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Li S, Liu S, Sun X, Hao L, Gao Q. Identification of endocrine-disrupting chemicals targeting key DCM-associated genes via bioinformatics and machine learning. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 274:116168. [PMID: 38460409 DOI: 10.1016/j.ecoenv.2024.116168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 02/04/2024] [Accepted: 02/27/2024] [Indexed: 03/11/2024]
Abstract
Dilated cardiomyopathy (DCM) is a primary cause of heart failure (HF), with the incidence of HF increasing consistently in recent years. DCM pathogenesis involves a combination of inherited predisposition and environmental factors. Endocrine-disrupting chemicals (EDCs) are exogenous chemicals that interfere with endogenous hormone action and are capable of targeting various organs, including the heart. However, the impact of these disruptors on heart disease through their effects on genes remains underexplored. In this study, we aimed to explore key DCM-related genes using machine learning (ML) and the construction of a predictive model. Using the Gene Expression Omnibus (GEO) database, we screened differentially expressed genes (DEGs) and performed enrichment analyses of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to DCM. Through ML techniques combining maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) logistic regression, we identified key genes for predicting DCM (IL1RL1, SEZ6L, SFRP4, COL22A1, RNASE2, HB). Based on these key genes, 79 EDCs with the potential to affect DCM were identified, among which 4 (3,4-dichloroaniline, fenitrothion, pyrene, and isoproturon) have not been previously associated with DCM. These findings establish a novel relationship between the EDCs mediated by key genes and the development of DCM.
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Affiliation(s)
- Shu Li
- Department of Health and Intelligent Engineering, College of Health Management, China Medical University, Shenyang, Liaoning Province 110122, PR China..
| | - Shuice Liu
- Department of Pharmacology, Shenyang Medical College, Shenyang, Liaoning Province 110001, PR China..
| | - Xuefei Sun
- Department of Pharmaceutical Toxicology, School of Pharmacy, China Medical University, Shenyang 110122, PR China..
| | - Liying Hao
- Department of Pharmaceutical Toxicology, School of Pharmacy, China Medical University, Shenyang 110122, PR China..
| | - Qinghua Gao
- Department of Developmental Cell Biology, Key Laboratory of Cell Biology, Ministry of Public Health, and Key Laboratory of Medical Cell Biology, Ministry of Education, China Medical University, No. 77 Puhe Road, Shenyang North New Area, Shenyang, Liaoning Province, PR China..
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Xin G, Niu J, Tian Q, Fu Y, Chen L, Yi T, Tian K, Sun X, Wang N, Wang J, Zhang H, Wang L. Identification of potential immune-related hub genes in Parkinson's disease based on machine learning and development and validation of a diagnostic classification model. PLoS One 2023; 18:e0294984. [PMID: 38051734 DOI: 10.1371/journal.pone.0294984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 11/14/2023] [Indexed: 12/07/2023] Open
Abstract
BACKGROUND Parkinson's disease is the second most common neurodegenerative disease in the world. However, current diagnostic methods are still limited, and available treatments can only mitigate the symptoms of the disease, not reverse it at the root. The immune function has been identified as playing a role in PD, but the exact mechanism is unknown. This study aimed to search for potential immune-related hub genes in Parkinson's disease, find relevant immune infiltration patterns, and develop a categorical diagnostic model. METHODS We downloaded the GSE8397 dataset from the GEO database, which contains gene expression microarray data for 15 healthy human SN samples and 24 PD patient SN samples. Screening for PD-related DEGs using WGCNA and differential expression analysis. These PD-related DEGs were analyzed for GO and KEGG enrichment. Subsequently, hub genes (dld, dlk1, iars and ttd19) were screened by LASSO and mSVM-RFE machine learning algorithms. We used the ssGSEA algorithm to calculate and evaluate the differences in nigrostriatal immune cell types in the GSE8397 dataset. The association between dld, dlk1, iars and ttc19 and 28 immune cells was investigated. Using the GSEA and GSVA algorithms, we analyzed the biological functions associated with immune-related hub genes. Establishment of a ceRNA regulatory network for immune-related hub genes. Finally, a logistic regression model was used to develop a PD classification diagnostic model, and the accuracy of the model was verified in three independent data sets. The three independent datasets are GES49036 (containing 8 healthy human nigrostriatal tissue samples and 15 PD patient nigrostriatal tissue samples), GSE20292 (containing 18 healthy human nigrostriatal tissue samples and 11 PD patient nigrostriatal tissue samples) and GSE7621 (containing 9 healthy human nigrostriatal tissue samples and 16 PD patient nigrostriatal tissue samples). RESULTS Ultimately, we screened for four immune-related Parkinson's disease hub genes. Among them, the AUC values of dlk1, dld and ttc19 in GSE8397 and three other independent external datasets were all greater than 0.7, indicating that these three genes have a certain level of accuracy. The iars gene had an AUC value greater than 0.7 in GES8397 and one independent external data while the AUC values in the other two independent external data sets ranged between 0.5 and 0.7. These results suggest that iars also has some research value. We successfully constructed a categorical diagnostic model based on these four immune-related Parkinson's disease hub genes, and the AUC values of the joint diagnostic model were greater than 0.9 in both GSE8397 and three independent external datasets. These results indicate that the categorical diagnostic model has a good ability to distinguish between healthy individuals and Parkinson's disease patients. In addition, ceRNA networks reveal complex regulatory relationships based on immune-related hub genes. CONCLUSION In this study, four immune-related PD hub genes (dld, dlk1, iars and ttd19) were obtained. A reliable diagnostic model for PD classification was developed. This study provides algorithmic-level support to explore the immune-related mechanisms of PD and the prediction of immune-related drug targets.
