Candidate biomarkers of EV-microRNA in detecting REM sleep behavior disorder and Parkinson's disease

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.

The Emerging Role of Long Non-Coding RNAs and MicroRNAs in Neurodegenerative Diseases: A Perspective of Machine Learning

Neurodegenerative diseases (NDs) are characterized by progressive neuronal dysfunction and death of brain cells population. As the early manifestations of NDs are similar, their symptoms are difficult to distinguish, making the timely detection and discrimination of each neurodegenerative disorder a priority. Several investigations have revealed the importance of microRNAs and long non-coding RNAs in neurodevelopment, brain function, maturation, and neuronal activity, as well as its dysregulation involved in many types of neurological diseases. Therefore, the expression pattern of these molecules in the different NDs have gained significant attention to improve the diagnostic and treatment at earlier stages. In this sense, we gather the different microRNAs and long non-coding RNAs that have been reported as dysregulated in each disorder. Since there are a vast number of non-coding RNAs altered in NDs, some sort of synthesis, filtering and organization method should be applied to extract the most relevant information. Hence, machine learning is considered as an important tool for this purpose since it can classify expression profiles of non-coding RNAs between healthy and sick people. Therefore, we deepen in this branch of computer science, its different methods, and its meaningful application in the diagnosis of NDs from the dysregulated non-coding RNAs. In addition, we demonstrate the relevance of machine learning in NDs from the description of different investigations that showed an accuracy between 85% to 95% in the detection of the disease with this tool. All of these denote that artificial intelligence could be an excellent alternative to help the clinical diagnosis and facilitate the identification diseases in early stages based on non-coding RNAs.

Modulation of the Interactions Between α-Synuclein and Lipid Membranes by Post-translational Modifications

Parkinson's disease is characterised by the presence in brain tissue of aberrant inclusions known as Lewy bodies and Lewy neurites, which are deposits composed by α-synuclein and a variety of other cellular components, including in particular lipid membranes. The dysregulation of the balance between lipid homeostasis and α-synuclein homeostasis is therefore likely to be closely involved in the onset and progression of Parkinson's disease and related synucleinopathies. As our understanding of this balance is increasing, we describe recent advances in the characterisation of the role of post-translational modifications in modulating the interactions of α-synuclein with lipid membranes. We then discuss the impact of these advances on the development of novel diagnostic and therapeutic tools for synucleinopathies.

Development of early diagnosis of Parkinson's disease: Illusion or reality?

The fight against neurodegenerative diseases, Alzheimer disease and Parkinson's disease (PD), is a challenge of the 21st century. The low efficacy of treating patients is due to the late diagnosis and start of therapy, after the degeneration of most specific neurons and depletion of neuroplasticity. It is believed that the development of early diagnosis (ED) and preventive treatment will delay the onset of specific symptoms. This review evaluates methodologies for developing ED of PD. Since PD is a systemic disease, and the degeneration of certain neurons precedes that of nigrostriatal dopaminergic neurons that control motor function, the current methodology is based on searching biomarkers, such as premotor symptoms and changes in body fluids (BF) in patients. However, all attempts to develop ED were unsuccessful. Therefore, it is proposed to enhance the current methodology by (i) selecting among biomarkers found in BF in patients at the clinical stage those that are characteristics of animal models of the preclinical stage, (ii) searching biomarkers in BF in subjects at the prodromal stage, selected by detecting premotor symptoms and failure of the nigrostriatal dopaminergic system. Moreover, a new methodology was proposed for the development of ED of PD using a provocative test, which is successfully used in internal medicine.

Serum microRNA expression levels in Turkish patients with Parkinson's disease

Objectives: To determine the serum expression levels of seven candidate microRNAs (miRNA); miR-19a, miR-19b, miR-29a, miR-29c, miR-181, miR-195 and miR-221 in Turkish patients with Parkinson's disease (PD) and explored their potential role in the diagnosis of PD. We further described the relationship between these miRNAs with the clinical findings and treatment of PD.Materials and methods: The study included 51 PD patients and 20 healthy controls. The clinical severity of disease was assessed using the Hoehn Yahr staging scale and the Unified Parkinson's Disease Rating Scale (UPDRS). Venous blood samples were taken after fasting for 12 h, then centrifuged. Obtained serum samples were stored until analysis of miRNA. In the laboratory, expression levels of these miRNAs were analyzed using a real-time PCR instrument. Receiver-operating characteristic analysis and area-under the-curve (AUC) was used to evaluate these miRNA levels as potential diagnostic biomarkers for PD.Results: miR-29c expression levels were increased significantly for PD patients compared to healthy controls. There were no significant differences in levels of other miRNAs between PD patients and controls. The AUC of miR-29c was 0.689. The sensitivity and specificity of this diagnostic test was 54.9% and 80.0%, respectively. miR-195 level was found to have a significant positive correlation only with age. Significant negative correlation was found between miR-29a level and UPDRS total score. miR-19b was found higher in ropinirole drug used group than that of pramipexole group.Conclusion: This study suggests that serum miR-29c expression level might be potential biomarker in the diagnosis of Turkish Parkinson patients.

