The Regulation of miR-206 on BDNF: A Motor Function Restoration Mechanism Research on Cerebral Ischemia Rats by Meridian Massage

Promise of mesenchymal stem cell-derived extracellular vesicles for alleviating subarachnoid hemorrhage-induced brain dysfunction by neuroprotective and antiinflammatory effects

Subarachnoid hemorrhage (SAH), accounting for ∼5% of all strokes, represents a catastrophic subtype of cerebrovascular accident. SAH predominantly results from intracranial aneurysm ruptures and affects ∼30,000 individuals annually in the United States and ∼6 individuals per 100,000 people worldwide. Recent studies have implicated that administering mesenchymal stem cell-derived extracellular vesicles (MSC-EVs) may be beneficial in inducing neuroprotective and antiinflammatory effects following SAH. EVs are nanosized particles bound by a lipid bilayer. MSC-EVs comprise a therapeutic cargo of nucleic acids, lipids, and proteins, having the promise to ease SAH-induced long-term brain impairments. This review evaluated the findings of published studies on the therapeutic efficacy of MSC-EVs in the context of SAH. A growing body of evidence points out the therapeutic potential of MSC-EVs for improving brain function in animal models of SAH. Specifically, studies demonstrated their ability to reduce neuronal apoptosis and neuroinflammation and enhance neurological recovery through neuroprotective and antiinflammatory mechanisms. Such outcomes reported in various studies suggest that MSC-EVs hold great potential as a novel and minimally invasive approach to ameliorate SAH-induced neurological damage and improve patient outcomes. The review also discusses the limitations of EV therapy and the required future research efforts toward harnessing the full potential of MSC-EVs in treating SAH.

Potential role of the lncRNA "HOTAIR"/miRNA "206"/BDNF network in the alteration in expression of synaptic plasticity gene arc and BDNF level in sera of patients with heroin use disorder through the PI3K/AKT/mTOR pathway compared to the controls

INTRODUCTION

Heroin use disorder (HUD) is a seriously increasing health issue, accounting for most deaths among drug abusers. Studying non-coding ribonucleic acid gene expression among drug abusers is a promising approach, as it may be used in diagnosis and therapeutics.

PARTICIPANTS AND METHODS

A total of 49 male heroin-dependent patients and 49 male control participants were recruited from Kasr Al Ainy Psychiatry and Addiction outpatient clinics, Faculty of Medicine, Cairo University. Sera were gathered. qRT-PCR was utilized for the detection of gene expression of non-coding RNAs such as "HOX transcript antisense RNA" (HOTAIR), micro-RNA (miRNA-206), phosphatidylinositol 3-kinase (PI3K), protein kinase B (AKT), mechanistic target of rapamycin (mTOR), and Activity Regulated Cytoskeleton Associated Protein (Arc). Sera Brain-Derived Neurotrophic Factor (BDNF) levels were assessed using ELISA. Using a western blot made it possible to determine the protein expression of PI3K, AKT, and mTOR.

RESULTS

The study demonstrated that gene expressions of HOTAIR, AKT, PI3K, and Arc were considerably lowered between cases and controls, while gene expressions of miR-206 and mTOR1 were significantly raised. PI3K and AKT protein expressions were downregulated, while mTOR expressions were upregulated. BDNF levels were significantly decreased in some cases.

CONCLUSION

The results of this study suggest that decreased HOTAIR in HUD relieves miR-206 inhibition, which thus increases and affects downstream PI3K/AKT/mTOR, ARC, and BDNF expression. This may be shared in addictive and relapsing behaviors.

GKLOMLI: a link prediction model for inferring miRNA-lncRNA interactions by using Gaussian kernel-based method on network profile and linear optimization algorithm

BACKGROUND

The limited knowledge of miRNA-lncRNA interactions is considered as an obstruction of revealing the regulatory mechanism. Accumulating evidence on Human diseases indicates that the modulation of gene expression has a great relationship with the interactions between miRNAs and lncRNAs. However, such interaction validation via crosslinking-immunoprecipitation and high-throughput sequencing (CLIP-seq) experiments that inevitably costs too much money and time but with unsatisfactory results. Therefore, more and more computational prediction tools have been developed to offer many reliable candidates for a better design of further bio-experiments.

METHODS

In this work, we proposed a novel link prediction model based on Gaussian kernel-based method and linear optimization algorithm for inferring miRNA-lncRNA interactions (GKLOMLI). Given an observed miRNA-lncRNA interaction network, the Gaussian kernel-based method was employed to output two similarity matrixes of miRNAs and lncRNAs. Based on the integrated matrix combined with similarity matrixes and the observed interaction network, a linear optimization-based link prediction model was trained for inferring miRNA-lncRNA interactions.

RESULTS

To evaluate the performance of our proposed method, k-fold cross-validation (CV) and leave-one-out CV were implemented, in which each CV experiment was carried out 100 times on a training set generated randomly. The high area under the curves (AUCs) at 0.8623 ± 0.0027 (2-fold CV), 0.9053 ± 0.0017 (5-fold CV), 0.9151 ± 0.0013 (10-fold CV), and 0.9236 (LOO-CV), illustrated the precision and reliability of our proposed method.

CONCLUSION

GKLOMLI with high performance is anticipated to be used to reveal underlying interactions between miRNA and their target lncRNAs, and deciphers the potential mechanisms of the complex diseases.