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Affiliation(s)
- Guanghao Xin
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, City Harbin, Province Heilongjiang, China
| | - Jingyan Niu
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, City Harbin, Province Heilongjiang, China
| | - Qinghua Tian
- Department of Neurology, The 962 Hospital of the Chinese People's Liberation Army Joint Logistic Support Force, City Harbin, Province Heilongjiang, China
| | - Yanchi Fu
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, City Harbin, Province Heilongjiang, China
| | - Lixia Chen
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, City Harbin, Province Heilongjiang, China
| | - Tingting Yi
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, City Harbin, Province Heilongjiang, China
| | - Kuo Tian
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, City Harbin, Province Heilongjiang, China
| | - Xuesong Sun
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, City Harbin, Province Heilongjiang, China
| | - Na Wang
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, City Harbin, Province Heilongjiang, China
| | - Jianjian Wang
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, City Harbin, Province Heilongjiang, China
| | - Huixue Zhang
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, City Harbin, Province Heilongjiang, China
| | - Lihua Wang
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, City Harbin, Province Heilongjiang, China
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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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4
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Fedoseyeva VB, Novosadova EV, Nenasheva VV, Novosadova LV, Grivennikov IA, Tarantul VZ. Activation of Embryonic Gene Transcription in Neural Precursor Cells Derived from the Induced Pluripotent Stem Cells of the Patients with Parkinson's Disease. BIOCHEMISTRY. BIOKHIMIIA 2023; 88:515-525. [PMID: 37080937 DOI: 10.1134/s0006297923040077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/22/2023]
Abstract
Parkinson's disease (PD) is one of the most common neurodegenerative diseases in the world. Despite numerous studies, the causes of this pathology remain completely unknown. This is, among other things, due to the difficulty of obtaining biological material for analysis. Neural cell cultures derived from the induced pluripotent stem cells (IPSCs) provide a great potential for studying molecular events underlying the pathogenesis of PD. This paper presents the results of bioinformatic analysis of the data obtained using RNA-seq technology in the study of neural precursors (NP) derived from IPSCs of the healthy donors and patients with PD carrying various mutations that are commonly associated with familial PD. This analysis showed that the level of transcription of multiple genes actively expressed in the nervous system at the embryonic stage of development was significantly increased in the NP cells obtained from the patients with PD, unlike in the case of healthy donors. Bioinformatic data have been, in general, confirmed using real-time PCR. The obtained data suggest that one of the causes of PD may be the shift of the gene expression pattern in neuronal cells towards embryonic gene expression pattern (termed dematuration).
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Affiliation(s)
- Viya B Fedoseyeva
- National Research Center "Kurchatov Institute", Moscow, 123182, Russia.
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5
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PCDH8 protects MPP+-induced neuronal injury in SH-SY5Y cells by inhibiting MAPK pathway. Mol Cell Toxicol 2022. [DOI: 10.1007/s13273-022-00257-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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6
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Novak G, Kyriakis D, Grzyb K, Bernini M, Rodius S, Dittmar G, Finkbeiner S, Skupin A. Single-cell transcriptomics of human iPSC differentiation dynamics reveal a core molecular network of Parkinson's disease. Commun Biol 2022; 5:49. [PMID: 35027645 PMCID: PMC8758783 DOI: 10.1038/s42003-021-02973-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 12/14/2021] [Indexed: 01/02/2023] Open
Abstract
Parkinson's disease (PD) is the second-most prevalent neurodegenerative disorder, characterized by the loss of dopaminergic neurons (mDA) in the midbrain. The underlying mechanisms are only partly understood and there is no treatment to reverse PD progression. Here, we investigated the disease mechanism using mDA neurons differentiated from human induced pluripotent stem cells (hiPSCs) carrying the ILE368ASN mutation within the PINK1 gene, which is strongly associated with PD. Single-cell RNA sequencing (RNAseq) and gene expression analysis of a PINK1-ILE368ASN and a control cell line identified genes differentially expressed during mDA neuron differentiation. Network analysis revealed that these genes form a core network, members of which interact with all known 19 protein-coding Parkinson's disease-associated genes. This core network encompasses key PD-associated pathways, including ubiquitination, mitochondrial function, protein processing, RNA metabolism, and vesicular transport. Proteomics analysis showed a consistent alteration in proteins of dopamine metabolism, indicating a defect of dopaminergic metabolism in PINK1-ILE368ASN neurons. Our findings suggest the existence of a network onto which pathways associated with PD pathology converge, and offers an inclusive interpretation of the phenotypic heterogeneity of PD.
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Affiliation(s)
- Gabriela Novak
- The Integrative Cell Signalling Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg.
- Luxembourg Institute of Health (LIH), Esch-sur-Alzette, Luxembourg.
- Center for Systems and Therapeutics, the Gladstone Institutes and Departments of Neurology and Physiology, University of California, San Francisco, San Francisco, CA, 94158, USA.
| | - Dimitrios Kyriakis
- The Integrative Cell Signalling Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Kamil Grzyb
- The Integrative Cell Signalling Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Michela Bernini
- The Integrative Cell Signalling Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Sophie Rodius
- Department of Infection and Immunity, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Gunnar Dittmar
- Department of Infection and Immunity, Luxembourg Institute of Health, Strassen, Luxembourg
- Department of Life Sciences and Medicine, University of Luxembourg, Belvaux, Luxembourg
| | - Steven Finkbeiner
- Center for Systems and Therapeutics, the Gladstone Institutes and Departments of Neurology and Physiology, University of California, San Francisco, San Francisco, CA, 94158, USA
| | - Alexander Skupin
- The Integrative Cell Signalling Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg.
- University of California San Diego, La Jolla, CA, 92093, USA.
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7
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Yang W, Hao W, Meng Z, Ding S, Li X, Zhang T, Huang W, Xu L, Zhang Y, Yang J, Gu X. Molecular Regulatory Mechanism and Toxicology of Neurodegenerative Processes in MPTP/Probenecid-Induced Progressive Parkinson's Disease Mice Model Revealed by Transcriptome. Mol Neurobiol 2021; 58:603-616. [PMID: 32997292 PMCID: PMC7843579 DOI: 10.1007/s12035-020-02128-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 09/08/2020] [Indexed: 12/13/2022]
Abstract
Parkinson's disease (PD) is a neurodegenerative disease caused by a variety of unclear complex pathogenic factors. The 1-methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine/probenecid (MPTP/p)-induced progressive PD mice is a well-recognized classic model for studying PD, but the molecular toxicology of this model is still unclear. Here, for the first time, we report gradual neurodegenerative processes in MPTP/p-induced progressive PD mice model using RNA-seq. Transcriptional responses are orchestrated to regulate the expression of many genes in substantia nigra, such as Ntf3, Pitx3, Th, and Drd2, leading to the degeneration of dopaminergic neurons at last. We proposed that the established model could be divided into three phases based on their molecular toxicological features: "the stress response phase" which maintained the microenvironment homeostasis, "the pre-neurodegenerative phase" which demonstrated observed MPTP/p cytotoxicity and gradual degeneration of dopaminergic neurons, and "the neurodegenerative phase" which reflected distinct damage and dopaminergic neuron apoptotic process. Glia cells exhibited a certain protective effect on dopaminergic neurons in 3rd and 6th MPTP/p-induced cytotoxicity. But in 10th MPTP/p injection, glia cells play a promoting role in PD and tissue damages caused by oxidative stress. This study also indicated that the substantia nigra of PD mice showed unique patterns of changes at each stage. Moreover, neurotrophic signaling pathway, ECM-receptor interaction, oxidative phosphorylation, apoptosis and necroptosis were enriched at 3rd and 6th MPTP/p injection, which might be associated with the PD progress. This study provided an extensive data set of molecular toxicology for elucidating of PD progression and offered comprehensive theoretical knowledge for the development of new therapy.
