102239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 01/31/2023] [Accepted: 01/31/2023] [Indexed: 02/04/2023]
Abstract
BACKGROUND Parkinson's disease (PD) is a complex neurodegenerative disorder and hampers normal living. It has been reported that programmed cell death 4 (PDCD4) is associated with tumor suppression, inflammatory response, and apoptosis. OBJECTIVE The aim of this study was to investigate the role of PDCD4 in PD. METHODS The in vivo and in vitro PD models were established by MPTP-induced mice and MMP+ stimulated MN9D cells, respectively. The expression of PDCD4 was detected by western blot. The MN9D cell viability and apoptosis were determined by MTT and flow cytometry assay. Moreover, the MN9D cell mitochondrial injury was evaluated by JC-1 staining. RESULTS In this study, PDCD4 was highly expressed in brain tissue of MPTP-induced PD mouse model. In a loss-function experiments, knockdown of PDCD4 promoted MN9D cell viability and allayed MPP+-triggered MN9D cell apoptosis. Furthermore, knockdown of PDCD4 ameliorated MPP+-evoked MN9D cell mitochondrial injury. Mechanically, knockdown of PDCD4 abolished the effect of MMP+ stimulation via activating phosphoinositide 3-kinase(PI3K)/AKT/mammalian target of rapamycin (mTOR) signal. Notably, the protective effects of shPDCD4 on cell apoptosis and mitochondrial injury were suppressed by PI3K inhibitor LY294002. CONCLUSION In summary,knockdown of PDCD4 ameliorates neural cell apoptosis and mitochondrial injury through activating the PI3K/AKT/mTOR signal, providing a novel target for PD treatment. AVAILABILITY OF DATA AND MATERIALS All data generated or analyzed during this study are included in this published article.
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Affiliation(s)
- Yanmin Li
- Department of Neurology, The First Hospital of Hebei Medical University, Shijiangzhuang, Hebei 050031, China; Department of Neurology, Hebei Hospital of Xuanwu Hospital Capital Medical University, Shijiazhuang, Hebei, 050031, China.
| | - Jianmin Pang
- Department of Neurology, The First Hospital of Hebei Medical University, Shijiangzhuang, Hebei 050031, China
| | - Jing Wang
- Department of Respiratory Medicine, Harrison International Peace Hospital, Hengshui, Hebei 053000, China
| | - Guining Dai
- Department of Neurology, The First Hospital of Hebei Medical University, Shijiangzhuang, Hebei 050031, China; Department of Neurology, Hebei Hospital of Xuanwu Hospital Capital Medical University, Shijiazhuang, Hebei, 050031, China
| | - Qianlan Bo
- Department of Neurology, The First Hospital of Hebei Medical University, Shijiangzhuang, Hebei 050031, China; Department of Neurology, Hebei Hospital of Xuanwu Hospital Capital Medical University, Shijiazhuang, Hebei, 050031, China
| | - Xiayue Wang
- Department of Neurology, The First Hospital of Hebei Medical University, Shijiangzhuang, Hebei 050031, China; Department of Neurology, Hebei Hospital of Xuanwu Hospital Capital Medical University, Shijiazhuang, Hebei, 050031, China
| | - Wei Wang
- Department of Neurology, The First Hospital of Hebei Medical University, Shijiangzhuang, Hebei 050031, China; Department of Neurology, Hebei Hospital of Xuanwu Hospital Capital Medical University, Shijiazhuang, Hebei, 050031, China
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23
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Wu Z, Hu Z, Gao Y, Xia Y, Zhang X, Jiang Z. A computational approach based on weighted gene co-expression network analysis for biomarkers analysis of Parkinson's disease and construction of diagnostic model. Front Comput Neurosci 2023; 16:1095676. [PMID: 36704228 PMCID: PMC9873349 DOI: 10.3389/fncom.2022.1095676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 12/23/2022] [Indexed: 01/11/2023] Open
Abstract
Background Parkinson's disease (PD) is a common age-related chronic neurodegenerative disease. There is currently no affordable, effective, and less invasive test for PD diagnosis. Metabolite profiling in blood and blood-based gene transcripts is thought to be an ideal method for diagnosing PD. Aim In this study, the objective is to identify the potential diagnostic biomarkers of PD by analyzing microarray gene expression data of samples from PD patients. Methods A computational approach, namely, Weighted Gene Co-expression Network Analysis (WGCNA) was used to construct co-expression gene networks and identify the key modules that were highly correlated with PD from the GSE99039 dataset. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was performed to identify the hub genes in the key modules with strong association with PD. The selected hub genes were then used to construct a diagnostic model based on logistic regression analysis, and the Receiver Operating Characteristic (ROC) curves were used to evaluate the efficacy of the model using the GSE99039 dataset. Finally, Reverse Transcription-Polymerase Chain Reaction (RT-PCR) was used to validate the hub genes. Results WGCNA identified two key modules associated with inflammation and immune response. Seven hub genes, LILRB1, LSP1, SIPA1, SLC15A3, MBOAT7, RNF24, and TLE3 were identified from the two modules and used to construct diagnostic models. ROC analysis showed that the diagnostic model had a good diagnostic performance for PD in the training and testing datasets. Results of the RT-PCR experiments showed that there were significant differences in the mRNA expression of LILRB1, LSP1, and MBOAT7 among the seven hub genes. Conclusion The 7-gene panel (LILRB1, LSP1, SIPA1, SLC15A3, MBOAT7, RNF24, and TLE3) will serve as a potential diagnostic signature for PD.
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Affiliation(s)
- Zhaoping Wu
- Department of Neurology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Zhiping Hu
- Department of Neurology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yunchun Gao
- Department of Neurology, The First People’s Hospital of Changde City, Changde, Hunan, China
| | - Yuechong Xia
- Department of Respiratory Medicine, Central South University, Changsha, Hunan, China
| | - Xiaobo Zhang
- Department of Neurology, The First People’s Hospital of Changde City, Changde, Hunan, China
| | - Zheng Jiang
- Department of Neurology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China,*Correspondence: Zheng Jiang,
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Huseby CJ, Delvaux E, Brokaw DL, Coleman PD. Blood RNA transcripts reveal similar and differential alterations in fundamental cellular processes in Alzheimer's disease and other neurodegenerative diseases. Alzheimers Dement 2022. [DOI: 10.1002/alz.12880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 09/30/2022] [Accepted: 10/21/2022] [Indexed: 12/24/2022]
Affiliation(s)
- Carol J. Huseby
- ASU‐Banner Neurodegenerative Disease Research Center Arizona State University Tempe Arizona USA
| | - Elaine Delvaux
- ASU‐Banner Neurodegenerative Disease Research Center Arizona State University Tempe Arizona USA
| | - Danielle L. Brokaw
- University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USA
| | - Paul D. Coleman
- ASU‐Banner Neurodegenerative Disease Research Center Arizona State University Tempe Arizona USA
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Hu S, Li S, Ning W, Huang X, Liu X, Deng Y, Franceschi D, Ogbuehi AC, Lethaus B, Savkovic V, Li H, Gaus S, Zimmerer R, Ziebolz D, Schmalz G, Huang S. Identifying crosstalk genetic biomarkers linking a neurodegenerative disease, Parkinson's disease, and periodontitis using integrated bioinformatics analyses. Front Aging Neurosci 2022; 14:1032401. [PMID: 36545026 PMCID: PMC9760933 DOI: 10.3389/fnagi.2022.1032401] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 11/16/2022] [Indexed: 12/12/2022] Open
Abstract
Objective To identify the genetic linkage mechanisms underlying Parkinson's disease (PD) and periodontitis, and explore the role of immunology in the crosstalk between both these diseases. Methods The gene expression omnibus (GEO) datasets associated with whole blood tissue of PD patients and gingival tissue of periodontitis patients were obtained. Then, differential expression analysis was performed to identify the differentially expressed genes (DEGs) deregulated in both diseases, which were defined as crosstalk genes. Inflammatory response-related genes (IRRGs) were downloaded from the MSigDB database and used for dividing case samples of both diseases into different clusters using k-means cluster analysis. Feature selection was performed using the LASSO model. Thus, the hub crosstalk genes were identified. Next, the crosstalk IRRGs were selected and Pearson correlation coefficient analysis was applied to investigate the correlation between hub crosstalk genes and hub IRRGs. Additionally, immune infiltration analysis was performed to examine the enrichment of immune cells in both diseases. The correlation between hub crosstalk genes and highly enriched immune cells was also investigated. Results Overall, 37 crosstalk genes were found to be overlapping between the PD-associated DEGs and periodontitis-associated DEGs. Using clustering analysis, the most optimal clustering effects were obtained for periodontitis and PD when k = 2 and k = 3, respectively. Using the LASSO feature selection, five hub crosstalk genes, namely, FMNL1, MANSC1, PLAUR, RNASE6, and TCIRG1, were identified. In periodontitis, MANSC1 was negatively correlated and the other four hub crosstalk genes (FMNL1, PLAUR, RNASE6, and TCIRG1) were positively correlated with five hub IRRGs, namely, AQP9, C5AR1, CD14, CSF3R, and PLAUR. In PD, all five hub crosstalk genes were positively correlated with all five hub IRRGs. Additionally, RNASE6 was highly correlated with myeloid-derived suppressor cells (MDSCs) in periodontitis, and MANSC1 was highly correlated with plasmacytoid dendritic cells in PD. Conclusion Five genes (i.e., FMNL1, MANSC1, PLAUR, RNASE6, and TCIRG1) were identified as crosstalk biomarkers linking PD and periodontitis. The significant correlation between these crosstalk genes and immune cells strongly suggests the involvement of immunology in linking both diseases.
