Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,884
datasets available to search
ShareScore release 0.9.0
Dataset results
1,884 results for “skeletal muscle”
Comparative profiling of skeletal muscle models reveals heterogeneity of transcriptome and metabolism
<p>This dataset is a complement to the following publication: Ahmed M. Abdelmoez, Laura Sardón Puig, Jonathon AB. Smith, Brendan M. Gabriel, Mladen Savikj, Lucile Dollet, Alexander V. Chibalin, Anna Krook, Juleen R. Zierath, and Nicolas J. Pillon. <a href="https://doi.org/10.1152/ajpcell.00540.2019">Comparative profiling of skeletal muscle models reveals heterogeneity of transcriptome and metabolism. </a>Am J Physiol Cell Physiol. 2019 Dec 11.</p> <p>METHODS: Publicly available data from myotubes and skeletal muscle tissues were selected from the GEO database. Raw files were downloaded and robust multi array (RMA) normalization was performed in unison for all samples from the same platform. For each human ENSEMBL, the rat and mouse orthologs were found using the R package BioMart and the arrays were merged based on the human ENSEMBL annotation. The database was then aggregated according to the official human gene symbol. When multiple ENSEMBL were found for a single gene symbol, an average was calculated.</p>
Alterations in RNA editing in skeletal muscle following exercise training in individuals with Parkinson's disease
<p>Parkinson’s Disease (PD) is the second most common neurodegenerative disease behind Alzheimer’s Disease, currently affecting more than 10 million people worldwide. The progression of PD results in the loss of function due to neurodegeneration and neuroinflammation. The etiology of PD is multifactorial, including both genetic and environmental origins. We explored changes in RNA editing, specifically editing through the actions of the Adenosine Deaminases Acting on RNA (ADARs), in the progression of PD. Analysis of ADAR editing of skeletal muscle transcriptomes from PD patients and controls, including those that engaged in a rehabilitative exercise training program revealed significant differences in ADAR editing patterns based on age, disease status, and following rehabilitative exercise. Further, deleterious editing events in protein coding regions were identified in multiple genes with known associations to PD pathogenesis. Our findings of differential ADAR editing complement findings of changes in transcriptional network identified by a recent Lavin et al. 2020 (<a href="https://doi.org/10.3389/fphys.2020.00653">https://doi.org/10.3389/fphys.2020.00653)</a> study and offer insights into dynamic ADAR editing changes associated with PD pathogenesis. VCF files were generated using AIDD (Plonski et al., 2020) (<a href="https://doi.org/10.1186/s12859-020-03888-6">https://doi.org/10.1186/s12859-020-03888-6</a>).</p>
Population-scale skeletal muscle single-nucleus multi-omic profiling reveals extensive context specific genetic regulation
<p>Data accompanying the manuscript "Population-scale skeletal muscle single-nucleus multi-omic profiling reveals extensive context specific genetic regulation".</p> <p>Note: For ATAC fragment files, e,caQTL full cis scan summary files, clustering objects, please see the CMDGA portal (https://cmdga.org/search/?searchTerm=stephen-parker%3AVarshney2024)<br>For raw data including fastq files, please see dbGaP repo phs001048.v3.p1</p> <p>Data in this repository includes:</p> <p>Filename: Description</p> <p>1. list of 8,666 genes for which exon-only counts were considered. See methods section "Adjusting RNA counts for overlapping gene annotations" in the manuscript.</p> <p>2. nucleus_sample_cluster_map.tsv: nucleus-sample-cluster map with other QC info. <br># index: nucleus identified syntax <modality>.<batch>.NM.<10X channel>.<barcode> <br># UMAP_1, UMAP_2: UMAP coordinates for visualization<br># modality: rna or atac<br># batch: processing batch identifier<br># hqaa_umi: high quality autosomal alignments (HQAA) for atac nuclei, unique molecular identifier (UMI) for tna <br># fraction_mitochondrial: fraction of reads mapping to the mitochondrial genome<br># cohort: sample cohort<br># tss_enrichment: TSS enrichment for atac nuclei<br># coarse_cluster_name: cluster name</p> <p>3. peaks.tar.gz: snATAC peak features including:<br># consensus-summits.bed: consensus summits along with the cell type that the summits was highest in.