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Affiliation(s)
- Weiwei Yang
- Key Laboratory of Acupuncture and Medicine Research of Ministry of Education, School of Medicine & Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Wenwen Hao
- Key Laboratory of Acupuncture and Medicine Research of Ministry of Education, School of Medicine & Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Zhuo Meng
- Key Laboratory of Acupuncture and Medicine Research of Ministry of Education, School of Medicine & Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Shiyan Ding
- Key Laboratory of Acupuncture and Medicine Research of Ministry of Education, School of Medicine & Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Xiaodi Li
- Key Laboratory of Acupuncture and Medicine Research of Ministry of Education, School of Medicine & Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Tao Zhang
- Key Laboratory of Acupuncture and Medicine Research of Ministry of Education, School of Medicine & Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Weixiao Huang
- Key Laboratory of Acupuncture and Medicine Research of Ministry of Education, School of Medicine & Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Lian Xu
- Jiangsu Clinical Medicine Center of Tissue Engineering and Nerve Injury Repair, Co-innovation Center of Neuroregeneration, Nantong University, Nantong, China
| | - Yu Zhang
- Key Laboratory of Acupuncture and Medicine Research of Ministry of Education, School of Medicine & Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Jian Yang
- Jiangsu Clinical Medicine Center of Tissue Engineering and Nerve Injury Repair, Co-innovation Center of Neuroregeneration, Nantong University, Nantong, China.
| | - Xiaosong Gu
- Key Laboratory of Acupuncture and Medicine Research of Ministry of Education, School of Medicine & Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing, China.
- Jiangsu Clinical Medicine Center of Tissue Engineering and Nerve Injury Repair, Co-innovation Center of Neuroregeneration, Nantong University, Nantong, China.
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8
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Phung DM, Lee J, Hong S, Kim YE, Yoon J, Kim YJ. Meta-Analysis of Differentially Expressed Genes in the Substantia Nigra in Parkinson's Disease Supports Phenotype-Specific Transcriptome Changes. Front Neurosci 2020; 14:596105. [PMID: 33390883 PMCID: PMC7775392 DOI: 10.3389/fnins.2020.596105] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 11/16/2020] [Indexed: 01/26/2023] Open
Abstract
Background Studies regarding differentially expressed genes (DEGs) in Parkinson’s disease (PD) have focused on common upstream regulators or dysregulated pathways or ontologies; however, the relationships between DEGs and disease-related or cell type-enriched genes have not been systematically studied. Meta-analysis of DEGs (meta-DEGs) are expected to overcome the limitations, such as replication failure and small sample size of previous studies. Purpose Meta-DEGs were performed to investigate dysregulated genes enriched with neurodegenerative disorder causative or risk genes in a phenotype-specific manner. Methods Six microarray datasets from PD patients and controls, for which substantia nigra sample transcriptome data were available, were downloaded from the NINDS data repository. Meta-DEGs were performed using two methods, combining p-values and combing effect size, and common DEGs were used for secondary analyses. Gene sets of cell type-enriched or disease-related genes for PD, Alzheimer’s disease (AD), and hereditary progressive ataxia were constructed by curation of public databases and/or published literatures. Results Our meta-analyses revealed 449 downregulated and 137 upregulated genes. Overrepresentation analyses with cell type-enriched genes were significant in neuron-enriched genes but not in astrocyte- or microglia-enriched genes. Meta-DEGs were significantly enriched in causative genes for hereditary disorders accompanying parkinsonism but not in genes associated with AD or hereditary progressive ataxia. Enrichment of PD-related genes was highly significant in downregulated DEGs but insignificant in upregulated genes. Conclusion Downregulated meta-DEGs were associated with PD-related genes, but not with other neurodegenerative disorder genes. These results highlight disease phenotype-specific changes in dysregulated genes in PD.
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Affiliation(s)
- Duong My Phung
- Department of Biomedical Gerontology, Ilsong Institute of Life and Science, Hallym University, Anyang, South Korea
| | - Jinwoo Lee
- Department of Computer Engineering, Hallym University, Chuncheon, South Korea
| | - SangKyoon Hong
- Hallym Institute of Translational Genomics and Bioinformatics, Anyang, South Korea
| | - Young Eun Kim
- Laboratory of Parkinson's Disease and Neurogenetics, Department of Neurology, Hallym University, Anyang, South Korea
| | - Jeehee Yoon
- Department of Computer Engineering, Hallym University, Chuncheon, South Korea
| | - Yun Joong Kim
- Department of Neurology, Yonsei University College of Medicine, Yongin, South Korea.,Department of Neurology, Yongin Severance Hospital, Yonsei University Health System, Yongin, South Korea
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Monaco A, Pantaleo E, Amoroso N, Bellantuono L, Lombardi A, Tateo A, Tangaro S, Bellotti R. Identifying potential gene biomarkers for Parkinson's disease through an information entropy based approach. Phys Biol 2020; 18:016003. [PMID: 33049726 DOI: 10.1088/1478-3975/abc09a] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Parkinson's disease (PD) is a chronic, progressive neurodegenerative disease and represents the most common disease of this type, after Alzheimer's dementia. It is characterized by motor and nonmotor features and by a long prodromal stage that lasts many years. Genetic research has shown that PD is a complex and multisystem disorder. To capture the molecular complexity of this disease we used a complex network approach. We maximized the information entropy of the gene co-expression matrix betweenness to obtain a gene adjacency matrix; then we used a fast greedy algorithm to detect communities. Finally we applied principal component analysis on the detected gene communities, with the ultimate purpose of discriminating between PD patients and healthy controls by means of a random forests classifier. We used a publicly available substantia nigra microarray dataset, GSE20163, from NCBI GEO database, containing gene expression profiles for 10 PD patients and 18 normal controls. With this methodology we identified two gene communities that discriminated between the two groups with mean accuracy of 0.88 ± 0.03 and 0.84 ± 0.03, respectively, and validated our results on an independent microarray experiment. The two gene communities presented a considerable reduction in size, over 100 times, compared to the initial network and were stable within a range of tested parameters. Further research focusing on the restricted number of genes belonging to the selected communities may reveal essential mechanisms responsible for PD at a network level and could contribute to the discovery of new biomarkers for PD.