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Affiliation(s)
- Shaonan Hu
- Stomatological Hospital, Southern Medical University, Guangzhou, China,*Correspondence: Shaonan Hu,
| | - Simin Li
- Stomatological Hospital, Southern Medical University, Guangzhou, China
| | - Wanchen Ning
- Stomatological Hospital, Southern Medical University, Guangzhou, China
| | - Xiuhong Huang
- Stomatological Hospital, Southern Medical University, Guangzhou, China
| | - Xiangqiong Liu
- Laboratory of Molecular Cell Biology, Beijing Tibetan Hospital, China Tibetology Research Center, Beijing, China
| | - Yupei Deng
- Laboratory of Molecular Cell Biology, Beijing Tibetan Hospital, China Tibetology Research Center, Beijing, China
| | - Debora Franceschi
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | | | - Bernd Lethaus
- Department of Cranio Maxillofacial Surgery, University Clinic Leipzig, Leipzig, Germany
| | - Vuk Savkovic
- Department of Cranio Maxillofacial Surgery, University Clinic Leipzig, Leipzig, Germany
| | - Hanluo Li
- Department of Cranio Maxillofacial Surgery, University Clinic Leipzig, Leipzig, Germany
| | - Sebastian Gaus
- Department of Cranio Maxillofacial Surgery, University Clinic Leipzig, Leipzig, Germany
| | - Rüdiger Zimmerer
- Department of Cranio Maxillofacial Surgery, University Clinic Leipzig, Leipzig, Germany
| | - Dirk Ziebolz
- Department of Cariology, Endodontology and Periodontology, University of Leipzig, Leipzig, Germany
| | - Gerhard Schmalz
- Department of Cariology, Endodontology and Periodontology, University of Leipzig, Leipzig, Germany
| | - Shaohong Huang
- Stomatological Hospital, Southern Medical University, Guangzhou, China,Shaohong Huang,
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Huseby CJ, Delvaux E, Brokaw DL, Coleman PD. Blood Transcript Biomarkers Selected by Machine Learning Algorithm Classify Neurodegenerative Diseases including Alzheimer's Disease. Biomolecules 2022; 12:1592. [PMID: 36358942 PMCID: PMC9687215 DOI: 10.3390/biom12111592] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 10/22/2022] [Accepted: 10/22/2022] [Indexed: 10/15/2023] Open
Abstract
The clinical diagnosis of neurodegenerative diseases is notoriously inaccurate and current methods are often expensive, time-consuming, or invasive. Simple inexpensive and noninvasive methods of diagnosis could provide valuable support for clinicians when combined with cognitive assessment scores. Biological processes leading to neuropathology progress silently for years and are reflected in both the central nervous system and vascular peripheral system. A blood-based screen to distinguish and classify neurodegenerative diseases is especially interesting having low cost, minimal invasiveness, and accessibility to almost any world clinic. In this study, we set out to discover a small set of blood transcripts that can be used to distinguish healthy individuals from those with Alzheimer's disease, Parkinson's disease, Huntington's disease, amyotrophic lateral sclerosis, Friedreich's ataxia, or frontotemporal dementia. Using existing public datasets, we developed a machine learning algorithm for application on transcripts present in blood and discovered small sets of transcripts that distinguish a number of neurodegenerative diseases with high sensitivity and specificity. We validated the usefulness of blood RNA transcriptomics for the classification of neurodegenerative diseases. Information about features selected for the classification can direct the development of possible treatment strategies.
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Affiliation(s)
- Carol J. Huseby
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ 85281, USA
| | - Elaine Delvaux
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ 85281, USA
| | - Danielle L. Brokaw
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Paul D. Coleman
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ 85281, USA
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The Potential Role of Voltage-Dependent Anion Channel in the Treatment of Parkinson’s Disease. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2022; 2022:4665530. [PMID: 36246397 PMCID: PMC9556184 DOI: 10.1155/2022/4665530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 09/11/2022] [Accepted: 09/20/2022] [Indexed: 11/18/2022]
Abstract
Parkinson’s disease (PD) is a neurodegenerative disease second only to Alzheimer’s disease in terms of prevalence. Previous studies have indicated that the occurrence and progression of PD are associated with mitochondrial dysfunction. Mitochondrial dysfunction is one of the most important causes for apoptosis of dopaminergic neurons. Therefore, maintaining the stability of mitochondrial functioning is a potential strategy in the treatment of PD. Voltage-dependent anion channel (VDAC) is the main component in the outer mitochondrial membrane, and it participates in a variety of biological processes. In this review, we focus on the potential roles of VDACs in the treatment of PD. We found that VDACs are involved in PD by regulating apoptosis, autophagy, and ferroptosis. VDAC1 oligomerization, VDACs ubiquitination, regulation of mitochondrial permeability transition pore (mPTP) by VDACs, and interaction between VDACs and α-synuclein (α-syn) are all promising methods for the treatment of PD. We proposed that inhibition of VDAC1 oligomerization and promotion of VDAC1 ubiquitination as an effective approach for the treatment of PD. Previous studies have proven that the expression of VDAC1 has a significant change in PD models. The expression levels of VDAC1 are decreased in the substantia nigra (SN) of patients suffering from PD compared with the control group consisting of normal individuals by using bioinformatics tools. VDAC2 is involved in PD mainly through the regulation of apoptosis. VDAC3 may have a similar function to VDAC1. It can be concluded that the functional roles of VDACs contribute to the therapeutic strategy of PD.