<br># narrow peaks in clusters<br># consensus summit feature (summit +- 150bp) identified in each cluster - these were used in GWAS enrichments.</p> <p>4. snrna-cell-type-specific-genes.tsv: Normalized expression scores for genes in each cell-type cluster</p> <p>5. eqtl_permute.tar.gz: Permutation scan eQTL in each cell-type cluster. Columns: <br># variant: syntax <chrom>:<hg38 pos>:<ref>:<alt><br># effect_allele: effect allele (was the alt allele)<br># other_allele: non-effect allele<br># feature: gene name<br># featureCoordinates_tss: gene TSS<br># p-value: nominal p value<br># beta: slope/beta of the linear regression. Keyed on the alt allele<br># se: standard error of the slope<br># snp: SNP ID<br># strand: gene strand<br># n_variants_tested: number of variants tested for the gene<br># distance_var_pheno: distance of the variant with the gene TSS<br># n_effective_tests: number of effective tests<br># p_beta: beta distribution adjusted p value<br># qvalue: qvalue (Storey)</p> <p>6. caqtl_permute.tar.gz: # Permutation scan caQTL in each cell-type cluster. Columns: <br># variant: syntax <chrom>:<hg38 pos>:<ref>:<alt><br># effect_allele: effect allele (was the alt allele)<br># other_allele: non-effect allele<br># feature: peak feature coordinates<br># p-value: nominal p value<br># beta: slope/beta of the linear regression. Keyed on the alt allele<br># se: standard error of the slope<br># snp: SNP ID<br># n_variants_tested: number of variants tested for the gene<br># distance_var_pheno: distance of the variant with the gene TSS<br># n_effective_tests: number of effective tests<br># p_beta: beta distribution adjusted p value<br># qvalue: qvalue (Storey)</p> <p>7. eqtl_credible_sets.tar.gz: # eQTL credible set. The file name denotes the egene and the signal hit id. Bed file columns: <br># 1: snp chromosome<br># 2: snp start<br># 3: snp end<br># 4: snp chrom_pos_ref_alt<br># 5: Bayes Factor <br># 6: PIP<br># 7: SNP rsid</p> <p>8. caqtl_credible_sets.tar.gz: # caqtl credible set. The file name denotes the capeak and the signal hit id. Bed file columns: <br># 1: snp chromosome<br># 2: snp start<br># 3: snp end<br># 4: snp chrom_pos_ref_alt<br># 5: Bayes Factor <br># 6: PIP<br># 7: SNP rsid</p> <p>9. cicero_all.tar.gz # Cicero coaccessibility results. Columns<br># Peak 1: Macs2 narrowpeak coordinate for peak 1<br># Peak 2: Macs2 narrowpeak coordinate for peak 2<br># coaccess: Cicero coaccessibility score</p> <p>10. cicero_gene_tss.tar.gz: Cicero coaccessibility results between peak and genes. Macs2 narrow peaks in the TSS+1kb upstream region are assigned that gene name. Columns<br># Cicero coaccessibility results between peak and genes. Macs2 narrow peaks in the TSS+1kb upstream region are assigned that gene name.Columns<br># Peak 1: Macs2 narrowpeak coordinate for peak 1<br># gene_name: Assigned gene<br># Peak 2: Macs2 narrowpeak coordinate for peak 2<br># coaccess: Cicero coaccessibility score<br>## Peak1 is the narrowpeak in the TSS region, peak2 is the distal peak</p> <p>11. mash.tar.gz Mashr results for e/caQTL - lfsr, posterior means and posterior SD for each tested eSNP-eGene, caSNP-caPeak pair. </p> <p>12. cellregmap.tar.gz: Cellregmap results for endothelial nucleus-level eQTL scans.<br>## Persistent genetic effect beta_g was calculated in a simple association model. <br>## An interaction model was fit to test for GxC effect. columns:<br># rho1, g2, e1, and eps2 are variance component measures outputs from CellRegMap corresponding to interaction, genetic, environment and residual variance components. <br># p_nominal: nominal p from cellRegMap<br># kind: model kind in CellRegMap - simple association or interaction<br># beta_g: Persistent genetic effect<br># gene_name: gene name for eQTL or peak feature name for caQTL<br># context: context used either factors (continuous) or subclusters (discrete)<br># snp: index snp for which model is fit. This is the most significant identified snp from our standard e,caQTL scans. chrom-hg38pos-rsid</p> <p><br>13. coloc-eqtl-caqtl.tsv: # Summary of eQTL-caQTL coloc in each cluster. Columns:<br># nsnps: Number of SNPs in the region<br># eqtl_hit: SNP with the highest Bayes factor in the SuSiE eQTL credible set<br># caqtl_hit: SNP with the