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Affiliation(s)
- A Monaco
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Bari, Italy
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Parkinson's Disease Master Regulators on Substantia Nigra and Frontal Cortex and Their Use for Drug Repositioning. Mol Neurobiol 2020; 58:1517-1534. [PMID: 33211252 DOI: 10.1007/s12035-020-02203-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 11/03/2020] [Indexed: 12/14/2022]
Abstract
Parkinson's disease (PD) is among the most prevalent neurodegenerative diseases. Available evidences support the view of PD as a complex disease, being the outcome of interactions between genetic and environmental factors. In face of diagnosis and therapy challenges, and the elusive PD etiology, the use of alternative methodological approaches for the elucidation of the disease pathophysiological mechanisms and proposal of novel potential therapeutic interventions has become increasingly necessary. In the present study, we first reconstructed the transcriptional regulatory networks (TN), centered on transcription factors (TF), of two brain regions affected in PD, the substantia nigra pars compacta (SNc) and the frontal cortex (FCtx). Then, we used case-control studies data from these regions to identify TFs working as master regulators (MR) of the disease, based on region-specific TNs. Twenty-nine regulatory units enriched with differentially expressed genes were identified for the SNc, and twenty for the FCtx, all of which were considered MR candidates for PD. Three consensus MR candidates were found for SNc and FCtx, namely ATF2, SLC30A9, and ZFP69B. In order to search for novel potential therapeutic interventions, we used these consensus MR candidate signatures as input to the Connectivity Map (CMap), a computational drug repositioning webtool. This analysis resulted in the identification of four drugs that reverse the expression pattern of all three MR consensus simultaneously, benperidol, harmaline, tubocurarine chloride, and vorinostat, thus suggested as novel potential PD therapeutic interventions.
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11
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Turkez H, Nóbrega FRD, Ozdemir O, Bezerra Filho CDSM, Almeida RND, Tejera E, Perez-Castillo Y, Sousa DPD. NFBTA: A Potent Cytotoxic Agent against Glioblastoma. Molecules 2019; 24:E2411. [PMID: 31261921 PMCID: PMC6651752 DOI: 10.3390/molecules24132411] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Revised: 05/28/2019] [Accepted: 05/29/2019] [Indexed: 12/15/2022] Open
Abstract
Piplartine (PPL), also known as piperlongumine, is a biologically active alkaloid extracted from the Piper genus which has been found to have highly effective anticancer activity against several tumor cell lines. This study investigates in detail the antitumoral potential of a PPL analogue; (E)-N-(4-fluorobenzyl)-3-(3,4,5-trimethoxyphenyl) acrylamide (NFBTA). The anticancer potential of NFBTA on the glioblastoma multiforme (GBM) cell line (U87MG) was determined by 3-(4,5-dimethyl-2-thia-zolyl)-2, 5-diphenyl-2H-tetrazolium bromide (MTT), and lactate dehydrogenase (LDH) release analysis, and the selectivity index (SI) was calculated. To detect cell apoptosis, fluorescent staining via flow cytometry and Hoechst 33258 staining were performed. Oxidative alterations were assessed via colorimetric measurement methods. Alterations in expressions of key genes related to carcinogenesis were determined. Additionally, in terms of NFBTA cytotoxic, oxidative, and genotoxic damage potential, the biosafety of this novel agent was evaluated in cultured human whole blood cells. Cell viability analyses revealed that NFBTA exhibited strong cytotoxic activity in cultured U87MG cells, with high selectivity and inhibitory activity in apoptotic processes, as well as potential for altering the principal molecular genetic responses in U87MG cell growth. Molecular docking studies strongly suggested a plausible anti-proliferative mechanism for NBFTA. The results of the experimental in vitro human glioblastoma model and computational approach revealed promising cytotoxic activity for NFBTA, helping to orient further studies evaluating its antitumor profile for safe and effective therapeutic applications.
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Affiliation(s)
- Hasan Turkez
- Department of Molecular Biology and Genetics, Erzurum Technical University, Erzurum 25240, Turkey
- Department of Pharmacy, "G. d'Annunzio" University of Chieti-Pescara, Via dei Vestini 31, 66013 Chieti Scalo, Italy
| | - Flávio Rogério da Nóbrega
- Department of Pharmaceutical Sciences, Federal University of Paraíba, João Pessoa, PB 58051-085, Brazil
| | - Ozlem Ozdemir
- Department of Molecular Biology and Genetics, Erzurum Technical University, Erzurum 25240, Turkey
| | | | | | - Eduardo Tejera
- Escuela de Ciencias Físicas y Matemáticas, Universidad de Las Américas, Quito 170125, Ecuador
| | | | - Damião Pergentino de Sousa
- Department of Pharmaceutical Sciences, Federal University of Paraíba, João Pessoa, PB 58051-085, Brazil.
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12
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Kelly J, Moyeed R, Carroll C, Albani D, Li X. Gene expression meta-analysis of Parkinson's disease and its relationship with Alzheimer's disease. Mol Brain 2019; 12:16. [PMID: 30819229 PMCID: PMC6396547 DOI: 10.1186/s13041-019-0436-5] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2018] [Accepted: 02/15/2019] [Indexed: 12/16/2022] Open
Abstract
Parkinson’s disease (PD) and Alzheimer’s disease (AD) are the most common neurodegenerative diseases and have been suggested to share common pathological and physiological links. Understanding the cross-talk between them could reveal potentials for the development of new strategies for early diagnosis and therapeutic intervention thus improving the quality of life of those affected. Here we have conducted a novel meta-analysis to identify differentially expressed genes (DEGs) in PD microarray datasets comprising 69 PD and 57 control brain samples which is the biggest cohort for such studies to date. Using identified DEGs, we performed pathway, upstream and protein-protein interaction analysis. We identified 1046 DEGs, of which a majority (739/1046) were downregulated in PD. YWHAZ and other genes coding 14–3-3 proteins are identified as important DEGs in signaling pathways and in protein-protein interaction networks (PPIN). Perturbed pathways also include mitochondrial dysfunction and oxidative stress. There was a significant overlap in DEGs between PD and AD, and over 99% of these were differentially expressed in the same up or down direction across the diseases. REST was identified as an upstream regulator in both diseases. Our study demonstrates that PD and AD share significant common DEGs and pathways, and identifies novel genes, pathways and upstream regulators which may be important targets for therapy in both diseases.
