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2016 Plasma microRNA markers of upper limb recovery following human stroke
- Matthew A Edwardson, Xiaogang Zhong, Amrita Cheema, Alexander Dromerick
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- Journal:
- Journal of Clinical and Translational Science / Volume 2 / Issue S1 / June 2018
- Published online by Cambridge University Press:
- 21 November 2018, p. 45
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- Article
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- You have access Access
- Open access
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OBJECTIVES/SPECIFIC AIMS: MicroRNAs are small, non-coding RNAs that control gene expression by inhibiting protein translation. Preclinical studies in rodent stroke models suggest that changes in microRNA expression contribute to neural repair mechanisms. To our knowledge, no one has previously assessed microRNA changes during the recovery phase of human stroke. Our goal was to determine whether patients with significant upper limb recovery following stroke have alteration of neural repair-related microRNA expression when compared to those with poor recovery. METHODS/STUDY POPULATION: Plasma was collected at 19 days post-stroke from 27 participants with mild-moderate upper extremity impairment enrolled in the Critical Periods After Stroke Study. MicroRNA expression was assessed using TaqMan microRNA assays (Thermo Fisher Scientific). Good recovery was defined as ≥6 point change in the Action Research Arm Test (ARAT) score from baseline to 6 months. Bioinformatics analysis compared the plasma microRNA expression profiles of participants with good Versus poor recovery. Candidate biomarkers were identified after correcting for multiple comparisons using a false discovery rate <0.05. RESULTS/ANTICIPATED RESULTS: Eleven microRNAs had significantly altered expression in the good (n=22) Versus poor (n=5) recovery groups, with 2 showing increased expression—miR-371-3p and miR-520g, and 9 showing decreased expression—miR-449b, miR-519b, miR-581, miR-616, miR-892b, miR-941, miR-1179, miR-1292, and miR1296. Three of these could be implicated in neural repair mechanisms. Elevated miR-371-3p levels increase the likelihood that pluripotent stem cells will differentiate into neural progenitors. MiR-892b decreases levels of amyloid precursor protein, which has been implicated as a regulator of synapse formation. Finally miR-941, the only known human-specific microRNA, downregulates the CSPα protein which is involved in neurotransmitter release. DISCUSSION/SIGNIFICANCE OF IMPACT: This preliminary study suggests that circulating microRNAs in the plasma may help serve as biomarkers of neural repair and aid in understanding human neural repair mechanisms. If validated in larger studies with appropriate controls, these markers could aid in timing rehabilitation therapy or designing recovery-based therapeutics.
20 - Optimized Cross-Study Analysis of Microarray-Based Predictors
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- By Xiaogang Zhong, Johns Hopkins University, Luigi Marchionni, Johns Hopkins University, Leslie Cope, Johns Hopkins University, Edwin S. Iversen, Duke University, Elizabeth S. Garrett-Mayer, Medical University of South Carolina, Edward Gabrielson, Johns Hopkins University, Giovanni Parmigiani, Harvard University
- Edited by Kim-Anh Do, Zhaohui Steve Qin, Emory University, Atlanta, Marina Vannucci, Rice University, Houston
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- Book:
- Advances in Statistical Bioinformatics
- Published online:
- 05 June 2013
- Print publication:
- 10 June 2013, pp 398-422
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Summary
Background
Genome-wide analyses, especially gene expression profiling using microarrays, have been extensively used in medical research and led to the identification of several molecular signatures involved in various aspects of human disease pathogenesis. Individual studies have typically investigated relatively small numbers of samples, making cross-study validation a crucial step for the scientific community. Combined use of gene expression data from public repositories has proved difficult due to inherent differences in microarray platforms, protocols used in independent laboratories, experimental designs, and annotations for both genes and samples. Several methodologies have been proposed to address these issues, depending on the experimental strategies and on the biological and clinical questions. When samples phenotypes are known, statistical methods that handle data sets separately and then apply gene-wise meta-analytic approaches have proven successful, allowing the identification of statistically relevant intersections of molecular signatures from different studies (Rhodes et al., 2002; Ghosh et al., 2003; Rhodes et al., 2004; Wang et al., 2004). Advanced multilevel models are now available for this task (Conlon et al., 2007; Scharpf et al., 2009). As an alternative, the assimilation of gene expression measurements, achieved by merging the data sets, has also been used to evaluate molecular signatures obtained from different studies (Sorlie et al., 2003; Hu et al., 2005; Kapp et al., 2006; Hayes et al., 2006). Finally, we previously developed a method to evaluate cross-platform consistency of expression patterns, using integrative correlation (ICOR).