highest Bayes factor in the SuSiE caQTL credible set<br># PP.H0.abf: Coloc posterior probability for no signal<br># PP.H1.abf: Coloc posterior probability for signal in dataset 1<br># PP.H2.abf: Coloc posterior probability for signal in dataset 2<br># PP.H3.abf: Coloc posterior probability for different signals in datasets 1 and 2<br># PP.H4.abf: Coloc posterior probability for shared signal in datasets 1 and 2<br># idx1: Index of the SuSiE credible set for dataset 1<br># idx2: Index of the SuSiE credible set for dataset 2<br># cluster: cluster name<br># egene: eGene name<br># capeak: caPeak coordinates</p> <p>14. cit-mrs-summary.tsv: Summary from CIT and MR Steiger directionality tests. Columns:<br># cluster: cluster name<br># egene: eGene name<br># capeak: caPeak coordinates<br># eqhit: SNP with the highest Bayes factor in the SuSiE eQTL credible set<br># cahit: SNP with the highest Bayes factor in the SuSiE caQTL credible set<br># p.cit_c_c-e: P value for CIT causal cahit-ca-to-e model<br># q.cit_c_c-e: q value for CIT causal cahit-ca-to-e model<br># p.cit_rc_c-e: P value for CIT reverse-causal eqhit-ca-to-e model <br># q.cit_rc_c-e: value for CIT reverse-causal eqhit-ca-to-e model <br># p.cit_c_e-c: P value for CIT causal eqhit-e-to-ca model<br># q.cit_c_e-c: q value for CIT causal eqhit-e-to-ca model<br># p.cit_rc_e-c: P value for CIT reverse-causal cahit-e-to-ca model <br># q.cit_rc_e-c: q value for CIT reverse-causal cahit-e-to-ca model <br># cit_direction: Direction inferred from CIT <br># correct_causal_direction--ca-to-e: MR Steiger directionality test - is ca-to-e direction correct?<br># correct_causal_direction--e-to-ca: MR Steiger directionality test - is e-to-ca direction correct?<br># sensitivity_ratio--ca-to-e: MR Steiger Sensitivity ratio for ca-to-e model <br># sensitivity_ratio--e-to-ca: MR Steiger Sensitivity ratio for e-to-ca model<br># steiger_test--ca-to-e: MR Steiger directionality test P value for ca-to-e model<br># steiger_test--e-to-ca: MR Steiger directionality test P value for e-to-ca model<br># steiger_q--ca-to-e: MR Steiger directionality test q value for ca-to-e model<br># steiger_q--e-to-ca: MR Steiger directionality test q value for e-to-ca model<br># mrs_direction: Direction inferred from MR Steiger<br># direction: Direction inferred requiring consistent results between CIT and MR Steiger directionality test</p> <p>15. coloc-gwas-eqtl.tsv and<br>16. coloc-gwas-caqtl.tsv # Summary of e/caQTL coloc with GWAS in each cluster. Columns:<br># nsnps: Number of SNPs in the region<br># gwas_hit: SNP with the highest bayes factor in the SuSiE GWAS credible set<br># eqtl_hit: SNP with the highest bayes factor in the SuSiE eQTL credible set<br># caqtl_hit: SNP with the highest bayes factor in the SuSiE caQTL credible set<br># PP.H0.abf: Coloc posterior probability for no signal<br># PP.H1.abf: Coloc posterior probability for signal in dataset 1<br># PP.H2.abf: Coloc posterior probability for signal in dataset 2<br># PP.H3.abf: Coloc posterior probability for different signal in datasets 1 and 2<br># PP.H4.abf: Coloc posterior probability for shared signal in datasets 1 and 2<br># idx1: Index of the SuSiE credible set for dataset 1<br># idx2: Index of the SuSiE credible set for dataset 2<br># cluster: cluster name<br># egene: eGene name<br># capeak: caPeak coordinates<br># p12min: Min prior p12 where the PP H4 > 0.5. Lower this value, more robust is the colocalization<br># trait: GWAS trait name<br># gwas_locus: GWAS locus name for the coloc test - a 250kb left and right flanking genomic window on this SNP was considered for testing coloc between all pairs of GWAS/QTL signals identified in this region <br># traitname: Expanded GWAS trait name<br># variable_type: GWAS type <br># source: Source of GWAS - either UKBB or other study</p> <p>17. supplementary_tables.xlsx: Supplementary tables from the manuscript.