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      Microarray analysis identifies coding and non-coding RNA markers of liver injury in whole body irradiated mice

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          Abstract

          Radiation injury from medical, accidental, or intentional sources can induce acute and long-term hepatic dysregulation, fibrosis, and cancer. This long-term hepatic dysregulation decreases quality of life and may lead to death. Our goal in this study is to determine acute changes in biological pathways and discover potential RNA biomarkers predictive of radiation injury. We performed whole transcriptome microarray analysis of mouse liver tissue (C57BL/6 J) 48 h after whole-body irradiation with 1, 2, 4, 8, and 12 Gray to identify significant expression changes in mRNAs, lncRNAs, and miRNAs, We also validated changes in specific RNAs through qRT-PCR. We used Ingenuity Pathway Analysis (IPA) to identify pathways associated with gene expression changes. We observed significant dysregulation of multiple mRNAs across all doses. In contrast, miRNA dysregulation was observed upwards of 2 Gray. The most significantly upregulated mRNAs function as tumor suppressors: Cdkn1a, Phlda3, and Eda2r. The most significantly downregulated mRNAs were involved in hemoglobin synthesis, inflammation, and mitochondrial function including multiple members of Hbb and Hba. The most significantly upregulated miRNA included: miR-34a-5p, miR-3102-5p, and miR-3960, while miR-342-3p, miR-142a-3p, and miR-223-3p were most significantly downregulated. IPA predicted activation of cell cycle checkpoint control pathways and inhibition of pathways relevant to inflammation and erythropoietin. Clarifying expression of mRNA, miRNA and lncRNA at a short time point (48 h) offers insight into potential biomarkers, including radiation markers shared across organs and animal models. This information, once validated in human models, can aid in development of bio-dosimetry biomarkers, and furthers our understanding of acute pathway dysregulation.

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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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            Predicting effective microRNA target sites in mammalian mRNAs

            MicroRNA targets are often recognized through pairing between the miRNA seed region and complementary sites within target mRNAs, but not all of these canonical sites are equally effective, and both computational and in vivo UV-crosslinking approaches suggest that many mRNAs are targeted through non-canonical interactions. Here, we show that recently reported non-canonical sites do not mediate repression despite binding the miRNA, which indicates that the vast majority of functional sites are canonical. Accordingly, we developed an improved quantitative model of canonical targeting, using a compendium of experimental datasets that we pre-processed to minimize confounding biases. This model, which considers site type and another 14 features to predict the most effectively targeted mRNAs, performed significantly better than existing models and was as informative as the best high-throughput in vivo crosslinking approaches. It drives the latest version of TargetScan (v7.0; targetscan.org), thereby providing a valuable resource for placing miRNAs into gene-regulatory networks. DOI: http://dx.doi.org/10.7554/eLife.05005.001
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              SLC transporters as therapeutic targets: emerging opportunities.

              Solute carrier (SLC) transporters - a family of more than 300 membrane-bound proteins that facilitate the transport of a wide array of substrates across biological membranes - have important roles in physiological processes ranging from the cellular uptake of nutrients to the absorption of drugs and other xenobiotics. Several classes of marketed drugs target well-known SLC transporters, such as neurotransmitter transporters, and human genetic studies have provided powerful insight into the roles of more-recently characterized SLC transporters in both rare and common diseases, indicating a wealth of new therapeutic opportunities. This Review summarizes knowledge on the roles of SLC transporters in human disease, describes strategies to target such transporters, and highlights current and investigational drugs that modulate SLC transporters, as well as promising drug targets.
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                Author and article information

                Contributors
                aryankalayilm@mail.nih.gov
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                5 January 2023
                5 January 2023
                2023
                : 13
                : 200
                Affiliations
                [1 ]GRID grid.48336.3a, ISNI 0000 0004 1936 8075, Radiation Oncology Branch, Center for Cancer Research, , National Cancer Institute, National Institutes of Health, ; 10 Center Drive, Room B3B406, Bethesda, MD 20892 USA
                [2 ]GRID grid.420517.5, ISNI 0000 0004 0490 0428, Gryphon Scientific, ; Takoma Park, MD 20912 USA
                [3 ]GRID grid.5386.8, ISNI 000000041936877X, Department of Radiation Oncology, Weill Cornell Medicine, ; New York, NY 10065 USA
                [4 ]GRID grid.48336.3a, ISNI 0000 0004 1936 8075, Radiation Research Program, , National Cancer Institute, National Institutes of Health, ; Rockville, MD 20850 USA
                Article
                26784
                10.1038/s41598-022-26784-w
                9814510
                36604457
                d27124a3-7104-48b5-9deb-2fc03a789bcb
                © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 2 May 2022
                : 20 December 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000060, National Institute of Allergy and Infectious Diseases;
                Award ID: NRC-13028
                Award ID: NRC-13028
                Award ID: NRC-13028
                Award ID: NRC-13028
                Award ID: Y2-OD-0332-01
                Award ID: NRC-13028
                Award ID: NRC-13028
                Award ID: NRC-13028
                Award ID: NRC-13028
                Award ID: NRC-13028
                Award ID: NRC-13028
                Award ID: NRC-13028
                Award Recipient :
                Categories
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                © The Author(s) 2023

                Uncategorized
                non-coding rnas,biomarkers,health care,risk factors
                Uncategorized
                non-coding rnas, biomarkers, health care, risk factors

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