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      Identification of osteoclast-osteoblast coupling factors in humans reveals links between bone and energy metabolism

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          Abstract

          Bone remodeling consists of resorption by osteoclasts followed by formation by osteoblasts, and osteoclasts are a source of bone formation-stimulating factors. Here we utilize osteoclast ablation by denosumab (DMAb) and RNA-sequencing of bone biopsies from postmenopausal women to identify osteoclast-secreted factors suppressed by DMAb. Based on these analyses, LIF, CREG2, CST3, CCBE1, and DPP4 are likely osteoclast-derived coupling factors in humans. Given the role of Dipeptidyl Peptidase-4 (DPP4) in glucose homeostasis, we further demonstrate that DMAb-treated participants have a significant reduction in circulating DPP4 and increase in Glucagon-like peptide (GLP)-1 levels as compared to the placebo-treated group, and also that type 2 diabetic patients treated with DMAb show significant reductions in HbA1c as compared to patients treated either with bisphosphonates or calcium and vitamin D. Thus, our results identify several coupling factors in humans and uncover osteoclast-derived DPP4 as a potential link between bone remodeling and energy metabolism.

          Abstract

          Anti-resorptive bone therapies also inhibit bone formation, as osteoclasts secrete factors that stimulate bone formation by osteoblasts. Here, the authors identify osteoclast-secreted factors that couple bone resorption to bone formation in healthy subjects, and show that osteoclast-derived DPP4 may be a factor coupling bone resorption to energy metabolism.

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          Most cited references67

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          edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

          Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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            featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

            Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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              RSeQC: quality control of RNA-seq experiments.

              RNA-seq has been extensively used for transcriptome study. Quality control (QC) is critical to ensure that RNA-seq data are of high quality and suitable for subsequent analyses. However, QC is a time-consuming and complex task, due to the massive size and versatile nature of RNA-seq data. Therefore, a convenient and comprehensive QC tool to assess RNA-seq quality is sorely needed. We developed the RSeQC package to comprehensively evaluate different aspects of RNA-seq experiments, such as sequence quality, GC bias, polymerase chain reaction bias, nucleotide composition bias, sequencing depth, strand specificity, coverage uniformity and read distribution over the genome structure. RSeQC takes both SAM and BAM files as input, which can be produced by most RNA-seq mapping tools as well as BED files, which are widely used for gene models. Most modules in RSeQC take advantage of R scripts for visualization, and they are notably efficient in dealing with large BAM/SAM files containing hundreds of millions of alignments. RSeQC is written in Python and C. Source code and a comprehensive user's manual are freely available at: http://code.google.com/p/rseqc/.
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                Author and article information

                Contributors
                Oursler.merryjo@mayo.edu
                khosla.sundeep@mayo.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                7 January 2020
                7 January 2020
                2020
                : 11
                : 87
                Affiliations
                [1 ]ISNI 0000 0004 0459 167X, GRID grid.66875.3a, Mayo Clinic College of Medicine and Science, ; Rochester, MN USA
                [2 ]ISNI 0000000086837370, GRID grid.214458.e, University of Michigan School of Dentistry, ; Ann Arbor, MI USA
                [3 ]GRID grid.240988.f, Tan Tock Seng Hospital, ; Singapore, Singapore
                [4 ]ISNI 0000 0001 0728 0170, GRID grid.10825.3e, University of Southern Denmark, ; Odense, Denmark
                [5 ]ISNI 0000 0004 0512 5013, GRID grid.7143.1, Odense University Hospital, ; Odense, Denmark
                Author information
                http://orcid.org/0000-0002-3179-6414
                http://orcid.org/0000-0002-7341-1535
                http://orcid.org/0000-0002-2936-4372
                Article
                14003
                10.1038/s41467-019-14003-6
                6946812
                31911667
                d85c9609-d65f-4c87-ab68-eefa41110f08
                © The Author(s) 2020

                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
                : 23 April 2019
                : 9 December 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000049, U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging);
                Award ID: AG004875
                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: AR027065
                Award ID: AR070281
                Award ID: AR068275
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | NIH | National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
                Funded by: U.S. Department of Health & Human Services | NIH | National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                bone,osteoporosis
                Uncategorized
                bone, osteoporosis

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