scholarly journals Transcriptional profiling reveals extraordinary diversity among skeletal muscle tissues

2017 ◽  
Author(s):  
Erin E Terry ◽  
Xiping Zhang ◽  
Christy Hoffmann ◽  
Laura D Hughes ◽  
Scott A Lewis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Skeletal Muscle ◽  
Mrna Expression ◽  
Skeletal Muscles ◽  
Contractile Function ◽  
Expression Profiles ◽  
Differentially Expressed ◽  
Data Set ◽  
Transcriptional Profiles ◽  
Muscle Tissues

AbstractSkeletal muscle comprises a family of diverse tissues with highly specialized morphology, function, and metabolism. Many acquired diseases – including HIV, COPD, cancer cachexia, critical illness myopathy, and sepsis – affect specific muscles while sparing others. Even monogenic muscular dystrophies tend to selectively affect certain muscle groups, despite their causative genetic mutations being present in all tissues. These observations suggest that factors intrinsic to muscle tissues influence their susceptibility to various disease mechanisms. Nevertheless, most studies have not addressed transcriptional diversity among skeletal muscles. Here we use RNA sequencing (RNA-seq) to profile global mRNA expression in a wide array of skeletal, smooth, and cardiac muscle tissues from mice and rats. Our data set, MuscleDB, reveals extensive transcriptional diversity, with greater than 50% of transcripts differentially expressed among skeletal muscle tissues. This diversity is only partly explained by fiber type composition and developmental history, suggesting that specialized transcriptional profiles establish the functional identity of muscle tissues. We find conservation in the transcriptional profiles across species as well as between males and females, indicating that these data may be useful in predicting gene expression in related species. Notably, thousands of differentially expressed genes in skeletal muscle are associated with human disease, and hundreds of these genes encode targets of drugs on the market today. We detect mRNA expression of hundreds of putative myokines that may underlie the endocrine functions of skeletal muscle. In addition to demonstrating the intrinsic diversity of skeletal muscles, these data provide a resource for generating testable hypotheses regarding the mechanisms that establish differential disease susceptibility in muscle.Significance StatementSkeletal muscles are a diverse family of tissues with a common contractile function but divergent morphology, development, and metabolism. One need only reflect on the different functions of limb muscles and the diaphragm to realize the highly specialized nature of these tissues. Nevertheless, every study of global gene expression has analyzed at most one representative skeletal muscle. Here we measure gene expression from 11 different skeletal muscles in mice and rats. We show that there is no such thing as a representative skeletal muscle, as gene expression profiles vary widely among the tissues analyzed. These data are an important resource for pharmacologists, tissue engineers, and investigators studying the mechanisms of cellular specialization.

scholarly journals Transcriptional profiling reveals extraordinary diversity among skeletal muscle tissues

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Erin E Terry ◽  
Xiping Zhang ◽  
Christy Hoffmann ◽  
Laura D Hughes ◽  
Scott A Lewis ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Cardiac Muscle ◽  
Mrna Expression ◽  
Skeletal Muscles ◽  
Transcriptional Profiling ◽  
Muscular Dystrophies ◽  
Data Set ◽  
Muscle Tissues ◽  
Muscle Groups ◽  
Intrinsic Diversity

Skeletal muscle comprises a family of diverse tissues with highly specialized functions. Many acquired diseases, including HIV and COPD, affect specific muscles while sparing others. Even monogenic muscular dystrophies selectively affect certain muscle groups. These observations suggest that factors intrinsic to muscle tissues influence their resistance to disease. Nevertheless, most studies have not addressed transcriptional diversity among skeletal muscles. Here we use RNAseq to profile mRNA expression in skeletal, smooth, and cardiac muscle tissues from mice and rats. Our data set, MuscleDB, reveals extensive transcriptional diversity, with greater than 50% of transcripts differentially expressed among skeletal muscle tissues. We detect mRNA expression of hundreds of putative myokines that may underlie the endocrine functions of skeletal muscle. We identify candidate genes that may drive tissue specialization, including Smarca4, Vegfa, and Myostatin. By demonstrating the intrinsic diversity of skeletal muscles, these data provide a resource for studying the mechanisms of tissue specialization.