SG-LSTM-FRAME: a computational frame using sequence and geometrical information via LSTM to predict miRNA-gene associations

MOTIVATION

MircroRNAs (miRNAs) regulate target genes and are responsible for lethal diseases such as cancers. Accurately recognizing and identifying miRNA and gene pairs could be helpful in deciphering the mechanism by which miRNA affects and regulates the development of cancers. Embedding methods and deep learning methods have shown their excellent performance in traditional classification tasks in many scenarios. But not so many attempts have adapted and merged these two methods into miRNA-gene relationship prediction. Hence, we proposed a novel computational framework. We first generated representational features for miRNAs and genes using both sequence and geometrical information and then leveraged a deep learning method for the associations' prediction.

RESULTS

We used long short-term memory (LSTM) to predict potential relationships and proved that our method outperformed other state-of-the-art methods. Results showed that our framework SG-LSTM got an area under curve of 0.94 and was superior to other methods. In the case study, we predicted the top 10 miRNA-gene relationships and recommended the top 10 potential genes for hsa-miR-335-5p for SG-LSTM-core. We also tested our model using a larger dataset, from which 14 668 698 miRNA-gene pairs were predicted. The top 10 unknown pairs were also listed.

AVAILABILITY

Our work can be download in https://github.com/Xshelton/SG_LSTM.

CONTACT

luojiawei@hnu.edu.cn.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Briefings in Bioinformatics online.

Elevated Plasma microRNA-105-5p Level in Patients With Idiopathic Parkinson's Disease: A Potential Disease Biomarker

Parkinson's disease (PD) is the second most common neurodegenerative disease, which still lacks a biomarker to aid in diagnosis and to differentiate diagnosis at the early stage of the disease. microRNAs (miRNAs) are small and evolutionary conserved non-coding RNAs that are involved in post-transcriptional gene regulation. Several miRNAs have been proposed as potential biomarkers in several diseases. In the present study, we screened miRNAs using a network vulnerability analysis, to evaluate their potential as PD biomarkers. We first extracted miRNAs that were differentially expressed between PD and healthy controls (HC) samples. Then we constructed the PD-specific miRNA-mRNA network and screened miRNA biomarkers using a new bioinformatics model. With this model, we identified miR-105-5p as a putative biomarker for PD. Moreover, we measured miR-105-5p levels in the plasma of patients with idiopathic PD (IPD) (n = 319), neurological disease controls (NDC, n = 305) and HC (n = 273) using reverse transcription real-time quantitative PCR (RT-qPCR). Our data clearly demonstrated that the plasma miR-105-5p level in IPD patients was significantly higher than those of HC (251%, p < 0.001) and NDC (347%, p < 0.001). There was no significant difference in miR-105-5p expression between IPD patients with or without anti-PD medications. Interestingly, we found that the plasma miR-105-5p expression level may be able to differentiate IPD from parkinsonian syndrome, essential tremor and other neurodegenerative diseases. We believe that a change in the plasma miR-105-5p level is a potential biomarker for IPD.

Evaluation of cerebrospinal fluid proteins as potential biomarkers for early stage Parkinson's disease diagnosis

Cerebrospinal fluid (CSF) has often been used as the source of choice for biomarker discovery with the goal to support the diagnosis of neurodegenerative diseases. For this study, we selected 15 CSF protein markers which were identified in previously published clinical investigations and proposed as potential biomarkers for PD diagnosis. We aimed at investigating and confirming their suitability for early stage diagnosis of the disease. The current study was performed in a two-fold confirmatory approach. Firstly, the CSF protein markers were analysed in confirmatory cohort I comprising 80 controls and 80 early clinical PD patients. Through univariate analysis we found significant changes of six potential biomarkers (α-syn, DJ-1, Aβ42, S100β, p-Tau and t-Tau). In order to increase robustness of the observations for potential patient differentiation, we developed-based on a machine learning approach-an algorithm which enabled identifying a panel of markers which would improve clinical diagnosis. Based on that model, a panel comprised of α-syn, S100β and UCHL1 were suggested as promising candidates. Secondly, we aimed at replicating our observations in an independent cohort (confirmatory cohort II) comprising 30 controls and 30 PD patients. The univariate analysis demonstrated Aβ42 as the only reproducible potential biomarker. Taking into account both technical and clinical aspects, these observations suggest that the large majority of the investigated CSF proteins currently proposed as potential biomarkers lack robustness and reproducibility in supporting diagnosis in the early clinical stages of PD.