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Smith C, Guennewig B, Muller S. Robust subtractive stability measures for fast and exhaustive feature importance ranking and selection in generalised linear models. AUST NZ J STAT 2022. [DOI: 10.1111/anzs.12375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Connor Smith
- School of Mathematical and Physical Sciences Macquarie University Macquarie Park Australia
| | - Boris Guennewig
- Brain and Mind Centre School of Medical Sciences Faculty of Medicine and Health The University of Sydney Sydney Australia
| | - Samuel Muller
- School of Mathematical and Physical Sciences Macquarie University Macquarie Park Australia
- School of Mathematics and Statistics Faculty of Science The University of Sydney Sydney Australia
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Transcriptome Profiling Reveals Differential Expression of Circadian Behavior Genes in Peripheral Blood of Monozygotic Twins Discordant for Parkinson's Disease. Cells 2022; 11:cells11162599. [PMID: 36010675 PMCID: PMC9406852 DOI: 10.3390/cells11162599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 08/16/2022] [Accepted: 08/18/2022] [Indexed: 11/17/2022] Open
Abstract
Parkinson’s disease (PD) is one of the most common neurodegenerative diseases. Investigating individuals with the most identical genetic background is optimal for minimizing the genetic contribution to gene expression. These individuals include monozygotic twins discordant for PD. Monozygotic twins have the same genetic background, age, sex, and often similar environmental conditions. The aim of this study was to carry out a transcriptome analysis of the peripheral blood of three pairs of monozygotic twins discordant for PD. We identified the metabolic process “circadian behavior” as a priority process for further study. Different expression of genes included in the term “circadian behavior” confirms that this process is involved in PD pathogenesis. We found increased expression of three genes associated with circadian behavior, i.e., PTGDS, ADORA2A, and MTA1, in twins with PD. These genes can be considered as potential candidate genes for this disease.
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Augustine J, Jereesh AS. Blood-based gene-expression biomarkers identification for the non-invasive diagnosis of Parkinson's disease using two-layer hybrid feature selection. Gene X 2022; 823:146366. [PMID: 35202733 DOI: 10.1016/j.gene.2022.146366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Revised: 02/15/2022] [Accepted: 02/18/2022] [Indexed: 11/19/2022] Open
Abstract
Parkinson's disease (PD) is one of the most prevalent neurodegenerative diseases. Understanding the molecular mechanism and identifying potential biomarkers of PD promote effective treatments to the patients. Due to less invasiveness and easy accessibility, biomarkers from blood support early detection and diagnosis of PD. This study combined three independent PD microarray gene expression data from blood samples applying the early integration approach. Moderated t-statistics was employed to identify differentially expressed genes (DEGs). Relevant genes were selected using a two-layer embedded wrapper feature selection method with gradient boosting machine (GBM) in the first layer followed by an ensemble of wrappers including Recursive Feature Elimination (RFE), Genetic algorithm (GA) and Bi-directional elimination (Stepwise). All three wrappers were based on logistic regression classifier (LR). The PD-predictability of the generated signature was tested using nine supervised classification models, including eight shallow machine learning and one deep learning. On an independent dataset, GSE72267, Support Vector Machine-Radial (SVMR), and Deep Neural Network (DNN) showed the best performance with AUC 0.821 and 0.82, respectively. Comparison with existing blood-based PD signatures and the biological analysis verified the reliability of the proposed signature.
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Affiliation(s)
- Jisha Augustine
- Bioinformatics Lab, Department of Computer Science, Cochin University of Science and Technology, Kerala 682022, India.
| | - A S Jereesh
- Bioinformatics Lab, Department of Computer Science, Cochin University of Science and Technology, Kerala 682022, India.
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Pantaleo E, Monaco A, Amoroso N, Lombardi A, Bellantuono L, Urso D, Lo Giudice C, Picardi E, Tafuri B, Nigro S, Pesole G, Tangaro S, Logroscino G, Bellotti R. A Machine Learning Approach to Parkinson’s Disease Blood Transcriptomics. Genes (Basel) 2022; 13:genes13050727. [PMID: 35627112 PMCID: PMC9141063 DOI: 10.3390/genes13050727] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 04/16/2022] [Accepted: 04/18/2022] [Indexed: 12/23/2022] Open
Abstract
The increased incidence and the significant health burden associated with Parkinson’s disease (PD) have stimulated substantial research efforts towards the identification of effective treatments and diagnostic procedures. Despite technological advancements, a cure is still not available and PD is often diagnosed a long time after onset when irreversible damage has already occurred. Blood transcriptomics represents a potentially disruptive technology for the early diagnosis of PD. We used transcriptome data from the PPMI study, a large cohort study with early PD subjects and age matched controls (HC), to perform the classification of PD vs. HC in around 550 samples. Using a nested feature selection procedure based on Random Forests and XGBoost we reached an AUC of 72% and found 493 candidate genes. We further discussed the importance of the selected genes through a functional analysis based on GOs and KEGG pathways.
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Affiliation(s)
- Ester Pantaleo
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Via A. Orabona 4, 70125 Bari, Italy; (E.P.); (A.M.); (N.A.); (L.B.); (S.T.); (R.B.)
- Dipartimento di Scienze Mediche di Base, Neuroscienze e Organi di Senso, Università degli Studi di Bari Aldo Moro, Piazza G. Cesare 11, 70124 Bari, Italy;
- Dipartimento Interateneo di Fisica M. Merlin, Università degli Studi di Bari Aldo Moro, Via G. Amendola 173, 70125 Bari, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Via A. Orabona 4, 70125 Bari, Italy; (E.P.); (A.M.); (N.A.); (L.B.); (S.T.); (R.B.)
| | - Nicola Amoroso
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Via A. Orabona 4, 70125 Bari, Italy; (E.P.); (A.M.); (N.A.); (L.B.); (S.T.); (R.B.)
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Via A. Orabona 4, 70125 Bari, Italy
| | - Angela Lombardi
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Via A. Orabona 4, 70125 Bari, Italy; (E.P.); (A.M.); (N.A.); (L.B.); (S.T.); (R.B.)
- Dipartimento Interateneo di Fisica M. Merlin, Università degli Studi di Bari Aldo Moro, Via G. Amendola 173, 70125 Bari, Italy
- Correspondence:
| | - Loredana Bellantuono
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Via A. Orabona 4, 70125 Bari, Italy; (E.P.); (A.M.); (N.A.); (L.B.); (S.T.); (R.B.)
- Dipartimento di Scienze Mediche di Base, Neuroscienze e Organi di Senso, Università degli Studi di Bari Aldo Moro, Piazza G. Cesare 11, 70124 Bari, Italy;
| | - Daniele Urso
- Centro per le Malattie Neurodegenerative e l’Invecchiamento Cerebrale, Dipartimento di Ricerca Clinica in Neurologia, Università degli Studi di Bari Aldo Moro, Pia Fondazione Cardinale G. Panico, 73039 Tricase, Italy; (D.U.); (B.T.); (S.N.)
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, De Crespigny Park, London SE5 8AF, UK
| | - Claudio Lo Giudice
- Dipartimento di Bioscienze, Biotecnologie e Biofarmaceutica, Università degli Studi di Bari Aldo Moro, Via A. Orabona 4, 70125 Bari, Italy; (C.L.G.); (E.P.); (G.P.)
| | - Ernesto Picardi
- Dipartimento di Bioscienze, Biotecnologie e Biofarmaceutica, Università degli Studi di Bari Aldo Moro, Via A. Orabona 4, 70125 Bari, Italy; (C.L.G.); (E.P.); (G.P.)
- Istituto di Biomembrane, Bioenergetica e Biotecnologie Molecolari, Consiglio Nazionale delle Ricerche, Via G. Amendola 122/O, 70126 Bari, Italy
| | - Benedetta Tafuri
- Centro per le Malattie Neurodegenerative e l’Invecchiamento Cerebrale, Dipartimento di Ricerca Clinica in Neurologia, Università degli Studi di Bari Aldo Moro, Pia Fondazione Cardinale G. Panico, 73039 Tricase, Italy; (D.U.); (B.T.); (S.N.)
| | - Salvatore Nigro
- Centro per le Malattie Neurodegenerative e l’Invecchiamento Cerebrale, Dipartimento di Ricerca Clinica in Neurologia, Università degli Studi di Bari Aldo Moro, Pia Fondazione Cardinale G. Panico, 73039 Tricase, Italy; (D.U.); (B.T.); (S.N.)