scholarly journals Gene expression profiling of skeletal muscles

2021 ◽  
Author(s):  
Sarah I. Alto ◽  
Chih-Ning Chang ◽  
Kevin Brown ◽  
Chrissa Kioussi ◽  
Theresa M. Filtz
Keyword(s):  
Gene Expression ◽  
Skeletal Muscle ◽  
Differentially Expressed Genes ◽  
Tibialis Anterior ◽  
Skeletal Muscles ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Differentially Expressed ◽  
Analysis Tool

AbstractSoleus and tibialis anterior are two well-characterized skeletal muscles commonly utilized in skeletal muscle-related studies. Next-generation sequencing provides an opportunity for an in-depth biocomputational analysis to identify the gene expression patterns between soleus and tibialis anterior and analyze those genes’ functions based on past literature. This study acquired the gene expression profiles from soleus and tibialis anterior murine skeletal muscle biopsies via RNA-sequencing. Read counts were processed through edgeR’s differential gene expression analysis. Differentially expressed genes were filtered down using a false discovery rate less than 0.05c, a fold-change value larger than twenty, and an association with overrepresented pathways based on the Reactome pathway over-representation analysis tool. Most of the differentially expressed genes associated with soleus encoded for components of lipid metabolism and unique contractile elements. Differentially expressed genes associated with tibialis anterior encoded mostly for glucose and glycogen metabolic pathways’ regulatory enzymes and calcium-sensitive contractile components. These gene expression distinctions partly explain the genetic basis for muscle specialization and may help to explain skeletal muscle susceptibility to disease and drugs and refine tissue engineering approaches.

scholarly journals Integrative transcriptome data mining for identification of core lncRNAs in breast cancer

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7821 ◽  
Author(s):  
Xiaoming Zhang ◽  
Jing Zhuang ◽  
Lijuan Liu ◽  
Zhengguo He ◽  
Cun Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Prognostic Value ◽  
Early Stage ◽  
Expression Profiles ◽  
Diagnostic Value ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Data Set

Background Cumulative evidence suggests that long non-coding RNAs (lncRNAs) play an important role in tumorigenesis. This study aims to identify lncRNAs that can serve as new biomarkers for breast cancer diagnosis or screening. Methods First, the linear fitting method was used to identify differentially expressed genes from the breast cancer RNA expression profiles in The Cancer Genome Atlas (TCGA). Next, the diagnostic value of all differentially expressed lncRNAs was evaluated using a receiver operating characteristic (ROC) curve. Then, the top ten lncRNAs with the highest diagnostic value were selected as core genes for clinical characteristics and prognosis analysis. Furthermore, core lncRNA-mRNA co-expression networks based on weighted gene co-expression network analysis (WGCNA) were constructed, and functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The differential expression level and diagnostic value of core lncRNAs were further evaluated by using independent data set from Gene Expression Omnibus (GEO). Finally, the expression status and prognostic value of core lncRNAs in various tumors were analyzed based on Gene Expression Profiling Interactive Analysis (GEPIA). Results Seven core lncRNAs (LINC00478, PGM5-AS1, AL035610.1, MIR143HG, RP11-175K6.1, AC005550.4, and MIR497HG) have good single-factor diagnostic value for breast cancer. AC093850.2 has a prognostic value for breast cancer. AC005550.4 and MIR497HG can better distinguish breast cancer patients in early-stage from the advanced-stage. Low expression of MAGI2-AS3, LINC00478, AL035610.1, MIR143HG, and MIR145 may be associated with lymph node metastasis in breast cancer. Conclusion Our study provides candidate biomarkers for the diagnosis and prognosis of breast cancer, as well as a bioinformatics basis for the further elucidation of the molecular pathological mechanism of breast cancer.

PADGE: analysis of heterogeneous patterns of differential gene expression

2007 ◽  
Vol 32 (1) ◽  
pp. 154-159 ◽  
Author(s):  
Li Li ◽  
Amitabha Chaudhuri ◽  
John Chant ◽  
Zhijun Tang
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Web Application ◽  
Statistical Power ◽  
Profile Analysis ◽  
Expression Profiles ◽  
Differentially Expressed ◽  
Cancer Genes ◽  
Data Set ◽  
Differential Gene

