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      Reversal of the diabetic bone signature with anabolic therapies in mice

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          Abstract

          The mechanisms underlying the bone disease induced by diabetes are complex and not fully understood; and antiresorptive agents, the current standard of care, do not restore the weakened bone architecture. Herein, we reveal the diabetic bone signature in mice at the tissue, cell, and transcriptome levels and demonstrate that three FDA-approved bone-anabolic agents correct it. Diabetes decreased bone mineral density (BMD) and bone formation, damaged microarchitecture, increased porosity of cortical bone, and compromised bone strength. Teriparatide (PTH), abaloparatide (ABL), and romosozumab/anti-sclerostin antibody (Scl-Ab) all restored BMD and corrected the deteriorated bone architecture. Mechanistically, PTH and more potently ABL induced similar responses at the tissue and gene signature levels, increasing both formation and resorption with positive balance towards bone gain. In contrast, Scl-Ab increased formation but decreased resorption. All agents restored bone architecture, corrected cortical porosity, and improved mechanical properties of diabetic bone; and ABL and Scl-Ab increased toughness, a fracture resistance index. Remarkably, all agents increased bone strength over the healthy controls even in the presence of severe hyperglycemia. These findings demonstrate the therapeutic value of bone anabolic agents to treat diabetes-induced bone disease and suggest the need for revisiting the approaches for the treatment of bone fragility in diabetes.

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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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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              BEDTools: a flexible suite of utilities for comparing genomic features

              Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                TMBellido@uams.edu
                Journal
                Bone Res
                Bone Res
                Bone Research
                Nature Publishing Group UK (London )
                2095-4700
                2095-6231
                19 April 2023
                19 April 2023
                2023
                : 11
                : 19
                Affiliations
                [1 ]GRID grid.241054.6, ISNI 0000 0004 4687 1637, Department of Physiology and Cell Biology, , University of Arkansas for Medical Sciences, ; Little Rock, AR USA
                [2 ]GRID grid.413916.8, ISNI 0000 0004 0419 1545, Central Arkansas Veterans Healthcare System, John L. McClellan Little Rock, ; Little Rock, AR USA
                [3 ]GRID grid.241054.6, ISNI 0000 0004 4687 1637, Department of Biostatistics, , University of Arkansas for Medical Sciences, ; Little Rock, AR USA
                [4 ]GRID grid.241054.6, ISNI 0000 0004 4687 1637, Department of Biomedical Informatics, , University of Arkansas for Medical Sciences, ; Little Rock, AR USA
                [5 ]GRID grid.241054.6, ISNI 0000 0004 4687 1637, Winthrop P. Rockefeller Cancer Institute, , University of Arkansas for Medical Sciences, ; Little Rock, AR USA
                Author information
                http://orcid.org/0000-0002-3588-1579
                http://orcid.org/0000-0002-8203-7004
                Article
                261
                10.1038/s41413-023-00261-0
                10115794
                37076478
                2850d22a-36cf-4473-aa84-9fa86dfd7b51
                © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 10 January 2023
                : 1 March 2023
                : 22 March 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000738, U.S. Department of Veterans Affairs (Department of Veterans Affairs);
                Award ID: VA-IK6BX004596
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000069, U.S. Department of Health & Human Services | NIH | National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS);
                Award ID: R01-AR059357
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100001422, American Society of Hematology (ASH);
                Award ID: Junior Faculty Scholar Award
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100006108, U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences (NCATS);
                Award ID: KL2TR003108
                Award ID: UL1TR003107
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences (NCATS)
                Categories
                Article
                Custom metadata
                © The Author(s) 2023

                bone quality and biomechanics,pathogenesis
                bone quality and biomechanics, pathogenesis

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