- Istituto di Nanotecnologia (NANOTEC), Consiglio Nazionale delle Ricerche, Via Monteroni, 73100 Lecce, Italy
| | - Graziano Pesole
- Dipartimento di Bioscienze, Biotecnologie e Biofarmaceutica, Università degli Studi di Bari Aldo Moro, Via A. Orabona 4, 70125 Bari, Italy; (C.L.G.); (E.P.); (G.P.)
- Istituto di Biomembrane, Bioenergetica e Biotecnologie Molecolari, Consiglio Nazionale delle Ricerche, Via G. Amendola 122/O, 70126 Bari, Italy
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Via A. Orabona 4, 70125 Bari, Italy; (E.P.); (A.M.); (N.A.); (L.B.); (S.T.); (R.B.)
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, Via A. Orabona 4, 70125 Bari, Italy
| | - Giancarlo Logroscino
- Dipartimento di Scienze Mediche di Base, Neuroscienze e Organi di Senso, Università degli Studi di Bari Aldo Moro, Piazza G. Cesare 11, 70124 Bari, Italy;
- Centro per le Malattie Neurodegenerative e l’Invecchiamento Cerebrale, Dipartimento di Ricerca Clinica in Neurologia, Università degli Studi di Bari Aldo Moro, Pia Fondazione Cardinale G. Panico, 73039 Tricase, Italy; (D.U.); (B.T.); (S.N.)
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Via A. Orabona 4, 70125 Bari, Italy; (E.P.); (A.M.); (N.A.); (L.B.); (S.T.); (R.B.)
- Dipartimento Interateneo di Fisica M. Merlin, Università degli Studi di Bari Aldo Moro, Via G. Amendola 173, 70125 Bari, Italy
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Multiple Criteria Optimization (MCO): A gene selection deterministic tool in RStudio. PLoS One 2022; 17:e0262890. [PMID: 35085348 PMCID: PMC8794188 DOI: 10.1371/journal.pone.0262890] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 01/09/2022] [Indexed: 11/19/2022] Open
Abstract
Identifying genes with the largest expression changes (gene selection) to characterize a given condition is a popular first step to drive exploration into molecular mechanisms and is, therefore, paramount for therapeutic development. Reproducibility in the sciences makes it necessary to emphasize objectivity and systematic repeatability in biological and informatics analyses, including gene selection. With these two characteristics in mind, in previous works our research team has proposed using multiple criteria optimization (MCO) in gene selection to analyze microarray datasets. The result of this effort is the MCO algorithm, which selects genes with the largest expression changes without user manipulation of neither informatics nor statistical parameters. Furthermore, the user is not required to choose either a preference structure among multiple measures or a predetermined quantity of genes to be deemed significant a priori. This implies that using the same datasets and performance measures (PMs), the method will converge to the same set of selected differentially expressed genes (repeatability) despite who carries out the analysis (objectivity). The present work describes the development of an open-source tool in RStudio to enable both: (1) individual analysis of single datasets with two or three PMs and (2) meta-analysis with up to five microarray datasets, using one PM from each dataset. The capabilities afforded by the code include license-free portability and the possibility to carry out analyses via modest computer hardware, such as personal laptops. The code provides affordable, repeatable, and objective detection of differentially expressed genes from microarrays. It can be used to analyze other experiments with similar experimental comparative layouts, such as microRNA arrays and protein arrays, among others. As a demonstration of the capabilities of the code, the analysis of four publicly-available microarray datasets related to Parkinson´s Disease (PD) is presented here, treating each dataset individually or as a four-way meta-analysis. These MCO-supported analyses made it possible to identify MMP9 and TUBB2A as potential PD genetic biomarkers based on their persistent appearance across each of the case studies. A literature search confirmed the importance of these genes in PD and indeed as PD biomarkers, which evidences the code´s potential.
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Longitudinal metabolomics profiling of serum amino acids in rotenone-induced Parkinson's mouse model. Amino Acids 2022; 54:111-121. [PMID: 35028704 DOI: 10.1007/s00726-021-03117-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 12/01/2021] [Indexed: 11/27/2022]
Abstract
Recently, the detailed etiology and pathogenesis of Parkinson's disease (PD) have not been fully clarified yet. Increasing evidences suggested that the disturbance of peripheral branched-chain amino acids (BCAAs) metabolism can promote the occurrence and progression of neurodegenerative diseases through neuroinflammatory signaling. Although there are several studies on the metabolomics of PD, longitudinal study of metabolic pathways is still lacking. Therefore, the purpose of the present study was to determine the longitudinal alterations in serum amino acid profiles in PD mouse model. Gas chromatography-mass spectrometry (GC-MS) was applied to detect serum amino acid concentrations in C57BL/6 mice after 0, 3 and 4 weeks of oral administration with rotenone. Then the data were analysed by principal component analysis (PCA) and orthogonal projection to latent structures (OPLS) analysis. Finally, the correlations between different kinds of serum amino acids and behaviors in rotenone-treated mice were also explored. Compared with 0-week mice, the levels of L-isoleucine and L-leucine were down-regulated in 3-week and 4-week mice, especially in 4-week mice. Moreover, the comprehensive analysis showed that L-isoleucine and L-leucine were negatively correlated with pole-climbing time and positively correlated with fecal weight and water content of PD mice. These results not only suggested that L-isoleucine and L-leucine may be potential biomarkers, but also pointed out the possibility of treating PD by intervening in the circulating amino acids metabolism.
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Parkinson's Disease Subtyping Using Clinical Features and Biomarkers: Literature Review and Preliminary Study of Subtype Clustering. Diagnostics (Basel) 2022; 12:diagnostics12010112. [PMID: 35054279 PMCID: PMC8774435 DOI: 10.3390/diagnostics12010112] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 12/31/2021] [Accepted: 01/03/2022] [Indexed: 12/29/2022] Open
Abstract
The second most common progressive neurodegenerative disorder, Parkinson’s disease (PD), is characterized by a broad spectrum of symptoms that are associated with its progression. Several studies have attempted to classify PD according to its clinical manifestations and establish objective biomarkers for early diagnosis and for predicting the prognosis of the disease. Recent comprehensive research on the classification of PD using clinical phenotypes has included factors such as dominance, severity, and prognosis of motor and non-motor symptoms and biomarkers. Additionally, neuroimaging studies have attempted to reveal the pathological substrate for motor symptoms. Genetic and transcriptomic studies have contributed to our understanding of the underlying molecular pathogenic mechanisms and provided a basis for classifying PD. Moreover, an understanding of the heterogeneity of clinical manifestations in PD is required for a personalized medicine approach. Herein, we discuss the possible subtypes of PD based on clinical features, neuroimaging, and biomarkers for developing personalized medicine for PD. In addition, we conduct a preliminary clustering using gait features for subtyping PD. We believe that subtyping may facilitate the development of therapeutic strategies for PD.