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Affiliation(s)
- Jack Kelly
- Faculty of Medicine and Dentistry, Plymouth University, Plymouth, PL6 8BU, UK
| | - Rana Moyeed
- Faculty of Science and Engineering, Plymouth University, Plymouth, PL6 8BU, UK
| | - Camille Carroll
- Faculty of Medicine and Dentistry, Plymouth University, Plymouth, PL6 8BU, UK
| | - Diego Albani
- Department of Neuroscience, IRCCS - Istituto di Ricerche Farmacologiche "Mario Negri" Via La Masa 19, 20156, Milan, Italy
| | - Xinzhong Li
- Faculty of Medicine and Dentistry, Plymouth University, Plymouth, PL6 8BU, UK. .,School of Science, Engineering & Design, Teesside University, Middlesbrough, TS1 3BX, UK.
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13
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Ramos V, Franco-Crespo A, González-Pérez L, Guerra Y, Ramos-Galarza C, Pazmiño P, Tejera E. Analysis of organizational power networks through a holistic approach using consensus strategies. Heliyon 2019; 5:e01172. [PMID: 30793053 PMCID: PMC6370583 DOI: 10.1016/j.heliyon.2019.e01172] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Revised: 12/25/2018] [Accepted: 01/23/2019] [Indexed: 12/30/2022] Open
Abstract
Power is one of the most complex organizational attributes to understand due to the multiple related variables and dimensions in which it appears. The ownership and use and of power are reflected in the interpersonal relationships within an organization, as a result, modeling its structure and interactions can lead to knowledge about the power networks that shape it. The objective of this study was to identify the behavior of organizational networks based on existing sources of power, using a consensual analysis of the different topologies present in these networks. The study was carried out in a private production company in Ecuador, which has representation at a domestic level. To this end, a 12-question personalized questionnaire was designed with the aim of identifying specific networks and was applied to 1190 workers in the company. The results were obtained using organizational network analysis and a consensus strategy to integrate the centralities found in multiple networks into one. This study can serve as a reference to organizations, so they can know the relationships between people within it, as part of their management process. In this way, the identification of people within power networks is useful for knowing the “key” actors in the promotion of organizational changes, as well as for the development of career plans based on the position that people occupy in the organizational system.
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Affiliation(s)
- Valentina Ramos
- Sistemas de Información, Gestión de la Tecnología e Innovación (SIGTI-Research Group), Escuela Politécnica Nacional, Ecuador
| | - Antonio Franco-Crespo
- Sistemas de Información, Gestión de la Tecnología e Innovación (SIGTI-Research Group), Escuela Politécnica Nacional, Ecuador
| | - Lien González-Pérez
- Sistemas de Información, Gestión de la Tecnología e Innovación (SIGTI-Research Group), Escuela Politécnica Nacional, Ecuador
| | - Yasel Guerra
- Sistemas de Información, Gestión de la Tecnología e Innovación (SIGTI-Research Group), Escuela Politécnica Nacional, Ecuador
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14
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López-Cortés A, Paz-Y-Miño C, Cabrera-Andrade A, Barigye SJ, Munteanu CR, González-Díaz H, Pazos A, Pérez-Castillo Y, Tejera E. Gene prioritization, communality analysis, networking and metabolic integrated pathway to better understand breast cancer pathogenesis. Sci Rep 2018; 8:16679. [PMID: 30420728 PMCID: PMC6232116 DOI: 10.1038/s41598-018-35149-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Accepted: 10/16/2018] [Indexed: 12/30/2022] Open
Abstract
Consensus strategy was proved to be highly efficient in the recognition of gene-disease association. Therefore, the main objective of this study was to apply theoretical approaches to explore genes and communities directly involved in breast cancer (BC) pathogenesis. We evaluated the consensus between 8 prioritization strategies for the early recognition of pathogenic genes. A communality analysis in the protein-protein interaction (PPi) network of previously selected genes was enriched with gene ontology, metabolic pathways, as well as oncogenomics validation with the OncoPPi and DRIVE projects. The consensus genes were rationally filtered to 1842 genes. The communality analysis showed an enrichment of 14 communities specially connected with ERBB, PI3K-AKT, mTOR, FOXO, p53, HIF-1, VEGF, MAPK and prolactin signaling pathways. Genes with highest ranking were TP53, ESR1, BRCA2, BRCA1 and ERBB2. Genes with highest connectivity degree were TP53, AKT1, SRC, CREBBP and EP300. The connectivity degree allowed to establish a significant correlation between the OncoPPi network and our BC integrated network conformed by 51 genes and 62 PPi. In addition, CCND1, RAD51, CDC42, YAP1 and RPA1 were functional genes with significant sensitivity score in BC cell lines. In conclusion, the consensus strategy identifies both well-known pathogenic genes and prioritized genes that need to be further explored.
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Affiliation(s)
- Andrés López-Cortés
- Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Mariscal Sucre Avenue, 170129, Quito, Ecuador.