<br>Information included in sheets:<br>1. "marker_genes": Marker genes known from literature used to annotate clusters<br>2. "n_nuclei": n pass-QC nuclei per modality-sample-cluster</p> <p>2. "snrna_GO_enrichment": GO term enrichment: matrix of cluster vs top 2 GO terms</p> <p>3. "qtl_scan_info": e/caQTL scan info<br>cluster: cluster<br>ntested_eqtl: N genes tested for eQTL<br>nsig_eqtl: N significant (5% FDR) eGenes<br>n_pheno_pcs_eqtl: N phenotype PCs considered for eQTL<br>ratio_eqtl: Ratio of N eGenes/N genes tested<br>nsig_caqtl: N peaks tested for caQTL<br>ntested_caqtl: N significant (5% FDR) caPeaks<br>n_pheno_pcs_caqtl: N phenotype PCs considered for caQTL<br>ratio_caqtl: Ratio of N caPeaks/N peaks tested<br>nsamples_eqtl: N samples for eQTL<br>nsamples_caqtl: N samples for caQTL</p> <p>4. "gwas_trait_list": GWAS trait info<br>trait: GWAS trait ID<br>traitname: GWAS trait description<br>variable_type: GWAS type. case/control (cc), continuous_irnt=continuous inverse-normal transformed<br>source: GWAS source<br>doi: GWAS study DOI</p> <p>5. "traits_in_ldsc_baseline" - list of annotations included in the baseline model for LDSC</p> <p>6. "gwas_enrichment_in_peaks" GWAS enrichment in cluster peaks (S-LDSC)</p> <p>7. "gwas_enrichment_in_qtl_peaks" GWAS enrichment in QTL peaks (fGWAS) # fGWAS results comparing GWAS enrichment in type 1 annotations<br>CI_lower_ln, estimate_ln, CI_upper_ln: natural log of lower confidence interval, estimate, and upper confidence interval<br>trait: trait id<br>traitname: trait name<br>annotation: annotation<br>sig: 1 if CIs don't overlap 0, otherwise 0</p> <p>8. t2d_gwas_caqtl_coloc and<br>9. t2d_gwas_eqtl_coloc:<br>Summary of e,caQTL coloc with T2D GWAS in each cluster, along with target gene nominations. Columns:<br>nsnps: Number of SNPs in the region<br>gwas_hit: SNP with the highest bayes factor in the SuSiE GWAS credible set<br>eqtl_hit: SNP with the highest bayes factor in the SuSiE eQTL credible set<br>caqtl_hit: SNP with the highest bayes factor in the SuSiE caQTL credible set<br>PP.H0.abf: Coloc posterior probability for no signal<br>PP.H1.abf: Coloc posterior probability for signal in dataset 1<br>PP.H2.abf: Coloc posterior probability for signal in dataset 2<br>PP.H3.abf: Coloc posterior probability for different signal in datasets 1 and 2<br>PP.H4.abf: Coloc posterior probability for shared signal in datasets 1 and 2<br>idx1: Index of the SuSiE credible set for dataset 1<br>idx2: Index of the SuSiE credible set for dataset 2<br>cluster: cluster name<br>egene: eGene name<br>capeak: caPeak coordinates<br>p12min: Min prior p12 where the PP H4 > 0.5. Lower this value, more robust is the colocalization<br>trait: GWAS trait id<br>diamante_gwas_locus: GWAS signal from the DIAMANTE 2018 study. Some signals that our SuSiE runs identified were not present in the original study in which case this column is NA<br>traitname: Expanded GWAS trait name<br>capeak_in_tss: caPeak in TSS + 1kb upstream region of a gene<br>gene_target_standard_cicero: caPeak coaccessible with TSS peak of a gene considering nuclei from all samples for co-accessibility<br>gene_target_allelic_cicero: caPeak coaccessible with TSS peak of a gene considering nuclei from samples homozygous for the caSNP allele associated with increased accessibility<br>gwashit_nominal_egene: gwas_hit nominally associated with these genes nominated in the columns capeak_in_tss, gene_target_standard_cicero, and gene_target_allelic_cicero</p> <p>10. MPRA results for the C2CD4A locus</p>
Dataset related to article "SMA-miRs (miR-181a-5p, -324-5p, and -451a) are overexpressed in spinal muscular atrophy skeletal muscle and serum samples"
<p>mice survival after treatment with anti-miR-181a-5p; mice weight after treatment with anti-miR-324-5p</p>
Harmonization of experimental procedures to assess mitochondrial respiration in human permeabilized skeletal muscle fibers
<p>DatLab files of the experiments included in the "Harmonization of experimental procedures to assess mitochondrial respiration in human permeabilized skeletal muscle fibers" manuscript (<a href="https://doi.org/10.1016/j.freeradbiomed.2024.07.039" target="_blank" rel="noopener">https://doi.org/10.1016/j.freeradbiomed.2024.07.039</a>).</p>
Advanced Glycation End-products are Retained in Decellularized Muscle Matrix Derived from Aged Skeletal Muscle