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Yalçin M, Malhan D, Basti A, Peralta AR, Ferreira JJ, Relógio A. A Computational Analysis in a Cohort of Parkinson's Disease Patients and Clock-Modified Colorectal Cancer Cells Reveals Common Expression Alterations in Clock-Regulated Genes. Cancers (Basel) 2021; 13:cancers13235978. [PMID: 34885088 PMCID: PMC8657387 DOI: 10.3390/cancers13235978] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 11/18/2021] [Accepted: 11/21/2021] [Indexed: 12/13/2022] Open
Abstract
Simple Summary Cancer and neurodegenerative diseases are two aging-related pathologies with differential developmental characteristics, but they share altered cellular pathways. Interestingly, dysregulations in the biological clock are reported in both diseases, though the extent and potential consequences of such disruption have not been fully elucidated. In this study, we aimed at characterizing global changes on common cellular pathways associated with Parkinson’s disease (PD) and colorectal cancer (CRC). We used gene expression data retrieved from an idiopathic PD (IPD) patient cohort and from CRC cells with unmodified versus genetically altered clocks. Our results highlight common differentially expressed genes between IPD patients and cells with disrupted clocks, suggesting a role for the circadian clock in the regulation of pathways altered in both pathologies. Interestingly, several of these genes are related to cancer hallmarks and may have an impact on the overall survival of colon cancer patients, as suggested by our analysis. Abstract Increasing evidence suggests a role for circadian dysregulation in prompting disease-related phenotypes in mammals. Cancer and neurodegenerative disorders are two aging related diseases reported to be associated with circadian disruption. In this study, we investigated a possible effect of circadian disruption in Parkinson’s disease (PD) and colorectal cancer (CRC). We used high-throughput data sets retrieved from whole blood of idiopathic PD (IPD) patients and time course data sets derived from an in vitro model of CRC including the wildtype and three core-clock knockout (KO) cell lines. Several gene expression alterations in IPD patients resembled the expression profiles in the core-clock KO cells. These include expression changes in DBP, GBA, TEF, SNCA, SERPINA1 and TGFB1. Notably, our results pointed to alterations in the core-clock network in IPD patients when compared to healthy controls and revealed variations in the expression profile of PD-associated genes (e.g., HRAS and GBA) upon disruption of the core-clock genes. Our study characterizes changes at the transcriptomic level following circadian clock disruption on common cellular pathways associated with cancer and neurodegeneration (e.g., immune system, energy metabolism and RNA processing), and it points to a significant influence on the overall survival of colon cancer patients for several genes resulting from our analysis (e.g., TUBB6, PAK6, SLC11A1).
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Affiliation(s)
- Müge Yalçin
- Institute for Theoretical Biology (ITB), Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, 10117 Berlin, Germany; (M.Y.); (D.M.); (A.B.)
- Molecular Cancer Research Center (MKFZ), Medical Department of Hematology, Oncology, and Tumour Immunology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Deeksha Malhan
- Institute for Theoretical Biology (ITB), Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, 10117 Berlin, Germany; (M.Y.); (D.M.); (A.B.)
- Molecular Cancer Research Center (MKFZ), Medical Department of Hematology, Oncology, and Tumour Immunology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, 10117 Berlin, Germany
- Institute for Systems Medicine and Faculty of Human Medicine, MSH Medical School Hamburg, 20457 Hamburg, Germany
| | - Alireza Basti
- Institute for Theoretical Biology (ITB), Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, 10117 Berlin, Germany; (M.Y.); (D.M.); (A.B.)
- Molecular Cancer Research Center (MKFZ), Medical Department of Hematology, Oncology, and Tumour Immunology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, 10117 Berlin, Germany
- Institute for Systems Medicine and Faculty of Human Medicine, MSH Medical School Hamburg, 20457 Hamburg, Germany
| | - Ana Rita Peralta
- EEG/Sleep Laboratory, Department Neurosciences and Mental Health, Hospital de Santa Maria—CHULN, 1649-035 Lisbon, Portugal;
- Department of Neurology, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisbon, Portugal
- Instituto de Fisiologia, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisbon, Portugal
- CNS-Campus Neurológico Senior, 2560-280 Torres Vedras, Portugal;
| | - Joaquim J. Ferreira
- CNS-Campus Neurológico Senior, 2560-280 Torres Vedras, Portugal;
- Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisbon, Portugal
- Laboratory of Clinical Pharmacology and Therapeutics, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisbon, Portugal
| | - Angela Relógio
- Institute for Theoretical Biology (ITB), Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, 10117 Berlin, Germany; (M.Y.); (D.M.); (A.B.)
- Molecular Cancer Research Center (MKFZ), Medical Department of Hematology, Oncology, and Tumour Immunology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, 10117 Berlin, Germany
- Institute for Systems Medicine and Faculty of Human Medicine, MSH Medical School Hamburg, 20457 Hamburg, Germany
- Correspondence: or
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Song W, Wang W, Liu Z, Cai W, Yu S, Zhao M, Lin GN. A Comprehensive Evaluation of Cross-Omics Blood-Based Biomarkers for Neuropsychiatric Disorders. J Pers Med 2021; 11:1247. [PMID: 34945719 PMCID: PMC8703948 DOI: 10.3390/jpm11121247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 11/22/2021] [Accepted: 11/23/2021] [Indexed: 12/03/2022] Open
Abstract
The identification of peripheral multi-omics biomarkers of brain disorders has long been hindered by insufficient sample size and confounder influence. This study aimed to compare biomarker potential for different molecules and diseases. We leveraged summary statistics of five blood quantitative trait loci studies (N = 1980 to 22,609) and genome-wide association studies (N = 9725 to 500,199) from 14 different brain disorders, such as Schizophrenia (SCZ) and Alzheimer's Disease (AD). We applied summary-based and two-sample Mendelian Randomization to estimate the associations between blood molecules and brain disorders. We identified 524 RNA, 807 methylation sites, 29 proteins, seven cytokines, and 22 metabolites having a significant association with at least one of 14 brain disorders. Simulation analyses indicated that a cross-omics combination of biomarkers had better performance for most disorders, and different disorders could associate with different omics. We identified an 11-methylation-site model for SCZ diagnosis (Area Under Curve, AUC = 0.74) by analyzing selected candidate markers in published datasets (total N = 6098). Moreover, we constructed an 18-methylation-sites model that could predict the prognosis of elders with mild cognitive impairment (hazard ratio = 2.32). We provided an association landscape between blood cross-omic biomarkers and 14 brain disorders as well as a suggestion guide for future clinical discovery and application.
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Affiliation(s)
- Weichen Song
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; (W.S.); (W.W.); (Z.L.); (W.C.)
| | - Weidi Wang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; (W.S.); (W.W.); (Z.L.); (W.C.)
| | - Zhe Liu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; (W.S.); (W.W.); (Z.L.); (W.C.)
| | - Wenxiang Cai
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; (W.S.); (W.W.); (Z.L.); (W.C.)
| | - Shunying Yu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai 200030, China; (S.Y.); (M.Z.)
| | - Min Zhao
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai 200030, China; (S.Y.); (M.Z.)
| | - Guan Ning Lin
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; (W.S.); (W.W.); (Z.L.); (W.C.)
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Zoo: Selecting Transcriptomic and Methylomic Biomarkers by Ensembling Animal-Inspired Swarm Intelligence Feature Selection Algorithms. Genes (Basel) 2021; 12:genes12111814. [PMID: 34828418 PMCID: PMC8621246 DOI: 10.3390/genes12111814] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 11/12/2021] [Accepted: 11/15/2021] [Indexed: 02/03/2023] Open
Abstract
Biological omics data such as transcriptomes and methylomes have the inherent “large p small n” paradigm, i.e., the number of features is much larger than that of the samples. A feature selection (FS) algorithm selects a subset of the transcriptomic or methylomic biomarkers in order to build a better prediction model. The hidden patterns in the FS solution space make it challenging to achieve a feature subset with satisfying prediction performances. Swarm intelligence (SI) algorithms mimic the target searching behaviors of various animals and have demonstrated promising capabilities in selecting features with good machine learning performances. Our study revealed that different SI-based feature selection algorithms contributed complementary searching capabilities in the FS solution space, and their collaboration generated a better feature subset than the individual SI feature selection algorithms. Nine SI-based feature selection algorithms were integrated to vote for the selected features, which were further refined by the dynamic recursive feature elimination framework. In most cases, the proposed Zoo algorithm outperformed the existing feature selection algorithms on transcriptomics and methylomics datasets.