We have devised a novel analysis approach, percentile analysis for differential gene expression (PADGE), for identifying genes differentially expressed between two groups of heterogeneous samples. PADGE was designed to compare expression profiles of sample subgroups at a series of percentile cutoffs and to examine the trend of relative expression between sample groups as expression level increases. Simulation studies showed that PADGE has more statistical power than t-statistics, cancer outlier profile analysis (COPA) (Tomlins SA, Rhodes DR, Perner S, Dhanasekaran SM, Mehra R, Sun XW, Varambally S, Cao X, Tchinda J, Kuefer R, Lee C, Montie JE, Shah RB, Pienta KJ, Rubin MA, Chinnaiyan AM. Science 310: 644–648, 2005), and kurtosis (Teschendorff AE, Naderi A, Barbosa-Morais NL, Caldas C. Bioinformatics 22: 2269–2275, 2006). Application of PADGE to microarray data sets in tumor tissues demonstrated its utility in prioritizing cancer genes encoding potential therapeutic targets or diagnostic markers. A web application was developed for researchers to analyze a large gene expression data set from heterogeneous biological samples and identify differentially expressed genes between subsets of sample classes using PADGE and other available approaches. Availability: http://www.cgl.ucsf.edu/Research/genentech/padge/ .

LncRNA-mRNA Expression Profiles and Functional Networks Associated with Cognitive Impairment in Folate-deficient Mice

2021 ◽  
Vol 24 ◽  
Author(s):  
Xiaojin Feng ◽  
Fenfang Zhan ◽  
Jialing Hu ◽  
Fuzhou Hua ◽  
Guohai Xu
Keyword(s):  
Gene Expression ◽  
Cognitive Impairment ◽  
Mrna Expression ◽  
Expression Profiles ◽  
Differentially Expressed ◽  
Functional Networks ◽  
Ppi Network ◽  
Hub Genes ◽  
Cerna Network ◽  
Deficient Mice

Background: Cognitive impairment is a common neurocognitive disorder that affects millions of worldwide people’s health,related tofolate deficiency. Objective: The present study aimed to investigate the lncRNA-mRNA functional networks associated with cognitive impairment in folate-deficient mice and elucidate their possible molecular mechanisms. Methods: We downloaded the gene expression profile (GSE148126) of lncRNAs and mRNAs from NCBI Gene Expression Omnibus (GEO) database. Four groups of mouse hippocampi were analyzed, including 4 months (4mo) and 18 months (18mo) of folic acid (FA) deficiency/supplementation. The differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) were identified using gplots and heatmap packages. The functions of the DEmRNAs were evaluated using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The hub genes wereidentified by CytoHubba plugins of Cytoscape, and protein-protein interaction (PPI) network of deregulated mRNAs was performed using STRING database. Finally, lncRNA-mRNA co-expression and competitive endogenous RNA (ceRNA) network analyses were constructed. Results: In total, we screened 67 lncRNAs with 211 mRNAs, and 89 lncRNAs with 229 mRNAs were differentially expressed in 4mo_FAand 18mo_FA deficient mice, respectively. GO analyses indicated that DEmRNAs were highly related to terms involved in binding and biological regulation. KEGG pathway analyses demonstrated that these genes were significantly enriched for Renin secretion, Pancreatic secretion and AMPK signaling pathways in 18mo_FA deficiency group. Subsequently, the top 5 hub genes werescreened from the PPI network, which may be key genes with the progression of folate deficiency. Upon the lncRNA-mRNA co-expression network analysis, we identified the top 10 lncRNAs having the maximum number of connections with related mRNAs. Finally, a ceRNA network was constructed for DE lncRNAs and DEmRNAs, and several pivotal miRNAs were predicted. Conclusions: This study identified the lncRNA-mRNA expression profiles and functional networks associated with cognitive impairment in folate-deficient mice, which provided support for the possible mechanisms and therapy for this disease.

Differential global gene expression in red and white skeletal muscle

2001 ◽  
Vol 280 (4) ◽  
pp. C763-C768 ◽  
Author(s):  
W. G. Campbell ◽  
S. E. Gordon ◽  
C. J. Carlson ◽  
J. S. Pattison ◽  
M. T. Hamilton ◽  
...  
Keyword(s):  
Gene Expression ◽  
Skeletal Muscle ◽  
Mrna Expression ◽  
Global Gene Expression ◽  
Differentially Expressed ◽  
Fiber Types ◽  
Signaling Proteins ◽  
Red Muscle ◽  
Mrna Expression Analysis ◽  
Muscle Types

