Candidate biomarkers of EV-microRNA in detecting REM sleep behavior disorder and Parkinson’s disease
Abstract Parkinson’s disease (PD) lacks reliable, non-invasive biomarker tests for early intervention and management. Thus, a minimally invasive test for the early detection and monitoring of PD and REM sleep behavior disorder (iRBD) is a highly unmet need for developing drugs and planning patient c...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-01-01
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| Series: | npj Parkinson's Disease |
| Online Access: | https://doi.org/10.1038/s41531-023-00628-4 |
| _version_ | 1797355951112585216 |
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| author | Yuanyuan Li Ying Cao Wei Liu Fangzheng Chen Hongdao Zhang Haisheng Zhou Aonan Zhao Ningdi Luo Jun Liu Ligang Wu |
| author_facet | Yuanyuan Li Ying Cao Wei Liu Fangzheng Chen Hongdao Zhang Haisheng Zhou Aonan Zhao Ningdi Luo Jun Liu Ligang Wu |
| author_sort | Yuanyuan Li |
| collection | DOAJ |
| description | Abstract Parkinson’s disease (PD) lacks reliable, non-invasive biomarker tests for early intervention and management. Thus, a minimally invasive test for the early detection and monitoring of PD and REM sleep behavior disorder (iRBD) is a highly unmet need for developing drugs and planning patient care. Extracellular vehicles (EVs) are found in a wide variety of biofluids, including plasma. EV-mediated functional transfer of microRNAs (miRNAs) may be viable candidates as biomarkers for PD and iRBD. Next-generation sequencing (NGS) of EV-derived small RNAs was performed in 60 normal controls, 56 iRBD patients and 53 PD patients to profile small non-coding RNAs (sncRNAs). Moreover, prospective follow-up was performed for these 56 iRBD patients for an average of 3.3 years. Full-scale miRNA profiles of plasma EVs were evaluated by machine-learning methods. After optimizing the library construction method for low RNA inputs (named EVsmall-seq), we built a machine learning algorithm that identified diagnostic miRNA signatures for distinguishing iRBD patients (AUC 0.969) and PD patients (AUC 0.916) from healthy individuals; and PD patients (AUC 0.929) from iRBD patients. We illustrated all the possible expression patterns across healthy-iRBD-PD hierarchy. We also showed 20 examples of miRNAs with consistently increasing or decreasing expression levels from controls to iRBD to PD. In addition, four miRNAs were found to be correlated with iRBD conversion. Distinct characteristics of the miRNA profiles among normal, iRBD and PD samples were discovered, which provides a panel of promising biomarkers for the identification of PD patients and those in the prodromal stage iRBD. |
| first_indexed | 2024-03-08T14:18:34Z |
| format | Article |
| id | doaj.art-d6d5b89cca1545f4bfe8c1d208a297b1 |
| institution | Directory Open Access Journal |
| issn | 2373-8057 |
| language | English |
| last_indexed | 2024-03-08T14:18:34Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Parkinson's Disease |
| spelling | doaj.art-d6d5b89cca1545f4bfe8c1d208a297b12024-01-14T12:16:17ZengNature Portfolionpj Parkinson's Disease2373-80572024-01-0110111110.1038/s41531-023-00628-4Candidate biomarkers of EV-microRNA in detecting REM sleep behavior disorder and Parkinson’s diseaseYuanyuan Li0Ying Cao1Wei Liu2Fangzheng Chen3Hongdao Zhang4Haisheng Zhou5Aonan Zhao6Ningdi Luo7Jun Liu8Ligang Wu9Department of Neurology & Institute of Neurology, Ruijin Hospital affiliated to Shanghai Jiaotong University School of MedicineKey Laboratory of RNA Science and Engineering, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of SciencesKey Laboratory of RNA Science and Engineering, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of SciencesDepartment of Neurology & Institute of Neurology, Ruijin Hospital affiliated to Shanghai Jiaotong University School of MedicineKey Laboratory of RNA Science and Engineering, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of SciencesKey Laboratory of RNA Science and Engineering, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of SciencesDepartment of Neurology & Institute of Neurology, Ruijin Hospital affiliated to Shanghai Jiaotong University School of MedicineDepartment of Neurology & Institute of Neurology, Ruijin Hospital affiliated to Shanghai Jiaotong University School of MedicineDepartment of Neurology & Institute of Neurology, Ruijin Hospital affiliated to Shanghai Jiaotong University School of MedicineKey Laboratory of RNA Science and Engineering, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of SciencesAbstract Parkinson’s disease (PD) lacks reliable, non-invasive biomarker tests for early intervention and management. Thus, a minimally invasive test for the early detection and monitoring of PD and REM sleep behavior disorder (iRBD) is a highly unmet need for developing drugs and planning patient care. Extracellular vehicles (EVs) are found in a wide variety of biofluids, including plasma. EV-mediated functional transfer of microRNAs (miRNAs) may be viable candidates as biomarkers for PD and iRBD. Next-generation sequencing (NGS) of EV-derived small RNAs was performed in 60 normal controls, 56 iRBD patients and 53 PD patients to profile small non-coding RNAs (sncRNAs). Moreover, prospective follow-up was performed for these 56 iRBD patients for an average of 3.3 years. Full-scale miRNA profiles of plasma EVs were evaluated by machine-learning methods. After optimizing the library construction method for low RNA inputs (named EVsmall-seq), we built a machine learning algorithm that identified diagnostic miRNA signatures for distinguishing iRBD patients (AUC 0.969) and PD patients (AUC 0.916) from healthy individuals; and PD patients (AUC 0.929) from iRBD patients. We illustrated all the possible expression patterns across healthy-iRBD-PD hierarchy. We also showed 20 examples of miRNAs with consistently increasing or decreasing expression levels from controls to iRBD to PD. In addition, four miRNAs were found to be correlated with iRBD conversion. Distinct characteristics of the miRNA profiles among normal, iRBD and PD samples were discovered, which provides a panel of promising biomarkers for the identification of PD patients and those in the prodromal stage iRBD.https://doi.org/10.1038/s41531-023-00628-4 |
| spellingShingle | Yuanyuan Li Ying Cao Wei Liu Fangzheng Chen Hongdao Zhang Haisheng Zhou Aonan Zhao Ningdi Luo Jun Liu Ligang Wu Candidate biomarkers of EV-microRNA in detecting REM sleep behavior disorder and Parkinson’s disease npj Parkinson's Disease |
| title | Candidate biomarkers of EV-microRNA in detecting REM sleep behavior disorder and Parkinson’s disease |
| title_full | Candidate biomarkers of EV-microRNA in detecting REM sleep behavior disorder and Parkinson’s disease |
| title_fullStr | Candidate biomarkers of EV-microRNA in detecting REM sleep behavior disorder and Parkinson’s disease |
| title_full_unstemmed | Candidate biomarkers of EV-microRNA in detecting REM sleep behavior disorder and Parkinson’s disease |
| title_short | Candidate biomarkers of EV-microRNA in detecting REM sleep behavior disorder and Parkinson’s disease |
| title_sort | candidate biomarkers of ev microrna in detecting rem sleep behavior disorder and parkinson s disease |
| url | https://doi.org/10.1038/s41531-023-00628-4 |
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