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Complementary Transcriptomic and Proteomic Analysis in the Substantia Nigra of Parkinson's Disease. DISEASE MARKERS 2021; 2021:2148820. [PMID: 34659588 PMCID: PMC8517625 DOI: 10.1155/2021/2148820] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 09/16/2021] [Accepted: 09/21/2021] [Indexed: 12/18/2022]
Abstract
Parkinson's disease (PD) is a disease that involves brain damage and is associated with neuroinflammation, mitochondrial damage, and cell aging. However, the pathogenic mechanism of PD is still unknown. Sequencing data and proteomic data can describe the fluctuation of molecular abundance in diseases at the mRNA level and protein level, respectively. In order to explore new targets in the pathogenesis of PD, the study analyzed molecular changes from the database by combining transcriptomic and proteomic analysis. Differentially expressed genes and differentially abundant proteins were summarized and analyzed. Enrichment and cluster analysis emphasized the importance of neurotransmitter release, mitochondrial damage, and vesicle transport. The molecular network revealed a subnetwork of 9 molecules related to SCNA and TH and revealed hub gene with differential expression at both mRNA and protein levels. It found that ACHE and CADPS could be used as new targets in PD, emphasizing that impaired nerve signal transmission and vesicle transport affect the pathogenesis of PD. Our research emphasized that the joint analysis and verification of transcriptomics and proteomics were devoted to understanding the comprehensive views and mechanism of pathogenesis in PD.
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E. B, D. B, Elumalai VK, R. V. Automatic and non-invasive Parkinson’s disease diagnosis and severity rating using LSTM network. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107463] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Quan P, Wang K, Yan S, Wen S, Wei C, Zhang X, Cao J, Yao L. Integrated network analysis identifying potential novel drug candidates and targets for Parkinson's disease. Sci Rep 2021; 11:13154. [PMID: 34162989 PMCID: PMC8222400 DOI: 10.1038/s41598-021-92701-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 06/14/2021] [Indexed: 11/09/2022] Open
Abstract
This study aimed to identify potential novel drug candidates and targets for Parkinson's disease. First, 970 genes that have been reported to be related to PD were collected from five databases, and functional enrichment analysis of these genes was conducted to investigate their potential mechanisms. Then, we collected drugs and related targets from DrugBank, narrowed the list by proximity scores and Inverted Gene Set Enrichment analysis of drug targets, and identified potential drug candidates for PD treatment. Finally, we compared the expression distribution of the candidate drug-target genes between the PD group and the control group in the public dataset with the largest sample size (GSE99039) in Gene Expression Omnibus. Ten drugs with an FDR < 0.1 and their corresponding targets were identified. Some target genes of the ten drugs significantly overlapped with PD-related genes or already known therapeutic targets for PD. Nine differentially expressed drug-target genes with p < 0.05 were screened. This work will facilitate further research into the possible efficacy of new drugs for PD and will provide valuable clues for drug design.
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Affiliation(s)
- Pusheng Quan
- Department of Neurology, The First Affiliated Hospital, Harbin Medical University, Harbin, 150081, China
| | - Kai Wang
- Center of TOF-PET/CT/MR, The Fourth Affiliated Hospital, Harbin Medical University, Harbin, 150081, China
| | - Shi Yan
- Department of Neurology, The First Affiliated Hospital, Harbin Medical University, Harbin, 150081, China
| | - Shirong Wen
- Department of Neurology, The First Affiliated Hospital, Harbin Medical University, Harbin, 150081, China
| | - Chengqun Wei
- Department of General Practice, Heilongjiang Provincial Hospital, Harbin, 150081, China
| | - Xinyu Zhang
- Department of Neurology, The First Affiliated Hospital, Harbin Medical University, Harbin, 150081, China
| | - Jingwei Cao
- Department of Neurology, The First Affiliated Hospital, Harbin Medical University, Harbin, 150081, China
| | - Lifen Yao
- Department of Neurology, The First Affiliated Hospital, Harbin Medical University, Harbin, 150081, China.
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Wang J, Lan Y, He L, Tang R, Li Y, Huang Y, Liang S, Gao Z, Price M, Yue B, He M, Guo T, Fan Z. Sex-specific gene expression in the blood of four primates. Genomics 2021; 113:2605-2613. [PMID: 34116169 DOI: 10.1016/j.ygeno.2021.06.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 05/03/2021] [Accepted: 06/06/2021] [Indexed: 10/21/2022]
Abstract
Blood is an important non-reproductive tissue, but little is known about the sex-specific gene expressions in the blood. Therefore, we investigated sex-specific gene expression differences in the blood tissues of four primates, rhesus macaques (Macaca mulatta), Tibetan macaques (M. thibetana), yellow baboons (Papio cynocephalus), and humans. We identified seven sex-specific differentially expressed genes (SDEGs) in each non-human primate and 31 SDEGs in humans. The four primates had only one common SDEG, MAP7D2. In humans, immune-related SDEGs were identified as up-regulated, but also down-regulated in females. We also found that most of the X-Y gene pairs had similar expression levels between species, except pair EIF1AY/EIF1AX. The expression level of X-Y gene pairs of rhesus and Tibetan macaques showed no significant differential expression levels, while humans had six significant XY-biased and three XX-biased X-Y gene pairs. Our observed sex differences in blood should increase understanding of sex differences in primate blood tissue.
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Affiliation(s)
- Jiao Wang
- Key Laboratory of Bioresources and Eco-Environment (Ministry of Education), College of Life Sciences, Sichuan University, Chengdu 610065, Sichuan, China
| | - Yue Lan
- Key Laboratory of Bioresources and Eco-Environment (Ministry of Education), College of Life Sciences, Sichuan University, Chengdu 610065, Sichuan, China
| | - Lewei He
- Key Laboratory of Bioresources and Eco-Environment (Ministry of Education), College of Life Sciences, Sichuan University, Chengdu 610065, Sichuan, China
| | - Ruixiang Tang
- Sichuan Key Laboratory of Conservation Biology on Endangered Wildlife, College of Life Sciences, Sichuan University, Chengdu 610064, Sichuan, China
| | - Yuhui Li
- Institute of Blood Transfusion, Chinese Academy of Medical Sciences, Chengdu 610052, Sichuan, China
| | - Yuan Huang
- Medical Laboratory Department of West China Fourth Hospital, Sichuan University, Chengdu 610000, Sichuan, China
| | - Shan Liang
- Institute of Blood Transfusion, Chinese Academy of Medical Sciences, Chengdu 610052, Sichuan, China
| | - Zhan Gao
- Institute of Blood Transfusion, Chinese Academy of Medical Sciences, Chengdu 610052, Sichuan, China
| | - Megan Price
- Key Laboratory of Bioresources and Eco-Environment (Ministry of Education), College of Life Sciences, Sichuan University, Chengdu 610065, Sichuan, China.
| | - Bisong Yue
- Sichuan Key Laboratory of Conservation Biology on Endangered Wildlife, College of Life Sciences, Sichuan University, Chengdu 610064, Sichuan, China.
| | - Miao He
- Institute of Blood Transfusion, Chinese Academy of Medical Sciences, Chengdu 610052, Sichuan, China.
| | - Tao Guo
- Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, China.
| | - Zhenxin Fan
- Key Laboratory of Bioresources and Eco-Environment (Ministry of Education), College of Life Sciences, Sichuan University, Chengdu 610065, Sichuan, China.
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42
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Lin Z, Zhou L, Li Y, Liu S, Xie Q, Xu X, Wu J. Identification of potential genomic biomarkers for Parkinson's disease using data pooling of gene expression microarrays. Biomark Med 2021; 15:585-595. [PMID: 33988461 DOI: 10.2217/bmm-2020-0325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Aim: In this study, we aimed to identify potential diagnostic biomarkers Parkinson's disease (PD) by exploring microarray gene expression data of PD patients. Materials & methods: Differentially expressed genes associated with PD were screened from the GSE99039 dataset using weighted gene co-expression network analysis, followed by gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses, gene-gene interaction network analysis and receiver operator characteristics analysis. Results: We identified two PD-associated modules, in which genes from the chemokine signaling pathway were primarily enriched. In particular, CS, PRKCD, RHOG and VAMP2 directly interacted with known PD-associated genes and showed higher expression in the PD samples, and may thus be potential biomarkers in PD diagnosis. Conclusion: A DFG-analysis identified a four-gene panel (CS, PRKCD, RHOG, VAMP2) as a potential diagnostic predictor to diagnose PD.