The differences in gene expression among the fiber types of skeletal muscle have long fascinated scientists, but for the most part, previous experiments have only reported differences of one or two genes at a time. The evolving technology of global mRNA expression analysis was employed to determine the potential differential expression of ∼3,000 mRNAs between the white quad (white muscle) and the red soleus muscle (mixed red muscle) of female ICR mice (30–35 g). Microarray analysis identified 49 mRNA sequences that were differentially expressed between white and mixed red skeletal muscle, including newly identified differential expressions between muscle types. For example, the current findings increase the number of known, differentially expressed mRNAs for transcription factors/coregulators by nine and signaling proteins by three. The expanding knowledge of the diversity of mRNA expression between white and mixed red muscle suggests that there could be quite a complex regulation of phenotype between muscles of different fiber types.

scholarly journals Renin-angiotensin-aldosterone system inhibitors improve membrane stability and change gene-expression profiles in dystrophic skeletal muscles

2017 ◽  
Vol 312 (2) ◽  
pp. C155-C168 ◽  
Author(s):  
Jessica A. Chadwick ◽  
Sayak Bhattacharya ◽  
Jeovanna Lowe ◽  
Noah Weisleder ◽  
Jill A. Rafael-Fortney
Keyword(s):  
Gene Expression ◽  
Skeletal Muscle ◽  
Skeletal Muscles ◽  
Expression Profiles ◽  
Membrane Integrity ◽  
Gene Expression Profiles ◽  
Renin Angiotensin Aldosterone System ◽  
Raas Inhibitors ◽  
Dystrophic Mice ◽  
Inhibitor Treatment

Angiotensin-converting enzyme inhibitors (ACEi) and mineralocorticoid receptor (MR) antagonists are FDA-approved drugs that inhibit the renin-angiotensin-aldosterone system (RAAS) and are used to treat heart failure. Combined treatment with the ACEi lisinopril and the nonspecific MR antagonist spironolactone surprisingly improves skeletal muscle, in addition to heart function and pathology in a Duchenne muscular dystrophy (DMD) mouse model. We recently demonstrated that MR is present in all limb and respiratory muscles and functions as a steroid hormone receptor in differentiated normal human skeletal muscle fibers. The goals of the current study were to begin to define cellular and molecular mechanisms mediating the skeletal muscle efficacy of RAAS inhibitor treatment. We also compared molecular changes resulting from RAAS inhibition with those resulting from the current DMD standard-of-care glucocorticoid treatment. Direct assessment of muscle membrane integrity demonstrated improvement in dystrophic mice treated with lisinopril and spironolactone compared with untreated mice. Short-term treatments of dystrophic mice with specific and nonspecific MR antagonists combined with lisinopril led to overlapping gene-expression profiles with beneficial regulation of metabolic processes and decreased inflammatory gene expression. Glucocorticoids increased apoptotic, proteolytic, and chemokine gene expression that was not changed by RAAS inhibitors in dystrophic mice. Microarray data identified potential genes that may underlie RAAS inhibitor treatment efficacy and the side effects of glucocorticoids. Direct effects of RAAS inhibitors on membrane integrity also contribute to improved pathology of dystrophic muscles. Together, these data will inform clinical development of MR antagonists for treating skeletal muscles in DMD.

scholarly journals Differences in gene expression profiles for subcutaneous adipose, liver, and skeletal muscle tissues between Meishan and Landrace pigs with different backfat thicknesses

PLoS ONE ◽  
2018 ◽  
Vol 13 (9) ◽  
pp. e0204135 ◽  
Author(s):  
Misaki Kojima ◽  
Ikuyo Nakajima ◽  
Aisaku Arakawa ◽  
Satoshi Mikawa ◽  
Toshimi Matsumoto ◽  
...  
Keyword(s):  
Gene Expression ◽  
Skeletal Muscle ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Muscle Tissues ◽  
Subcutaneous Adipose

scholarly journals Transcriptional and Epigenetic Changes Influencing Skeletal Muscle Metabolism in Women With Polycystic Ovary Syndrome

