MicroRNA: Implications for Alzheimer Disease and other Human CNS Disorders

Maës, Olivier; Chertkow, Howard; Wang, Eugenia; Schipper, Hyman M.

doi:10.2174/138920209788185252

Cited by 186 publications

(133 citation statements)

References 198 publications

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“…The question is whether there are evolutionary-conserved homologs of lin-4 and lin-14 that control human longevity and if miRNAs are implicated in many age-associated pathologies. Other miRNAs are upregulated during aging as let-7f, miR-30d, miR -432, miR-517 and downregulated as let7i and miR-451 (Maes et al, 2009). Are they involved in gene regulation of aging related mechanism in AD?…”

Section: Several Mirnas Regulate Appmentioning

confidence: 99%

Advances in MicroRNAs and Alzheimer’s Disease Research

Ruberti¹,

Pezzola²,

Barbato³

2011

Alzheimer's Disease Pathogenesis-Core Concepts, Shifting Paradigms and Therapeutic Targets

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Section: Several Mirnas Regulate Appmentioning

confidence: 99%

Advances in MicroRNAs and Alzheimer’s Disease Research

Ruberti¹,

Pezzola²,

Barbato³

2011

Alzheimer's Disease Pathogenesis-Core Concepts, Shifting Paradigms and Therapeutic Targets

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“…MiRNAs function by regulating gene expression either by translational inhibition or message degradation, and thus are key to many biological processes. Dysregulation of miRNAs is known to cause a wide range of diseases (miR2Disease database Jiang et al 2009) such as hereditary progressive hearing loss (Mencía et al 2009), growth and skeleton defects (de Pontual et al 2011), various cancers Mraz et al 2009), heart diseases (Thum et al 2007), and Alzheimer's disease (Maes et al 2009). Mature miRNAs are made from precursors (pre-miRNAs) of ∼90 nt in length, characterized by a hairpin-like structure.…”

Section: Introductionmentioning

confidence: 99%

miRBoost: boosting support vector machines for microRNA precursor classification

Tran¹,

Tempel²,

Zerath³

et al. 2015

RNA

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Identification of microRNAs (miRNAs) is an important step toward understanding post-transcriptional gene regulation and miRNA-related pathology. Difficulties in identifying miRNAs through experimental techniques combined with the huge amount of data from new sequencing technologies have made in silico discrimination of bona fide miRNA precursors from non-miRNA hairpin-like structures an important topic in bioinformatics. Among various techniques developed for this classification problem, machine learning approaches have proved to be the most promising. However these approaches require the use of training data, which is problematic due to an imbalance in the number of miRNAs (positive data) and non-miRNAs (negative data), which leads to a degradation of their performance. In order to address this issue, we present an ensemble method that uses a boosting technique with support vector machine components to deal with imbalanced training data. Classification is performed following a feature selection on 187 novel and existing features. The algorithm, miRBoost, performed better in comparison with state-of-the-art methods on imbalanced human and cross-species data. It also showed the highest ability among the tested methods for discovering novel miRNA precursors. In addition, miRBoost was over 1400 times faster than the second most accurate tool tested and was significantly faster than most of the other tools. miRBoost thus provides a good compromise between prediction efficiency and execution time, making it highly suitable for use in genome-wide miRNA precursor prediction. The software miRBoost is available on our web server http://EvryRNA.ibisc.univ-evry.fr.

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“…MiRNAs are being actively explored for their potential as drug targets 1,2 . Further, alterations in miRNA expression are associated with several diseases 3 and simulation of these changes by artificial perturbation of miRNA expression can be used to study the cellular pathways involved in disease manifestation.…”

Section: Introductionmentioning

confidence: 99%

A Simple Alternative to Stereotactic Injection for Brain Specific Knockdown of miRNA

Suryawanshi

Sarangdhar

Vij

et al. 2015

JoVE

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MicroRNAs (miRNAs) are key regulators of gene expression. In the brain, vital processes like neurodevelopment and neuronal functions depend on the correct expression of microRNAs. Perturbation of microRNAs in the brain can be used to model neurodegenerative diseases by modulating neuronal cell death. Currently, stereotactic injection is used to deliver miRNA knockdown agents to specific location in the brain. Here, we discuss strategies to design antagomirs against miRNA with locked nucleotide modifications (LNA). Subsequently describe a method for brain specific delivery of antagomirs, uniformly across different regions of the brain. This method is simple and widely applicable since it overcomes the surgery, associated injury and limitation of local delivery in stereotactic injections. We prepared a complex of neurotropic, cellpenetrating peptide Rabies Virus Glycoprotein (RVG) with antagomir against miRNA-29 and injected through tail vein, to specifically deliver in the brain. The antagomir design incorporated features that allow specific targeting of the miRNA and formation of non-covalent complexes with the peptide. The knock-down of the miRNA in neuronal cells, resulted in apoptotic cell death and associated behavioural defects. Thus, the method can be used for acute models of neuro-degeneration through the perturbation of miRNAs. Video LinkThe video component of this article can be found at

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MicroRNA: Implications for Alzheimer Disease and other Human CNS Disorders

Cited by 186 publications

References 198 publications

Advances in MicroRNAs and Alzheimer’s Disease Research

Advances in MicroRNAs and Alzheimer’s Disease Research

miRBoost: boosting support vector machines for microRNA precursor classification

A Simple Alternative to Stereotactic Injection for Brain Specific Knockdown of miRNA

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