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      Skeletal muscle extracellular matrix remodeling with worsening glycemic control in nonhuman primates

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          Abstract

          Type 2 diabetes (T2D) development may be mediated by skeletal muscle (SkM) function, which is responsible for >80% of circulating glucose uptake. The goals of this study were to assess changes in global- and location-level gene expression, remodeling proteins, fibrosis, and vascularity of SkM with worsening glycemic control, through RNA sequencing, immunoblotting, and immunostaining. We evaluated SkM samples from health-diverse African green monkeys ( Cholorcebus aethiops sabaeus) to investigate these relationships. We assessed SkM remodeling at the molecular level by evaluating unbiased transcriptomics in age-, sex-, weight-, and waist circumference-matched metabolically healthy, prediabetic (PreT2D) and T2D monkeys ( n = 13). Our analysis applied novel location-specific gene differences and shows that extracellular facing and cell membrane-associated genes and proteins are highly upregulated in metabolic disease. We verified transcript patterns using immunohistochemical staining and protein analyses of matrix metalloproteinase 16 (MMP16), tissue inhibitor of metalloproteinase 2 (TIMP2), and VEGF. Extracellular matrix (ECM) functions to support intercellular communications, including the coupling of capillaries to muscle cells, which was worsened with increasing blood glucose. Multiple regression modeling from age- and health-diverse monkeys ( n = 33) revealed that capillary density was negatively predicted by only fasting blood glucose. The loss of vascularity in SkM co-occurred with reduced expression of hypoxia-sensing genes, which is indicative of a disconnect between altered ECM and reduced endothelial cells, and known perfusion deficiencies present in PreT2D and T2D. This report supports that rising blood glucose values incite ECM remodeling and reduce SkM capillarization, and that targeting ECM would be a rational approach to improve health with metabolic disease.

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          Most cited references61

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          Is Open Access

          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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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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              Normalization of RNA-seq data using factor analysis of control genes or samples.

              Normalization of RNA-sequencing (RNA-seq) data has proven essential to ensure accurate inference of expression levels. Here, we show that usual normalization approaches mostly account for sequencing depth and fail to correct for library preparation and other more complex unwanted technical effects. We evaluate the performance of the External RNA Control Consortium (ERCC) spike-in controls and investigate the possibility of using them directly for normalization. We show that the spike-ins are not reliable enough to be used in standard global-scaling or regression-based normalization procedures. We propose a normalization strategy, called remove unwanted variation (RUV), that adjusts for nuisance technical effects by performing factor analysis on suitable sets of control genes (e.g., ERCC spike-ins) or samples (e.g., replicate libraries). Our approach leads to more accurate estimates of expression fold-changes and tests of differential expression compared to state-of-the-art normalization methods. In particular, RUV promises to be valuable for large collaborative projects involving multiple laboratories, technicians, and/or sequencing platforms.
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                Author and article information

                Contributors
                Journal
                American Journal of Physiology-Regulatory, Integrative and Comparative Physiology
                American Journal of Physiology-Regulatory, Integrative and Comparative Physiology
                American Physiological Society
                0363-6119
                1522-1490
                March 01 2021
                March 01 2021
                : 320
                : 3
                : R226-R235
                Affiliations
                [1 ]Department of Pathology, Wake Forest University School of Medicine, Winston-Salem, North Carolina
                [2 ]Department of Hypertension, Wake Forest University School of Medicine, Winston-Salem, North Carolina
                [3 ]Center for Precision Medicine, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina
                [4 ]Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, North Carolina
                [5 ]Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, North Carolina
                [6 ]College of Health and Medicine, University of Tasmania, Hobart, Tasmania, Australia
                Article
                10.1152/ajpregu.00240.2020
                33206559
                0b991918-9851-4fdf-994f-2e97d15f373c
                © 2021
                History

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