2018 ◽  
Vol 103 (12) ◽  
pp. 4465-4477 ◽  
Author(s):  
Emma Nilsson ◽  
Anna Benrick ◽  
Milana Kokosar ◽  
Anna Krook ◽  
Eva Lindgren ◽  
...  
Keyword(s):  
Gene Expression ◽  
Insulin Resistance ◽  
Skeletal Muscle ◽  
Dna Methylation ◽  
Polycystic Ovary Syndrome ◽  
Mrna Expression ◽  
Polycystic Ovary ◽  
Differentially Expressed ◽  
Ovary Syndrome ◽  
Cpg Sites

Abstract Context Insulin resistance in skeletal muscle is a major risk factor for the development of type 2 diabetes in women with polycystic ovary syndrome (PCOS). Despite this, the mechanisms underlying insulin resistance in PCOS are largely unknown. Objective To investigate the genome-wide DNA methylation and gene expression patterns in skeletal muscle from women with PCOS and controls and relate them to phenotypic variations. Design/Participants In a case-control study, skeletal muscle biopsies from women with PCOS (n = 17) and age-, weight-, and body mass index‒matched controls (n = 14) were analyzed by array-based DNA methylation and mRNA expression profiling. Results Eighty-five unique transcripts were differentially expressed in muscle from women with PCOS vs controls, including DYRK1A, SYNPO2, SCP2, and NAMPT. Furthermore, women with PCOS had reduced expression of genes involved in immune system pathways. Two CpG sites showed differential DNA methylation after correction for multiple testing. However, an mRNA expression of ∼30% of the differentially expressed genes correlated with DNA methylation levels of CpG sites in or near the gene. Functional follow-up studies demonstrated that KLF10 is under transcriptional control of insulin, where insulin promotes glycogen accumulation in myotubes of human muscle cells. Testosterone downregulates the expression levels of COL1A1 and MAP2K6. Conclusion PCOS is associated with aberrant skeletal muscle gene expression with dysregulated pathways. Furthermore, we identified specific changes in muscle DNA methylation that may affect gene expression. This study showed that women with PCOS have epigenetic and transcriptional changes in skeletal muscle that, in part, can explain the metabolic abnormalities seen in these women.

scholarly journals Gene expression effects of glucocorticoid and mineralocorticoid receptor agonists and antagonists on normal human skeletal muscle

2017 ◽  
Vol 49 (6) ◽  
pp. 277-286 ◽  
Author(s):  
Jessica A. Chadwick ◽  
J. Spencer Hauck ◽  
Celso E. Gomez-Sanchez ◽  
Elise P. Gomez-Sanchez ◽  
Jill A. Rafael-Fortney
Keyword(s):  
Gene Expression ◽  
Skeletal Muscle ◽  
Duchenne Muscular Dystrophy ◽  
Muscular Dystrophy ◽  
Skeletal Muscles ◽  
Human Skeletal Muscle ◽  
Expression Profiles ◽  
Combined Treatment ◽  
Gene Expression Profiles ◽  
Steroid Hormone Receptors

Mineralocorticoid and glucocorticoid receptors are closely related steroid hormone receptors that regulate gene expression through many of the same hormone response elements. However, their transcriptional activities and effects in skeletal muscles are largely unknown. We recently identified mineralocorticoid receptors (MR) in skeletal muscles after finding that combined treatment with the angiotensin-converting enzyme inhibitor lisinopril and MR antagonist spironolactone was therapeutic in Duchenne muscular dystrophy mouse models. The glucocorticoid receptor (GR) agonist prednisolone is the current standard-of-care treatment for Duchenne muscular dystrophy because it prolongs ambulation, likely due to its anti-inflammatory effects. However, data on whether glucocorticoids have a beneficial or detrimental direct effect on skeletal muscle are controversial. Here, we begin to define the gene expression profiles in normal differentiated human skeletal muscle myotubes treated with MR and GR agonists and antagonists. The MR agonist aldosterone and GR agonist prednisolone had highly overlapping gene expression profiles, supporting the notion that prednisolone acts as both a GR and MR agonist that may have detrimental effects on skeletal muscles. Co-incubations with aldosterone plus either nonspecific or selective MR antagonists, spironolactone or eplerenone, resulted in similar numbers of gene expression changes, suggesting that both drugs can block MR activation to a similar extent. Eplerenone treatment alone decreased a number of important muscle-specific genes. This information may be used to develop biomarkers to monitor clinical efficacy of MR antagonists or GR agonists in muscular dystrophy, develop a temporally coordinated treatment with both drugs, or identify novel therapeutics with more specific downstream targets.

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