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      Neuroprotection of Bone Marrow-Derived Mesenchymal Stem Cell-Derived Extracellular Vesicle-Enclosed miR-410 Correlates with HDAC4 Knockdown in Hypoxic-Ischemic Brain Damage

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      Neurochemical Research
      Springer Science and Business Media LLC

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          Abstract

          Evidence exists reporting that miR-410 may rescue neurological deficits, neuronal injury, and neuronal apoptosis after experimental hypoxic ischemia. This study aimed to explore the mechanism by which miR-410 transferred by bone marrow-derived mesenchymal stem cell-derived extracellular vesicles (BMSC-EVs) may alleviate hypoxic-ischemic brain damage (HIBD) in newborn mice. BMSCs were isolated from total bone marrow cells of femur and tibia of newborn mice, and primary neurons were extracted from the cerebral cortex of newborn mice within 24 h of birth. EVs were extracted from BMSCs transfected with the mimic or inhibitor of miR-410. Primary neurons were subjected to hypoxia and treated with overexpression (oe)-HDAC4, small interfering RNA (siRNA)-β-catenin, or Wnt pathway inhibitor and/or EV (miR-410 mimic) or EV (miR-410 inhibitor). A neonatal mouse HIBD model was established and treated with EVs. When BMSC-EVs were endocytosed by primary neurons, miR-410 was upregulated, neuronal viability was elevated, and apoptosis was inhibited. miR-410 in BMSC-EVs targeted HDAC4, thus increasing neuronal viability and reducing apoptosis. Conversely, overexpression of HDAC4 activated the Wnt pathway and enhanced the nuclear translocation of β-catenin. Treatment with miR-410-containing BMSC-EVs improved learning and memory abilities of HIBD mice while attenuating apoptosis by inactivating the Wnt pathway via targeting HDAC4. Taken together, the findings suggest that miR-410 delivered by BMSC-EVs alleviates HIBD by inhibiting HDAC4-dependent Wnt pathway activation.

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          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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              Cytoscape: a software environment for integrated models of biomolecular interaction networks.

              Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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                Author and article information

                Journal
                Neurochemical Research
                Neurochem Res
                Springer Science and Business Media LLC
                0364-3190
                1573-6903
                October 2022
                August 26 2022
                October 2022
                : 47
                : 10
                : 3150-3166
                Article
                10.1007/s11064-022-03670-5
                36028735
                4bba45fd-b529-4a7f-a414-73eb256fe294
                © 2022

                https://www.springer.com/tdm

                https://www.springer.com/tdm

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