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Affiliation(s)
- Zhijian Lin
- Department of Neurology, Peking University Shenzhen Hospital, Shenzhen, 518036, PR China
| | - Lishu Zhou
- Department of Neurology, Peking University Shenzhen Hospital, Shenzhen, 518036, PR China.,The Clinical College of Peking University, Shenzhen Hospital of Anhui Medical University, Shenzhen, 518036, PR China
| | - Yaosha Li
- Department of Neurology, Peking University Shenzhen Hospital, Shenzhen, 518036, PR China
| | - Suni Liu
- Department of Neurology, Peking University Shenzhen Hospital, Shenzhen, 518036, PR China
| | - Qizhi Xie
- Department of Neurology, Peking University Shenzhen Hospital, Shenzhen, 518036, PR China
| | - Xu Xu
- College of Life Sciences & Oceanography, Shenzhen University, Shenzhen, 518060, PR China
| | - Jun Wu
- Department of Neurology, Peking University Shenzhen Hospital, Shenzhen, 518036, PR China
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43
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Gezen-Ak D, Alaylıoğlu M, Genç G, Şengül B, Keskin E, Sordu P, Güleç ZEK, Apaydın H, Bayram-Gürel Ç, Ulutin T, Yılmazer S, Ertan S, Dursun E. Altered Transcriptional Profile of Mitochondrial DNA-Encoded OXPHOS Subunits, Mitochondria Quality Control Genes, and Intracellular ATP Levels in Blood Samples of Patients with Parkinson's Disease. J Alzheimers Dis 2021; 74:287-307. [PMID: 32007957 DOI: 10.3233/jad-191164] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Mitochondrial dysfunctions are significant contributors to neurodegeneration. One result or a cause of mitochondrial dysfunction might be the disruption of mtDNA transcription. Limited data indicated an altered expression of mtDNA encoded transcripts in Alzheimer's disease (AD) or Parkinson's disease (PD). The number of mitochondria is high in cells with a high energy demand, such as muscle or nerve cells. AD or PD involves increased risk of cardiomyopathy, suggesting that mitochondrial dysfunction might be systemic. If it is systemic, we should observe it in different cell types. Given that, we wanted to investigate any disruption in the regulation of mtDNA encoded gene expression in addition to PINK1, PARKIN, and ATP levels in peripheral blood samples of PD cases who are affected by a neurodegenerative disorder that is very well known by its mitochondrial aspects. Our results showed for the first time that: 1) age of onset > 50 PD sporadic (PDS) cases: mtDNA transcription and quality control genes were affected; 2) age of onset <50 PDS cases: only mtDNA transcription was affected; and 3) PD cases with familial background: only quality control genes were affected. mtDNA copy number was not a confounder. Intracellular ATP levels of PD case subgroups were significantly higher than those of healthy subjects. We suggest that a systemic dysregulation of transcription of mtDNA or mitochondrial quality control genes might result in the development of a sporadic form of the disease. Additionally, ATP elevation might be an independent compensatory and response mechanism. Hyperactive cells in AD and PD require further investigation.
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Affiliation(s)
- Duygu Gezen-Ak
- Department of Medical Biology, Brain and Neurodegenerative Disorders Research Laboratories, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Merve Alaylıoğlu
- Department of Medical Biology, Brain and Neurodegenerative Disorders Research Laboratories, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Gençer Genç
- Department of Neurology, Şişli Etfal Training and Research Hospital, Istanbul, Turkey
| | - Büşra Şengül
- Department of Medical Biology, Brain and Neurodegenerative Disorders Research Laboratories, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Ebru Keskin
- Department of Medical Biology, Brain and Neurodegenerative Disorders Research Laboratories, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Pelin Sordu
- Department of Medical Biology, Brain and Neurodegenerative Disorders Research Laboratories, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Zeynep Ece Kaya Güleç
- Department of Neurology, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Hülya Apaydın
- Department of Neurology, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Çiğdem Bayram-Gürel
- Department of Medical Biology, Brain and Neurodegenerative Disorders Research Laboratories, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Turgut Ulutin
- Department of Medical Biology, Brain and Neurodegenerative Disorders Research Laboratories, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Selma Yılmazer
- Department of Medical Biology, Faculty of Medicine, Altınbaş University, Istanbul, Turkey
| | - Sibel Ertan
- Department of Neurology, Faculty of Medicine, Koç University, Istanbul, Turkey
| | - Erdinç Dursun
- Department of Medical Biology, Brain and Neurodegenerative Disorders Research Laboratories, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey.,Department of Neuroscience, Institute of Neurological Sciences, Istanbul University-Cerrahpasa, Istanbul, Turkey
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44
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Mei J, Desrosiers C, Frasnelli J. Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature. Front Aging Neurosci 2021; 13:633752. [PMID: 34025389 PMCID: PMC8134676 DOI: 10.3389/fnagi.2021.633752] [Citation(s) in RCA: 76] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 03/22/2021] [Indexed: 12/26/2022] Open
Abstract
Diagnosis of Parkinson's disease (PD) is commonly based on medical observations and assessment of clinical signs, including the characterization of a variety of motor symptoms. However, traditional diagnostic approaches may suffer from subjectivity as they rely on the evaluation of movements that are sometimes subtle to human eyes and therefore difficult to classify, leading to possible misclassification. In the meantime, early non-motor symptoms of PD may be mild and can be caused by many other conditions. Therefore, these symptoms are often overlooked, making diagnosis of PD at an early stage challenging. To address these difficulties and to refine the diagnosis and assessment procedures of PD, machine learning methods have been implemented for the classification of PD and healthy controls or patients with similar clinical presentations (e.g., movement disorders or other Parkinsonian syndromes). To provide a comprehensive overview of data modalities and machine learning methods that have been used in the diagnosis and differential diagnosis of PD, in this study, we conducted a literature review of studies published until February 14, 2020, using the PubMed and IEEE Xplore databases. A total of 209 studies were included, extracted for relevant information and presented in this review, with an investigation of their aims, sources of data, types of data, machine learning methods and associated outcomes. These studies demonstrate a high potential for adaptation of machine learning methods and novel biomarkers in clinical decision making, leading to increasingly systematic, informed diagnosis of PD.
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Affiliation(s)
- Jie Mei
- Chemosensory Neuroanatomy Lab, Department of Anatomy, Université du Québec à Trois-Rivières (UQTR), Trois-Rivières, QC, Canada
| | - Christian Desrosiers
- Laboratoire d'Imagerie, de Vision et d'Intelligence Artificielle (LIVIA), Department of Software and IT Engineering, École de Technologie Supérieure, Montreal, QC, Canada
| | - Johannes Frasnelli
- Chemosensory Neuroanatomy Lab, Department of Anatomy, Université du Québec à Trois-Rivières (UQTR), Trois-Rivières, QC, Canada
- Centre de Recherche de l'Hôpital du Sacré-Coeur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord-de-l'Île-de-Montréal (CIUSSS du Nord-de-l'Île-de-Montréal), Montreal, QC, Canada
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45
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Lake J, Storm CS, Makarious MB, Bandres-Ciga S. Genetic and Transcriptomic Biomarkers in Neurodegenerative Diseases: Current Situation and the Road Ahead. Cells 2021; 10:1030. [PMID: 33925602 PMCID: PMC8170880 DOI: 10.3390/cells10051030] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 04/21/2021] [Accepted: 04/24/2021] [Indexed: 12/19/2022] Open
Abstract
Neurodegenerative diseases are etiologically and clinically heterogeneous conditions, often reflecting a spectrum of disease rather than well-defined disorders. The underlying molecular complexity of these diseases has made the discovery and validation of useful biomarkers challenging. The search of characteristic genetic and transcriptomic indicators for preclinical disease diagnosis, prognosis, or subtyping is an area of ongoing effort and interest. The next generation of biomarker studies holds promise by implementing meaningful longitudinal and multi-modal approaches in large scale biobank and healthcare system scale datasets. This work will only be possible in an open science framework. This review summarizes the current state of genetic and transcriptomic biomarkers in Parkinson's disease, Alzheimer's disease, and amyotrophic lateral sclerosis, providing a comprehensive landscape of recent literature and future directions.
