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      MicroRNA-based therapeutics in central nervous system injuries

      Author(s): 1 , 2 , 3 , 2 , 1
      Publication date Created:
      Publication date (Electronic):
      Journal: Journal of Cerebral Blood Flow & Metabolism
      Publisher: SAGE Publications

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          Abstract

          Central nervous system (CNS) injuries, such as stroke, traumatic brain injury (TBI) and spinal cord injury (SCI), are important causes of death and long-term disability worldwide. MicroRNA (miRNA), small non-coding RNA molecules that negatively regulate gene expression, can serve as diagnostic biomarkers and are emerging as novel therapeutic targets for CNS injuries. MiRNA-based therapeutics include miRNA mimics and inhibitors (antagomiRs) to respectively decrease and increase the expression of target genes. In this review, we summarize current miRNA-based therapeutic applications in stroke, TBI and SCI. Administration methods, time windows and dosage for effective delivery of miRNA-based drugs into CNS are discussed. The underlying mechanisms of miRNA-based therapeutics are reviewed including oxidative stress, inflammation, apoptosis, blood-brain barrier protection, angiogenesis and neurogenesis. Pharmacological agents that protect against CNS injuries by targeting specific miRNAs are presented along with the challenges and therapeutic potential of miRNA-based therapies.

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          Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs.

          Lee P. Lim, Nelson C. Lau, Philip Garrett-Engele … (2005)
          MicroRNAs (miRNAs) are a class of noncoding RNAs that post-transcriptionally regulate gene expression in plants and animals. To investigate the influence of miRNAs on transcript levels, we transfected miRNAs into human cells and used microarrays to examine changes in the messenger RNA profile. Here we show that delivering miR-124 causes the expression profile to shift towards that of brain, the organ in which miR-124 is preferentially expressed, whereas delivering miR-1 shifts the profile towards that of muscle, where miR-1 is preferentially expressed. In each case, about 100 messages were downregulated after 12 h. The 3' untranslated regions of these messages had a significant propensity to pair to the 5' region of the miRNA, as expected if many of these messages are the direct targets of the miRNAs. Our results suggest that metazoan miRNAs can reduce the levels of many of their target transcripts, not just the amount of protein deriving from these transcripts. Moreover, miR-1 and miR-124, and presumably other tissue-specific miRNAs, seem to downregulate a far greater number of targets than previously appreciated, thereby helping to define tissue-specific gene expression in humans.
          Article 0 comments Cited 1425 times – based on 0 reviews     Review now
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            Is Open Access

            The Nrf2-antioxidant response element signaling pathway and its activation by oxidative stress.

            Truyen Nguyen, Paul Nioi, Cecil B. Pickett (2009)
            A major mechanism in the cellular defense against oxidative or electrophilic stress is activation of the Nrf2-antioxidant response element signaling pathway, which controls the expression of genes whose protein products are involved in the detoxication and elimination of reactive oxidants and electrophilic agents through conjugative reactions and by enhancing cellular antioxidant capacity. At the molecular level, however, the regulatory mechanisms involved in mediating Nrf2 activation are not fully understood. It is well established that Nrf2 activity is controlled, in part, by the cytosolic protein Keap1, but the nature of this pathway and the mechanisms by which Keap1 acts to repress Nrf2 activity remain to be fully characterized and are the topics of discussion in this minireview. In addition, a possible role of the Nrf2-antioxidant response element transcriptional pathway in neuroprotection will also be discussed.
            Article 0 comments Cited 826 times – based on 0 reviews     Review now
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              Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites

              Doron Betel, Anjali Koppal, Phaedra Agius … (2010)
              mirSVR is a new machine learning method for ranking microRNA target sites by a down-regulation score. The algorithm trains a regression model on sequence and contextual features extracted from miRanda-predicted target sites. In a large-scale evaluation, miRanda-mirSVR is competitive with other target prediction methods in identifying target genes and predicting the extent of their downregulation at the mRNA or protein levels. Importantly, the method identifies a significant number of experimentally determined non-canonical and non-conserved sites.
              Article 0 comments Cited 736 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Title: Journal of Cerebral Blood Flow & Metabolism
                Abbreviated Title: J Cereb Blood Flow Metab
                Publisher: SAGE Publications
                ISSN (Print): 0271-678X
                ISSN (Electronic): 1559-7016
                Publication date Created: July 25 2017
                Publication date Created: July 2018
                Publication date (Electronic): April 30 2018
                Publication date (Print): July 2018
                Volume: 38
                Issue: 7
                Pages: 1125-1148
                Affiliations
                [1 ]Department of Neurology, Pittsburgh Institute of Brain Disorders & Recovery, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
                [2 ]Department of Neurology and the M.I.N.D. Institute, University of California at Davis, Sacramento, CA, USA
                [3 ]Department of Neurology, University of Alberta, Edmonton, Alberta, Canada
                Article
                DOI: 10.1177/0271678X18773871
                PMC ID: 6434449
                PubMed ID: 29708005
                SO-VID: 7398b4bf-43a9-454d-bb04-c8da98e6c9c4
                Copyright © © 2018
                License:

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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