Candidate biomarkers of EV-microRNA in detecting REM sleep behavior disorder and Parkinson’s disease
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA699177 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Our study provides a valuable resource for small RNA profiles in normal, iRBD and PD plasma EVs, and showed that the EV-miRNAs have the potential to serve as biomarker for diagnosing the PD patients and iRBD patients.
2024-01-16 | GSE166070 | GEO
Project description:The perioculomotor (pIII) region of the midbrain was postulated as a sleep-regulating center in the 1890s but largely neglected in subsequent studies. Using activity-dependent labeling and gene-expression profiling, we identified pIII neurons that promote non-REM (NREM) sleep.
2019-05-01 | GSE124681 | GEO
Project description:An Excitatory Circuit in Perioculomotor Midbrain for Non-REM Sleep Control
| PRJNA513052 | ENA
Project description: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.
| S-EPMC10781790 | biostudies-literature
Project description:Molecular composition of circulating small extracellular vesicles (EVs) does not merely reflect the cells of origin, but also is enriched in specific biomolecules directly associated with the cellular transformation. Given their functional and clinical relevance, small EVs have emerged as a promising target among the liquid biopsy approaches for cancer detection. However, while most of the currently identified EV-miRs are only geared towards one-dimensional disease detection, their application for long-term tracking and treatment response monitoring has been largely elusive. In this study, we established and optimized a rapid, sensitive and robust liquid biopsy sampling method, and further used small RNA sequencing to comprehensively catalogue EV-miRomes in association with the progression and outcome of metastatic colorectal cancer (mCRC). By cross-comparison of EV-miRomes (n = 290) from multi-stage and longitundial cohorts, we uncovered 28 EV-miRs for detecting mCRC, and 131 EV-miRs for long-term monitoring of tumor size progression. Among these candidates, we further pinpointed a 15-EV-miR signature with dual detection and monitoring functions for mCRC. From this panel, EV-miR-320c was uncovered as a strong clinical marker – aside from its diagnostic power, its high expression has also been linked to lower overall survival and a greater likelihood of disease recurrence. Furthermore, target spectrum of this 15-EV-miR signature of mCRC suggested an involvement of small EVs in programming the mesenchymal–epithelial transition (MET) transition for distant localization of the metastasized cells and also in creating a tumor-favoring metastatic niche. Our clinically-oriented delineation of the mCRC-associated circulating EV-miRomes revealed the functional significance of these liquid biopsy markers and further strengthen their translational potential in tumor therapeutic monitoring.
2022-09-14 | GSE188627 | GEO
Project description:small RNA libraries from total RNA isolated from young adult animals Wild-type and rem-1 mutant animals were used for RNA isolation. Regular libraries were made using adaptor ligations at both ends. In addition, librraies were made from oxidised and TAP treated RNA.
2012-10-30 | E-GEOD-31783 | biostudies-arrayexpress
Project description:Imprinted genes are highly expressed in the hypothalamus, however whether specific imprinted genes affect hypothalamic neuromodulators and their functions is unknown. It has been suggested that Prader-Willi syndrome (PWS), a neurodevelopmental disorder caused by lack of paternal expression at the chromosome 15q11-q13, characterised with a hypothalamic insufficiency. Here we investigate the role of paternally expressed Snord116 gene within the context of sleep and metabolic abnormalities of PWS, and we report a novel role of this imprinted gene in the function and organisation of the two main neuromodulatory systems of the lateral hypothalamus (LH), namely the orexin (OX) and the melanin concentrating hormone (MCH). We observe that the dynamic between neuronal discharge in the LH and sleep-wake states of mice carrying the paternal deletion of the Snord116 (PWScrm+/p-) is compromised. This abnormal state-dependent neuronal activity is paralleled by a significant reduction of OX neurons in LH of mutants. Therefore, we propose that unbalance between OX- and MCH- expressing neurons in the LH of mutants reflects in a series of deficits manifested in the PWS, such as dysregulation of rapid eye movement (REM) sleep, food intake and temperature control.
2020-05-25 | GSE139524 | GEO
Project description:Background and purpose: Growing evidence suggests that Machine Learning (ML) models can assist the diagnosis of neurological disorders. However, little is known about the potential application of ML in diagnosing idiopathic REM sleep behavior disorder (iRBD), a parasomnia characterized by a high risk of phenoconversion to synucleinopathies. This study aimed to develop a model using ML algorithms to identify iRBD patients and test its accuracy. Methods: Data were acquired from 32 participants (20 iRBD patients and 12 controls). All subjects underwent a video-polysomnography. In all subjects, we measured the components of heart rate variability (HRV) during 24 h recordings and calculated night-to-day ratios (cardiac autonomic indices). Discriminating performances of single HRV features were assessed. ML models based on Logistic Regression (LR), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) were trained on HRV data. The utility of HRV features and ML models for detecting iRBD was evaluated by area under the ROC curve (AUC), sensitivity, specificity and accuracy corresponding to optimal models. Results: Cardiac autonomic indices had low performances (accuracy 63-69%) in distinguishing iRBD from control subjects. By contrast, the RF model performed the best, with excellent accuracy (94%), sensitivity (95%) and specificity (92%), while XGBoost showed accuracy (91%), specificity (83%) and sensitivity (95%). The mean triangular index during wake (TIw) was the best discriminating feature between iRBD and HC, with 81% accuracy, reaching 84% accuracy when combined with VLF power during sleep using an LR model. Conclusions: Our findings demonstrated that ML algorithms can accurately identify iRBD patients. Our model could be used in clinical practice to facilitate the early detection of this form of RBD.
| S-EPMC9689751 | biostudies-literature
Project description:In Alzheimer’s disease (AD), pathophysiological changes in the hippocampus cause deficits in episodic memory formation, leading to cognitive impairment. Hippocampal hyperactivity and decreased sleep quality are associated with early AD, but their basis is poorly understood. We find that homeostatic mechanisms transiently counteract increased excitatory drive of hippocampal CA1 neurons in AppNL-G-F mice, but fail to stabilize it at control levels. Spatial transcriptomics (ST) analysis identifies the Pmch gene encoding Melanin-Concentrating Hormone (MCH) as part of the adaptive response in AppNL-G-F mice. Hypothalamic MCH peptide is produced in sleep-active lateral hypothalamic neurons that project to CA1 and modulate memory. We show that MCH downregulates synaptic transmission and modulates firing rate homeostasis in hippocampal neurons. Moreover, MCH reverses the increased excitatory drive of CA1 neurons in AppNL-G-F mice. Consistent with our finding that a reduced fraction of MCH-neurons is active in AppNL-G-F mice, these animals spend less time in rapid eye movement (REM) sleep. In addition, MCH-axons projecting to CA1 become progressively impaired in both AppNL-G-F mice and AD patients. Our findings identify the MCH-system as vulnerable in early AD and suggest that impaired MCH-system function contributes to aberrant excitatory drive and sleep defects, which can compromise hippocampal-dependent functions.
2023-09-07 | GSE225181 | GEO