- RNASA-IMEDIR, Computer Sciences Faculty, University of Coruna, 15071, Coruna, Spain.
| | - César Paz-Y-Miño
- Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Mariscal Sucre Avenue, 170129, Quito, Ecuador
| | - Alejandro Cabrera-Andrade
- Carrera de Enfermería, Facultad de Ciencias de la Salud, Universidad de las Américas, Avenue de los Granados, 170125, Quito, Ecuador
- Grupo de Bio-Quimioinformática, Universidad de las Américas, Avenue de los Granados, 170125, Quito, Ecuador
| | - Stephen J Barigye
- Department of Chemistry, McGill University, 801 Sherbrooke Street West, Montreal, QC, H3A 0B8, Canada
| | - Cristian R Munteanu
- RNASA-IMEDIR, Computer Sciences Faculty, University of Coruna, 15071, Coruna, Spain
- INIBIC, Institute of Biomedical Research, CHUAC, UDC, 15006, Coruna, Spain
| | - Humberto González-Díaz
- Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940, Leioa, Biscay, Spain
- IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Biscay, Spain
| | - Alejandro Pazos
- RNASA-IMEDIR, Computer Sciences Faculty, University of Coruna, 15071, Coruna, Spain
- INIBIC, Institute of Biomedical Research, CHUAC, UDC, 15006, Coruna, Spain
| | - Yunierkis Pérez-Castillo
- Grupo de Bio-Quimioinformática, Universidad de las Américas, Avenue de los Granados, 170125, Quito, Ecuador
- Escuela de Ciencias Físicas y Matemáticas, Universidad de las Américas, Avenue de los Granados, 170125, Quito, Ecuador
| | - Eduardo Tejera
- Grupo de Bio-Quimioinformática, Universidad de las Américas, Avenue de los Granados, 170125, Quito, Ecuador.
- Facultad de Ingeniería y Ciencias Agropecuarias, Universidad de las Américas, Avenue de los Granados, 170125, Quito, Ecuador.
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15
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MGOGP: a gene module-based heuristic algorithm for cancer-related gene prioritization. BMC Bioinformatics 2018; 19:215. [PMID: 29871590 PMCID: PMC5989416 DOI: 10.1186/s12859-018-2216-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Accepted: 05/23/2018] [Indexed: 01/13/2023] Open
Abstract
Background Prioritizing genes according to their associations with a cancer allows researchers to explore genes in more informed ways. By far, Gene-centric or network-centric gene prioritization methods are predominated. Genes and their protein products carry out cellular processes in the context of functional modules. Dysfunctional gene modules have been previously reported to have associations with cancer. However, gene module information has seldom been considered in cancer-related gene prioritization. Results In this study, we propose a novel method, MGOGP (Module and Gene Ontology-based Gene Prioritization), for cancer-related gene prioritization. Different from other methods, MGOGP ranks genes considering information of both individual genes and their affiliated modules, and utilize Gene Ontology (GO) based fuzzy measure value as well as known cancer-related genes as heuristics. The performance of the proposed method is comprehensively validated by using both breast cancer and prostate cancer datasets, and by comparison with other methods. Results show that MGOGP outperforms other methods, and successfully prioritizes more genes with literature confirmed evidence. Conclusions This work will aid researchers in the understanding of the genetic architecture of complex diseases, and improve the accuracy of diagnosis and the effectiveness of therapy. Electronic supplementary material The online version of this article (10.1186/s12859-018-2216-0) contains supplementary material, which is available to authorized users.
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16
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Pietrosemoli N, Mella S, Yennek S, Baghdadi MB, Sakai H, Sambasivan R, Pala F, Di Girolamo D, Tajbakhsh S. Comparison of multiple transcriptomes exposes unified and divergent features of quiescent and activated skeletal muscle stem cells. Skelet Muscle 2017; 7:28. [PMID: 29273087 PMCID: PMC5741941 DOI: 10.1186/s13395-017-0144-8] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Accepted: 11/29/2017] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND Skeletal muscle satellite (stem) cells are quiescent in adult mice and can undergo multiple rounds of proliferation and self-renewal following muscle injury. Several labs have profiled transcripts of myogenic cells during the developmental and adult myogenesis with the aim of identifying quiescent markers. Here, we focused on the quiescent cell state and generated new transcriptome profiles that include subfractionations of adult satellite cell populations, and an artificially induced prenatal quiescent state, to identify core signatures for quiescent and proliferating. METHODS Comparison of available data offered challenges related to the inherent diversity of datasets and biological conditions. We developed a standardized workflow to homogenize the normalization, filtering, and quality control steps for the analysis of gene expression profiles allowing the identification up- and down-regulated genes and the subsequent gene set enrichment analysis. To share the analytical pipeline of this work, we developed Sherpa, an interactive Shiny server that allows multi-scale comparisons for extraction of desired gene sets from the analyzed datasets. This tool is adaptable to cell populations in other contexts and tissues. RESULTS A multi-scale analysis comprising eight datasets of quiescent satellite cells had 207 and 542 genes commonly up- and down-regulated, respectively. Shared up-regulated gene sets include an over-representation of the TNFα pathway via NFKβ signaling, Il6-Jak-Stat3 signaling, and the apical surface processes, while shared down-regulated gene sets exhibited an over-representation of Myc and E2F targets and genes associated to the G2M checkpoint and oxidative phosphorylation. However, virtually all datasets contained genes that are associated with activation or cell cycle entry, such as the immediate early stress response genes Fos and Jun. An empirical examination of fixed and isolated satellite cells showed that these and other genes were absent in vivo, but activated during procedural isolation of cells. CONCLUSIONS Through the systematic comparison and individual analysis of diverse transcriptomic profiles, we identified genes that were consistently differentially expressed among the different datasets and shared underlying biological processes key to the quiescent cell state. Our findings provide impetus to define and distinguish transcripts associated with true in vivo quiescence from those that are first responding genes due to disruption of the stem cell niche.