<p>Advanced glycation end-products (AGEs) accrue on skeletal muscle collagen in old age, stiffening the matrix and increasing inflammation. Whether decellularized biomaterials derived from aged muscle would suffer from increased AGE cross-links is unknown. We hypothesized that DMM from old muscle would have increased collagen, collagen cross-linking, stiffness, and AGEs. We isolated, decellularized, and characterized gastrocnemii of 1-month, 2-month, and 20-month old C57BlJ6 mice to determine age-dependent changes to collagen in muscle and decellularized muscle matrix (DMM). Total hydroxyproline and soluble hydroxyproline after proteinase K digestion were measured to assay collagen levels and cross-linking, respectively. Muscle fiber and DMM stiffness was determined using atomic force microscopy (AFM). AGE ELISAs were used to test AGE levels, and the effect of AGE cross-link breaker ALT-711 on DMM was tested. We determined age-dependent increases in collagen amount, cross-linking, and general stiffness are retained on DMM. DMM from old muscle was stiffer compared to younger groups according to shifts in AFM modulus distribution. We measured an increase in AGE-specific cross-links with old age in whole muscle and observed that these changes are countered in DMM by AGE cross-link breaker ALT-711. Future study investigating and countering the biological effects of old age on DMM is warranted.</p>
Prenatal skeletogenesis partially recovers from absent skeletal muscle as development progresses
<p>Raw data (measurements made) for paper entitled "Prenatal skeletogenesis partially recovers from absent skeletal muscle as development progresses"</p>
Data from: Electroacupuncture mimics exercise-induced changes in skeletal muscle gene expression in women with polycystic ovary syndrome
<p class="1stparatext">Context: Autonomic nervous system activation mediates the increase in whole-body glucose uptake in response to electroacupuncture but the mechanisms are largely unknown.</p> <p class="1stparatext">Objective: To identify the molecular mechanisms underlying electroacupuncture-induced glucose uptake in skeletal muscle in insulin-resistant overweight/obese women with and without polycystic ovary syndrome (PCOS).</p> <p class="1stparatext">Design/Participants: In a case-control study, skeletal muscle biopsies were collected from 15 women with PCOS and 14 controls before and after electroacupuncture. Gene expression and methylation was analyzed using Illumina BeadChips arrays.</p> <p class="1stparatext">Results: A single bout of electroacupuncture restores metabolic and transcriptional alterations and induces epigenetic changes in skeletal muscle. Transcriptomic analysis revealed 180 unique genes (<i>q </i>< 0.05) whose expression was changed by electroacupuncture, with 95% of the changes towards a healthier phenotype. We identified DNA methylation changes at 304 unique sites (<i>q </i>< 0.20), and these changes correlated with altered expression of 101 genes (<i>p</i> < 0.05). Among the 50 most upregulated genes in response to electroacupuncture, 38% were also upregulated in response to<b> </b>exercise. We identified a subset of genes that were selectively altered by electroacupuncture in women with PCOS. For example, <i>MSX1 </i>and <i>SRNX1 </i>were decreased in muscle tissue of women with PCOS and were increased by electroacupuncture and exercise. siRNA-mediated silencing of these two genes in cultured myotubes decreased glycogen synthesis, supporting a role for these genes in glucose homeostasis.</p> <p class="1stparatext">Conclusion: Our findings provide evidence that electroacupuncture normalizes gene expression in skeletal muscle in a manner similar to acute exercise. Electroacupuncture might therefore be a useful way of assisting those who have difficulties performing exercise.</p>
Dissecting muscle power output: Evidence of multi-scale power amplification in skeletal muscle
<p class="MsoNormal">Many animals use a combination of skeletal muscle and elastic structures to amplify power output for fast motions. Among vertebrates, tendons in series with skeletal muscle are often implicated as the primary power-amplifying spring, but muscles contain elastic structures at all levels of organization, from the muscle tendon to the extracellular matrix to elastic proteins within sarcomeres. The present study used <em>ex vivo</em> muscle preparations in combination with high-speed video to quantify power output, as the product of force and velocity, at several levels of muscle organization to determine where power amplification occurs. Dynamic ramp shortening contractions in isolated frog flexor digitorum superficialis brevis were compared with isotonic power