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Affiliation(s)
- Julie Lake
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA; (J.L.); (M.B.M.)
| | - Catherine S. Storm
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK;
- UCL Movement Disorders Centre, University College London, London WC1E 6BT, UK
| | - Mary B. Makarious
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA; (J.L.); (M.B.M.)
| | - Sara Bandres-Ciga
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA; (J.L.); (M.B.M.)
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46
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Mezlini AM, Das S, Goldenberg A. Finding associations in a heterogeneous setting: statistical test for aberration enrichment. Genome Med 2021; 13:68. [PMID: 33892787 PMCID: PMC8066476 DOI: 10.1186/s13073-021-00864-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 03/09/2021] [Indexed: 12/16/2022] Open
Abstract
Most two-group statistical tests find broad patterns such as overall shifts in mean, median, or variance. These tests may not have enough power to detect effects in a small subset of samples, e.g., a drug that works well only on a few patients. We developed a novel statistical test targeting such effects relevant for clinical trials, biomarker discovery, feature selection, etc. We focused on finding meaningful associations in complex genetic diseases in gene expression, miRNA expression, and DNA methylation. Our test outperforms traditional statistical tests in simulated and experimental data and detects potentially disease-relevant genes with heterogeneous effects.
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Affiliation(s)
- Aziz M. Mezlini
- Harvard Medical School, Boston, USA
- Department of Neurology, Massachusetts General Hospital, Boston, USA
- Department of Computer Science, University of Toronto, Toronto, Canada
- Genetics and genome biology, Hospital for sick children, Toronto, Canada
- The Vector Institute, Toronto, Canada
- Evidation Health, Inc., San Mateo, CA USA
| | - Sudeshna Das
- Harvard Medical School, Boston, USA
- Department of Neurology, Massachusetts General Hospital, Boston, USA
| | - Anna Goldenberg
- Department of Computer Science, University of Toronto, Toronto, Canada
- Genetics and genome biology, Hospital for sick children, Toronto, Canada
- The Vector Institute, Toronto, Canada
- CIFAR, Toronto, Canada
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Deep sequencing of sncRNAs reveals hallmarks and regulatory modules of the transcriptome during Parkinson’s disease progression. ACTA ACUST UNITED AC 2021; 1:309-322. [PMID: 37118411 DOI: 10.1038/s43587-021-00042-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 02/08/2021] [Indexed: 12/14/2022]
Abstract
Noncoding RNAs have diagnostic and prognostic importance in Parkinson's disease (PD). We studied circulating small noncoding RNAs (sncRNAs) in two large-scale longitudinal PD cohorts (Parkinson's Progression Markers Initiative (PPMI) and Luxembourg Parkinson's Study (NCER-PD)) and modeled their impact on the transcriptome. Sequencing of sncRNAs in 5,450 blood samples of 1,614 individuals in PPMI yielded 323 billion reads, most of which mapped to microRNAs but covered also other RNA classes such as piwi-interacting RNAs, ribosomal RNAs and small nucleolar RNAs. Dysregulated microRNAs associated with disease and disease progression occur in two distinct waves in the third and seventh decade of life. Originating predominantly from immune cells, they resemble a systemic inflammation response and mitochondrial dysfunction, two hallmarks of PD. Profiling 1,553 samples from 1,024 individuals in the NCER-PD cohort validated biomarkers and main findings by an independent technology. Finally, network analysis of sncRNA and transcriptome sequencing from PPMI identified regulatory modules emerging in patients with progressing PD.
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48
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Liu X, Wang Q, Yang Y, Stewart T, Shi M, Soltys D, Liu G, Thorland E, Cilento EM, Hou Y, Liu Z, Feng T, Zhang J. Reduced erythrocytic CHCHD2 mRNA is associated with brain pathology of Parkinson's disease. Acta Neuropathol Commun 2021; 9:37. [PMID: 33685516 PMCID: PMC7941904 DOI: 10.1186/s40478-021-01133-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 02/21/2021] [Indexed: 11/24/2022] Open
Abstract
Peripheral biomarkers indicative of brain pathology are critically needed for early detection of Parkinson’s disease (PD). In this study, using NanoString and digital PCR technologies, we began by screening for alterations in genes associated with PD or atypical Parkinsonism in erythrocytes of PD patients, in which PD-related changes have been reported, and which contain ~ 99% of blood α-synuclein. Erythrocytic CHCHD2 mRNA was significantly reduced even at the early stages of the disease. A significant reduction in protein and/or mRNA expression of CHCHD2 was confirmed in PD brains collected at autopsy as well as in the brains of a PD animal model overexpressing α-synuclein, in addition to seeing a reduction of CHCHD2 in erythrocytes of the same animals. Overexpression of α-synuclein in cellular models of PD also resulted in reduced CHCHD2, via mechanisms likely involving altered subcellular localization of p300 histone acetyltransferase. Finally, the utility of reduced CHCHD2 mRNA as a biomarker for detecting PD, including early-stage PD, was validated in a larger cohort of 205 PD patients and 135 normal controls, with a receiver operating characteristic analysis demonstrating > 80% sensitivity and specificity.
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49
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Gene expression barcode values reveal a potential link between Parkinson's disease and gastric cancer. Aging (Albany NY) 2021; 13:6171-6181. [PMID: 33596182 PMCID: PMC7950232 DOI: 10.18632/aging.202623] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 01/22/2021] [Indexed: 12/11/2022]
Abstract
Gastric cancer is a disease that develops from the lining of the stomach, whereas Parkinson’s disease is a long-term degenerative disorder of the central nervous system that mainly affects the motor system. Although these two diseases seem to be distinct from each other, increasing evidence suggests that they might be linked. To explore the linkage between these two diseases, differentially expressed genes between the diseased people and their normal controls were identified using the barcode algorithm. This algorithm transforms actual gene expression values into barcode values comprised of 1’s (expressed genes) and 0’s (silenced genes). Once the overlapped differentially expressed genes were identified, their biological relevance was investigated. Thus, using the gene expression profiles and bioinformatics methods, we demonstrate that Parkinson’s disease and gastric cancer are indeed linked. This research may serve as a pilot study, and it will stimulate more research to investigate the relationship between gastric cancer and Parkinson’s disease from the perspective of gene profiles and their functions.
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50
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Han Y, Huang L, Zhou F. A dynamic recursive feature elimination framework (dRFE) to further refine a set of OMIC biomarkers. Bioinformatics 2021; 37:2183-2189. [PMID: 33515240 DOI: 10.1093/bioinformatics/btab055] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 12/23/2020] [Accepted: 01/25/2021] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION A feature selection algorithm may select the subset of features with the best associations with the class labels. The recursive feature elimination (RFE) is a heuristic feature screening framework and has been widely used to select the biological OMIC biomarkers. This study proposed a dynamic recursive feature elimination (dRFE) framework with more flexible feature elimination operations. The proposed dRFE was comprehensively compared with 11 existing feature selection algorithms and five classifiers on the eight difficult transcriptome datasets from a previous study, the ten newly collected transcriptome datasets and the five methylome datasets. RESULTS The experimental data suggested that the regular RFE framework did not perform well, and dRFE outperformed the existing feature selection algorithms in most cases. The dRFE-detected features achieved Acc=1.0000 for the two methylome datasets GSE53045 and GSE66695. The best prediction accuracies of the dRFE-detected features were 0.9259, 0.9424, and 0.8601 for the other three methylome datasets GSE74845, GSE103186, and GSE80970, respectively. Four transcriptome datasets received Acc=1.0000 using the dRFE-detected features, and the prediction accuracies for the other six newly collected transcriptome datasets were between 0.6301 and 0.9917. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yuanyuan Han
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China, 130012
| | - Lan Huang
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China, 130012
| | - Fengfeng Zhou
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China, 130012
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