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Affiliation(s)
- Natalia Pietrosemoli
- Bioinformatics and Biostatistics Hub, C3BI, USR 3756 IP CNRS, Institut Pasteur, 75015 Paris, France
| | - Sébastien Mella
- Stem Cells and Development, Department of Developmental and Stem Cell Biology, Institut Pasteur, 75015 Paris, France
- CNRS UMR 3738, Institut Pasteur, 75015 Paris, France
| | - Siham Yennek
- Stem Cells and Development, Department of Developmental and Stem Cell Biology, Institut Pasteur, 75015 Paris, France
- CNRS UMR 3738, Institut Pasteur, 75015 Paris, France
- Novo Nordisk Foundation Center for Stem Cell Biology, DanStem, University of Copenhagen, 3B Blegdamsvej, DK-2200 Copenhagen N, Denmark
| | - Meryem B. Baghdadi
- Stem Cells and Development, Department of Developmental and Stem Cell Biology, Institut Pasteur, 75015 Paris, France
- CNRS UMR 3738, Institut Pasteur, 75015 Paris, France
| | - Hiroshi Sakai
- Stem Cells and Development, Department of Developmental and Stem Cell Biology, Institut Pasteur, 75015 Paris, France
- CNRS UMR 3738, Institut Pasteur, 75015 Paris, France
| | - Ramkumar Sambasivan
- Institute for Stem Cell Biology and Regenerative Medicine, GKVK PO, Bellary Road, Bengaluru, 560065 India
| | - Francesca Pala
- Stem Cells and Development, Department of Developmental and Stem Cell Biology, Institut Pasteur, 75015 Paris, France
- CNRS UMR 3738, Institut Pasteur, 75015 Paris, France
| | - Daniela Di Girolamo
- Stem Cells and Development, Department of Developmental and Stem Cell Biology, Institut Pasteur, 75015 Paris, France
- Dipartimento di Medicina Clinica e Chirurgia, Università degli Studi di Napoli Federico II, Via S. Pansini 5, 80131 Naples, Italy
| | - Shahragim Tajbakhsh
- Stem Cells and Development, Department of Developmental and Stem Cell Biology, Institut Pasteur, 75015 Paris, France
- CNRS UMR 3738, Institut Pasteur, 75015 Paris, France
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17
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Borrageiro G, Haylett W, Seedat S, Kuivaniemi H, Bardien S. A review of genome-wide transcriptomics studies in Parkinson's disease. Eur J Neurosci 2017; 47:1-16. [DOI: 10.1111/ejn.13760] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Revised: 09/26/2017] [Accepted: 10/19/2017] [Indexed: 12/14/2022]
Affiliation(s)
- Genevie Borrageiro
- Division of Molecular Biology and Human Genetics; Department of Biomedical Sciences; Faculty of Medicine and Health Sciences; Stellenbosch University; PO Box 241 Cape Town South Africa
| | - William Haylett
- Division of Molecular Biology and Human Genetics; Department of Biomedical Sciences; Faculty of Medicine and Health Sciences; Stellenbosch University; PO Box 241 Cape Town South Africa
| | - Soraya Seedat
- Department of Psychiatry; Faculty of Medicine and Health Sciences; Stellenbosch University; Cape Town South Africa
| | - Helena Kuivaniemi
- Division of Molecular Biology and Human Genetics; Department of Biomedical Sciences; Faculty of Medicine and Health Sciences; Stellenbosch University; PO Box 241 Cape Town South Africa
- Department of Psychiatry; Faculty of Medicine and Health Sciences; Stellenbosch University; Cape Town South Africa
| | - Soraya Bardien
- Division of Molecular Biology and Human Genetics; Department of Biomedical Sciences; Faculty of Medicine and Health Sciences; Stellenbosch University; PO Box 241 Cape Town South Africa
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18
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Lin D, Liang Y, Jing X, Chen Y, Lei M, Zeng Z, Zhou T, Wu X, Peng S, Zheng D, Huang K, Yang L, Xiao S, Liu J, Tao E. Microarray analysis of an synthetic α-synuclein induced cellular model reveals the expression profile of long non-coding RNA in Parkinson's disease. Brain Res 2017; 1678:384-396. [PMID: 29137975 DOI: 10.1016/j.brainres.2017.11.007] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2017] [Revised: 11/07/2017] [Accepted: 11/08/2017] [Indexed: 01/10/2023]
Abstract
Long non-coding RNAs (lncRNAs) are a new research focus that are reported to influence the pathogenetic process of neurodegenerative disorders. To uncover new disease-associated genes and their relevant mechanisms, we carried out a gene microarray analysis based on a Parkinson's disease (PD) in vitro model induced by α-synuclein oligomers. This cellular model induced by 25 μmol/L α-synuclein oligomers has been confirmed to show the stable, transmissible neurotoxicity of α-synuclein, a typical PD pathological marker. And several differentially expressed lncRNAs and mRNAs were identified in this model, such as G046036, G030771, AC009365.4, RPS14P3, CTB-11I22.1, and G007549. Subsequent ceRNA analysis determined the potential relationships between these lncRNAs and their associated mRNAs and microRNAs. The results of the present study widen our horizon of PD susceptibility genes and provide new pathways towards efficient diagnostic biomarkers and therapeutic targets for PD.
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Affiliation(s)
- D Lin
- Department of Neurology, The Sun Yat-sen Memorial Hospital of Sun Yat-sen University, 107 Yanjiang West Road, Guangzhou 510080, China
| | - Y Liang
- Department of Neurology, The Sun Yat-sen Memorial Hospital of Sun Yat-sen University, 107 Yanjiang West Road, Guangzhou 510080, China
| | - X Jing
- Department of Neurology, The Sun Yat-sen Memorial Hospital of Sun Yat-sen University, 107 Yanjiang West Road, Guangzhou 510080, China
| | - Y Chen
- Department of Neurology, The Sun Yat-sen Memorial Hospital of Sun Yat-sen University, 107 Yanjiang West Road, Guangzhou 510080, China
| | - M Lei
- Department of Neurology, The Sun Yat-sen Memorial Hospital of Sun Yat-sen University, 107 Yanjiang West Road, Guangzhou 510080, China
| | - Z Zeng
- Department of Neurology, The Sun Yat-sen Memorial Hospital of Sun Yat-sen University, 107 Yanjiang West Road, Guangzhou 510080, China
| | - T Zhou
- Department of Neurology, The Sun Yat-sen Memorial Hospital of Sun Yat-sen University, 107 Yanjiang West Road, Guangzhou 510080, China
| | - X Wu
- Department of Neurology, The Sun Yat-sen Memorial Hospital of Sun Yat-sen University, 107 Yanjiang West Road, Guangzhou 510080, China
| | - S Peng
- Department of Neurology, The Sun Yat-sen Memorial Hospital of Sun Yat-sen University, 107 Yanjiang West Road, Guangzhou 510080, China
| | - D Zheng
- Department of Neurology, The Sun Yat-sen Memorial Hospital of Sun Yat-sen University, 107 Yanjiang West Road, Guangzhou 510080, China
| | - K Huang
- Department of Neurology, The Sun Yat-sen Memorial Hospital of Sun Yat-sen University, 107 Yanjiang West Road, Guangzhou 510080, China
| | - L Yang
- Department of Neurology, The Sun Yat-sen Memorial Hospital of Sun Yat-sen University, 107 Yanjiang West Road, Guangzhou 510080, China
| | - S Xiao
- Department of Neurology, The Sun Yat-sen Memorial Hospital of Sun Yat-sen University, 107 Yanjiang West Road, Guangzhou 510080, China
| | - J Liu
- Department of Neurology, The Sun Yat-sen Memorial Hospital of Sun Yat-sen University, 107 Yanjiang West Road, Guangzhou 510080, China
| | - E Tao
- Department of Neurology, The Sun Yat-sen Memorial Hospital of Sun Yat-sen University, 107 Yanjiang West Road, Guangzhou 510080, China; Guangdong Province Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine, Sun Yat-sen University, 74 Zhongshan 2nd Road, Guangzhou 510080, China.