output to identify power amplification within muscle fibers, the muscle belly, free tendon and elements external to the muscle tendon. Energy accounting revealed that artifacts from compliant structures outside of the muscle–tendon unit contributed significant peak instantaneous power. This compliance included deflection of clamped bone that stored and released energy contributing 195.22±33.19 W kg<sup>−1</sup> (mean±s.e.m.) to the peak power output. In addition, we found that power detected from within the muscle fascicles for dynamic shortening ramps was 338.78 ±16.03 W kg<sup>−1</sup>, or nearly twice the maximum isotonic power output of 195.23±8.82 W kg<sup>−1</sup>. Measurements of muscle belly and muscle–tendon unit also demonstrated significant power amplification. These data suggest that intramuscular tissues, as well as bone, have the capacity to store and release energy to amplify whole-muscle power output.</p>
Drp1 controls Complex II assembly and skeletal muscle metabolism by Sdhaf2 action on mitochondria
<p>The Dynamin-related GTPase, Drp1 (encoded by <em>Dnm1l</em>) plays a central role in mitochondrial fission and is requisite for numerous cellular processes however its role in muscle metabolism remains unclear. Herein, we show that among human tissues, the highest number of gene correlations with <em>DNM1L </em> are in skeletal muscle. Knockdown of Drp1 (Drp1-KD) promoted mitochondrial hyperfusion in the muscle of male mice. Reduced fatty acid oxidation and impaired insulin action along with increased muscle succinate was observed in Drp1-KD muscle. Muscle Drp1-KD reduced Complex II assembly and activity as a consequence of diminished mitochondrial translocation of succinate dehydrogenase assembly factor 2 (Sdhaf2). Restoration of Sdhaf2 normalized Complex II activity, lipid oxidation, and insulin action in Drp1-KD myocytes. Drp1 is critical in maintaining mitochondrial Complex II assembly, lipid oxidation, and insulin sensitivity, suggesting a mechanistic link between mitochondrial morphology and skeletal muscle metabolism, which is clinically relevant in combatting metabolic-related diseases.</p>
Effect of long-term physical disability and aging on extracellular matrix biogenesis in human skeletal muscle
<p>Supplemental Table for a study <strong>Effect of long-term physical disability and aging on extracellular matrix biogenesis in human skeletal muscle</strong></p>
DIFFERENTIAL REGULATION OF MYOFIBRILLAR PROTEINS IN SKELETAL MUSCLES OF SEPTIC MICE
<p>Supplemental Table S1</p> <p>Supplemental Table S2</p> <p>Supplemental Figure S1</p> <p>Supplemental Figure S2</p> <p>Supplemental Figure S3</p> <p>Supplemental Figure S4</p>
FGF-2-dependent signaling activated in aged human skeletal muscle promotes intramuscular adipogenesis
<p><span><span><span><span><span><span><span><span><span><span><span>Aged skeletal muscle is markedly affected by fatty muscle infiltration and strategies to reduce the occurrence of intramuscular adipocytes are urgently needed. Here, we show that fibroblast growth factor-2 (FGF-2) not only stimulates muscle growth, but also promotes intramuscular adipogenesis. Using multiple screening assays upstream and downstream of microRNA (miR)-29a signaling, we located the secreted protein and adipogenic inhibitor SPARC to an FGF-2 signaling pathway that is conserved between skeletal muscle cells from mice and humans and that is activated in skeletal muscle of aged mice and humans. FGF-2 induces the miR-29a/SPARC axis through transcriptional activation of FRA-1, which binds and activates an evolutionary conserved AP-1 site element proximal in the miR-29a promoter. Genetic deletions in muscle cells and AAV-mediated overexpression of FGF-2 or SPARC in mouse skeletal muscle revealed that this axis regulates differentiation of fibro/adipogenic progenitors <i>in vitro</i> and intramuscular adipose tissue (IMAT) formation <i>in vivo</i>. Skeletal muscle from human donors aged > 75 years versus < 55 years showed activation of FGF-2-dependent signaling and increased IMAT. Thus, our data highlights a disparate role of FGF-2 in adult skeletal muscle and reveals a novel pathway to combat fat accumulation in aged human skeletal muscle.</span></span></span></span></span></span></span></span></span></span></span></p>