miRNA-based signatures in cerebrospinal fluid as potential diagnostic tools for early stage Parkinson's disease

Parkinson’s Disease is the second most common neurodegenerative disorder, affecting 1–2% of the elderly population. Its diagnosis is still based on the identification of motor symptoms when a considerable number of dopaminergic neurons are already lost. The development of translatable biomarkers for accurate diagnosis at the earliest stages of PD is of extreme interest. Several microRNAs have been associated with PD pathophysiology. Consequently, microRNAs are emerging as potential biomarkers, especially due to their presence in Cerebrospinal Fluid and peripheral circulation. This study employed small RNA sequencing, protein binding ligand assays and machine learning in a cross-sectional cohort comprising 40 early stage PD patients and 40 well-matched controls. We identified a panel comprising 5 microRNAs (Let-7f-5p, miR-27a-3p, miR-125a-5p, miR-151a-3p and miR-423-5p), with 90% sensitivity, 80% specificity and 82% area under the curve (AUC) for the differentiation of the cohorts. Moreover, we combined miRNA profiles with hallmark-proteins of PD and identified a panel (miR-10b-5p, miR-22-3p, miR-151a-3p and α-synuclein) reaching 97% sensitivity, 90% specificity and 96% AUC. We performed a gene ontology analysis for the genes targeted by the microRNAs present in each panel and showed the likely association of the models with pathways involved in PD pathogenesis.

MicroRNAs and Target Genes As Biomarkers for the Diagnosis of Early Onset of Parkinson Disease

Among the neurodegenerative disorders, Parkinson's disease (PD) ranks as the second most common disorder with a higher prevalence in individuals aged over 60 years old. Younger individuals may also be affected with PD which is known as early onset PD (EOPD). Despite similarities between the characteristics of EOPD and late onset PD (LODP), EOPD patients experience much longer disease manifestations and poorer quality of life. Although some individuals are more prone to have EOPD due to certain genetic alterations, the molecular mechanisms that differentiate between EOPD and LOPD remains unclear. Recent findings in PD patients revealed that there were differences in the genetic profiles of PD patients compared to healthy controls, as well as between EOPD and LOPD patients. There were variants identified that correlated with the decline of cognitive and motor symptoms as well as non-motor symptoms in PD. There were also specific microRNAs that correlated with PD progression, and since microRNAs have been shown to be involved in the maintenance of neuronal development, mitochondrial dysfunction and oxidative stress, there is a strong possibility that these microRNAs can be potentially used to differentiate between subsets of PD patients. PD is mainly diagnosed at the late stage, when almost majority of the dopaminergic neurons are lost. Therefore, identification of molecular biomarkers for early detection of PD is important. Given that miRNAs are crucial in controlling the gene expression, these regulatory microRNAs and their target genes could be used as biomarkers for early diagnosis of PD. In this article, we discussed the genes involved and their regulatory miRNAs, regarding their roles in PD progression, based on the findings of significantly altered microRNAs in EOPD studies. We also discussed the potential of these miRNAs as molecular biomarkers for early diagnosis.

Can Biomarkers Help the Early Diagnosis of Parkinson's Disease?

Parkinson's disease (PD) is a complex neurodegenerative disease with progressive loss of dopamine neurons. PD patients usually manifest a series of motor and non-motor symptoms. In order to provide better early diagnosis and subsequent disease-modifying therapies for PD patients, there is an urgent need to identify sensitive and specific biomarkers. Biomarkers can be divided into four categories: clinical, imaging, biochemical, and genetic. Ideal biomarkers not only improve our understanding of PD pathogenesis and progression, but also provide benefits for early risk evaluation and clinical diagnosis of PD. Although many efforts have been made and several biomarkers have been extensively investigated, few if any have been found useful for early diagnosis. Here, we summarize recent developments in the discovered biomarkers of PD and discuss their merits and limitations for the early diagnosis of PD.