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Tejera E, Cruz-Monteagudo M, Burgos G, Sánchez ME, Sánchez-Rodríguez A, Pérez-Castillo Y, Borges F, Cordeiro MNDS, Paz-Y-Miño C, Rebelo I. Consensus strategy in genes prioritization and combined bioinformatics analysis for preeclampsia pathogenesis. BMC Med Genomics 2017; 10:50. [PMID: 28789679 PMCID: PMC5549357 DOI: 10.1186/s12920-017-0286-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Accepted: 07/28/2017] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Preeclampsia is a multifactorial disease with unknown pathogenesis. Even when recent studies explored this disease using several bioinformatics tools, the main objective was not directed to pathogenesis. Additionally, consensus prioritization was proved to be highly efficient in the recognition of genes-disease association. However, not information is available about the consensus ability to early recognize genes directly involved in pathogenesis. Therefore our aim in this study is to apply several theoretical approaches to explore preeclampsia; specifically those genes directly involved in the pathogenesis. METHODS We firstly evaluated the consensus between 12 prioritization strategies to early recognize pathogenic genes related to preeclampsia. A communality analysis in the protein-protein interaction network of previously selected genes was done including further enrichment analysis. The enrichment analysis includes metabolic pathways as well as gene ontology. Microarray data was also collected and used in order to confirm our results or as a strategy to weight the previously enriched pathways. RESULTS The consensus prioritized gene list was rationally filtered to 476 genes using several criteria. The communality analysis showed an enrichment of communities connected with VEGF-signaling pathway. This pathway is also enriched considering the microarray data. Our result point to VEGF, FLT1 and KDR as relevant pathogenic genes, as well as those connected with NO metabolism. CONCLUSION Our results revealed that consensus strategy improve the detection and initial enrichment of pathogenic genes, at least in preeclampsia condition. Moreover the combination of the first percent of the prioritized genes with protein-protein interaction network followed by communality analysis reduces the gene space. This approach actually identifies well known genes related with pathogenesis. However, genes like HSP90, PAK2, CD247 and others included in the first 1% of the prioritized list need to be further explored in preeclampsia pathogenesis through experimental approaches.
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Affiliation(s)
- Eduardo Tejera
- Facultad de Medicina, Universidad de Las Américas, Av. de los Granados E12-41y Colimes esq, EC170125, Quito, Ecuador.
| | - Maykel Cruz-Monteagudo
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine and Center for Computational Science, University of Miami, FL 33136, Miami, USA.,Department of General Education, West Coast University-Miami Campus, Doral, FL 33178, USA.,CIQUP/Departamento de Quimica e Bioquimica, Faculdade de Ciências, Universidade do Porto, 4169-007, Porto, Portugal.,REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007, Porto, Portugal
| | - Germán Burgos
- Facultad de Medicina, Universidad de Las Américas, Av. de los Granados E12-41y Colimes esq, EC170125, Quito, Ecuador
| | - María-Eugenia Sánchez
- Facultad de Medicina, Universidad de Las Américas, Av. de los Granados E12-41y Colimes esq, EC170125, Quito, Ecuador
| | - Aminael Sánchez-Rodríguez
- Departamento de Ciencias Naturales, Universidad Técnica Particular de Loja, Calle París S/N, EC1101608, Loja, Ecuador
| | | | - Fernanda Borges
- CIQUP/Departamento de Quimica e Bioquimica, Faculdade de Ciências, Universidade do Porto, 4169-007, Porto, Portugal
| | | | - César Paz-Y-Miño
- Centro de Investigaciones genética y genómica, Facultad de Ciencias de la Salud, Universidad Tecnológica Equinoccial, Quito, Ecuador
| | - Irene Rebelo
- Faculty of Pharmacy, University of Porto, Porto, Portugal.,UCIBIO@REQUIMTE, Caparica, Portugal
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21
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Oerton E, Bender A. Concordance analysis of microarray studies identifies representative gene expression changes in Parkinson's disease: a comparison of 33 human and animal studies. BMC Neurol 2017; 17:58. [PMID: 28335819 PMCID: PMC5364698 DOI: 10.1186/s12883-017-0838-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Accepted: 03/13/2017] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND As the popularity of transcriptomic analysis has grown, the reported lack of concordance between different studies of the same condition has become a growing concern, raising questions as to the representativeness of different study types, such as non-human disease models or studies of surrogate tissues, to gene expression in the human condition. METHODS In a comparison of 33 microarray studies of Parkinson's disease, correlation and clustering analyses were used to determine the factors influencing concordance between studies, including agreement between different tissue types, different microarray platforms, and between neurotoxic and genetic disease models and human Parkinson's disease. RESULTS Concordance over all studies is low, with correlation of only 0.05 between differential gene expression signatures on average, but increases within human patients and studies of the same tissue type, rising to 0.38 for studies of human substantia nigra. Agreement of animal models, however, is dependent on model type. Studies of brain tissue from Parkinson's disease patients (specifically the substantia nigra) form a distinct group, showing patterns of differential gene expression noticeably different from that in non-brain tissues and animal models of Parkinson's disease; while comparison with other brain diseases (Alzheimer's disease and brain cancer) suggests that the mixed study types display a general signal of neurodegenerative disease. A meta-analysis of these 33 microarray studies demonstrates the greater ability of studies in humans and highly-affected tissues to identify genes previously known to be associated with Parkinson's disease. CONCLUSIONS The observed clustering and concordance results suggest the existence of a 'characteristic' signal of Parkinson's disease found in significantly affected human tissues in humans. These results help to account for the consistency (or lack thereof) so far observed in microarray studies of Parkinson's disease, and act as a guide to the selection of transcriptomic studies most representative of the underlying gene expression changes in the human disease.
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Affiliation(s)
- Erin Oerton
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK.
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