Large-scale integration of single-cell transcriptomic data captures transitional progenitor states in mouse skeletal muscle regeneration
<p>Skeletal muscle repair is driven by the coordinated self-renewal and fusion of myogenic stem and progenitor cells. Single-cell gene expression analyses of myogenesis have been hampered by the poor sampling of rare and transient cell states that are critical for muscle repair, and do not inform the spatial context that is important for myogenic differentiation. Here, we demonstrate how large-scale integration of single-cell and spatial transcriptomic data can overcome these limitations. We created a single-cell transcriptomic dataset of mouse skeletal muscle by integration, consensus annotation, and analysis of 23 newly collected scRNAseq datasets and 88 publicly available single-cell (scRNAseq) and single-nucleus (snRNAseq) RNA-sequencing datasets. The resulting dataset includes more than 365,000 cells and spans a wide range of ages, injury, and repair conditions. Together, these data enabled identification of the predominant cell types in skeletal muscle, and resolved cell subtypes, including endothelial subtypes distinguished by vessel-type of origin, fibro/adipogenic progenitors defined by functional roles, and many distinct immune populations. The representation of different experimental conditions and the depth of transcriptome coverage enabled robust profiling of sparsely expressed genes. We built a densely sampled transcriptomic model of myogenesis, from stem cell quiescence to myofiber maturation and identified rare, transitional states of progenitor commitment and fusion that are poorly represented in individual datasets. We performed spatial RNA sequencing of mouse muscle at three time points after injury and used the integrated dataset as a reference to achieve a high-resolution, local deconvolution of cell subtypes. We also used the integrated dataset to explore ligand-receptor co-expression patterns and identify dynamic cell-cell interactions in muscle injury response. We provide a public web tool to enable interactive exploration and visualization of the data. Our work supports the utility of large-scale integration of single-cell transcriptomic data as a tool for biological discovery.</p>
Warburg effect characterizes the skeletal muscle in Fabry Disease: experimental evidence and clinical implications
<p>Skeletal muscle (SM) pain and fatigue are common in Fabry disease (FD), and strongly impact the patient’s quality of life. Still, the SM in FD is poorly investigated. Although energetic alterations are reported in cells from FD patients, they have never been related to fatigue and pain. Given the pivotal relevance of energetics for SM health, we undertook the investigation of the SM. We consistently observed a reduced tolerance to aerobic activity and lactate accumulation in FD-humanized mouse model and patients. Accordingly, in sedentary mouse FD SM we detected an increase in fast/glycolytic-fibers, mirrored by an upregulation of glycolytic enzymes and glucose-transporters. In fibroblasts derived from FD patients, we confirmed a high glycolytic-rate and accordingly, metabolomic/lipidomic-analysis revealed that lipids are underutilized as energetic fuel in FD-patients. In the quest for a tentative mechanism, we explored analogies with genetic myopathies, that show altered expression of energetic metabolism. Specifically, we explored HIF-1 upregulation and found it increased in FD mice and patients. From a screening of miRNAs associated with metabolic stress, we found the significant upregulation of miR-17, which has been previously associated with HIF-1 up-regulation. A specific antagomir targeting miR-17 was able to inhibit HIF-1 accumulation and revert metabolic-remodeling in FD-cells. Our findings unveil an anaerobic glycolytic-switch under normoxia, known as Warburg Effect, which is induced by miR-17-mediated-upregulation of HIF-1. The miR-17/HIF-1 pathway can be a new therapeutic target in FD, and Exercise-testing and blood lactate may become a new diagnostic and monitoring-tool in FD.</p>
Cell selectivity in succinate receptor SUCNR1/GPR91 signaling in skeletal muscle
<p>Succinate is released by skeletal muscle during exercise and activates <em>SUCNR1</em>/GPR91. Signaling of SUCNR1 is involved in cell-cell communication in skeletal muscle. However, the specific cell types responding to succinate and the directionality of communication are unclear. <em>De novo</em> analysis of transcriptomic datasets demonstrated that <em>SUCNR1</em> mRNA is expressed in immune, adipose, and liver tissues, but scarce in skeletal muscle. In human tissues, <em>SUCNR1</em> mRNA was associated with macrophage markers. Single-cell RNA sequencing and fluorescent RNAscope demonstrated that in human skeletal muscle, <em>SUCNR1</em> mRNA is not expressed in muscle fibers but coincided with macrophage populations. Human M2-polarized macrophages exhibit high levels of <em>SUCNR1</em> mRNA and stimulation with selective agonists of SUCNR1 triggered Gq- and Gi-coupled signaling. Primary human skeletal muscle cells were unresponsive to <em>SUCNR1</em> agonists. In conclusion, SUCNR1 is not expressed in muscle cells and its role in the adaptive response of skeletal muscle to exercise is most likely mediated via paracrine mechanisms involving M2-like macrophages within the muscle.</p>
Effect of burosumab on muscle function and strength, and rates of ATP synthesis in skeletal muscle in adults with X-linked Hypophosphatemia
<p><strong>Abstract</strong></p> <p><strong>Context: </strong>Burosumab, a neutralizing antibody to FGF23, is approved for the treatment of X-linked hypophosphatemia. In clinical trials burosumab improves symptoms of pain, fatigue and stiffness and improves performance on certain muscle function studies.</p> <p><strong>Objective: </strong>Determine if burosumab would increase ATP synthesis in skeletal muscle of treatment-naïve adults with XLH and if so whether that correlated with improved muscle function.</p> <p><strong>Methods: </strong>Ten symptomatic adults, who had not received any treatment for XLH for years, had ATP synthesis rates assessed in the soleus/gastrocnemius muscle complex of the right calf using the <sup>31</sup>P magnetic resonance spectroscopy saturation transfer technique. Baseline muscle function tests and symptoms of pain, fatigue, stiffness and lower extremity joint pain were quantified. All participants were treated with burosumab, 1 mg/kg every four weeks for 12 weeks. ATP synthesis rates and muscle function tests were repeated 2-weeks (“peak”) and 4-weeks (“trough”) after the third dose of burosumab.</p> <p><strong>Results: </strong>Pain, fatigue, stiffness and lower extremity joint pain all improved with treatment. Performance on the 6-Minute Walk and Sit to Stand tests also improved significantly. Performance on the Timed Up and Go test did not significantly improve (p = 0.057). Muscle strength, measured by dynamometry, did not change significantly in either the upper or lower extremities during the study. ATP synthesis rates did not change over the three months of study in the group as whole. In a sub-analysis comparing individuals whose performances on the 6-Minute Walk Test and Sit to Stand tests were at or better than the mean outcome for those tests, to those whose outcomes were below the mean, no difference was observed in the rate of change in ATP synthesis rates. Despite profound and prolonged hypophosphatemia at baseline, intracellular muscle concentrations of phosphorus were normal.</p> <p><strong>Conclusion: </strong>The improvement in the 6-Minute Walk Test and Sit to Stand tests without any observed change in either upper or lower extremity muscle strength or ATP synthesis rates, suggests that improvement in pain, fatigue and stiffness may explain, at least in part, the improved performance on these two tests. The preserved intracellular phosphate levels suggests that adaptive mechanisms are present in skeletal muscle that insulate intracellular phosphorus from life-long FGF23-mediaed hypophosphatemia.</p>
Effects of Nebivolol on Skeletal Muscle During Exercise in Hypertensive Patients
ClinicalTrials.gov study NCT01501929. IPD Sharing: Not stated. Countries: 1. Publications: 24.
Impact of Protein and Alkali Supplementation on Skeletal Muscle in Older Adults
ClinicalTrials.gov study NCT04048616. IPD Sharing: NO. Countries: 1. Publications: 0.
Strength Training for Skeletal Muscle Adaptation After Stroke
ClinicalTrials.gov study NCT00827827. IPD Sharing: NO. Countries: 1. Publications: 2.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.