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351 results for “muscle regulation”
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>
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>
Up-regulation of sarcoplasmic reticulum function protects skeletal muscle against cytoplasmic calcium overload during hibernation in ground squirrels
<p>We investigated the potential mechanism of the SR in maintenance of calcium (Ca<sup>2+</sup>) homeostasis of slow-twitch muscle (soleus, SOL), fast-twitch muscle (extensor digitorum longus, EDL) and mixed muscle (gastrocnemius, GAS) in hibernating ground squirrels (<i>Spermophilus dauricus</i>). Results showed that cytosolic and SR Ca<sup>2+</sup> concentrations in distinct skeletal muscle fibers increased and decreased during late torpor, respectively, but both returned to summer-active levels during early torpor. Ryanodine receptor1 (RyR1) and sarco/endoplasmic reticulum Ca<sup>2+</sup> ATPase isoform 1 (SERCA1) protein expression increased during hibernation. Up-regulation factors of SERCA activity: Phospholamban phosphorylation increased in the SOL and GAS, β-adrenergic receptor-2 protein expression increased in the GAS, and calmodulin kinase-2 phosphorylation increased in the SOL during hibernation. Down-regulation factors of SERCA activity: Sarcolipin and SERCA1 co-localization decreased in the EDL and GAS. These data suggest that SERCA activity in skeletal muscle fibers increases likely during hibernation. FKBP12/calsequestrin1 (negative regulatory factors of RyR1) and RyR1 co-localization decreased in the GAS, indicating that the RyR1 channel opening probability increased during hibernation. Dihydropyridine receptors protein expression and its co-localization with RYR1 decreased during hibernation prompts that the contractility of skeletal muscle was weakened. Protein expression of Ca<sup>2+</sup>-binding proteins calsequestrin1 and calmodulin increased indicating that the ability of intracellular free calcium binding increased during whole hibernation period. These findings confirm that the release, uptake, and binding of free Ca<sup>2+</sup> in the SR were enhanced in different skeletal muscles during hibernation. Up-regulation of muscular sarcoplasmic reticulum function protects skeletal muscle fibers against cytoplasmic calcium overload during hibernation in ground squirrels.We investigated the potential mechanism of the SR in maintenance of calcium (Ca<sup>2+</sup>) homeostasis of slow-twitch muscle (soleus, SOL), fast-twitch muscle (extensor digitorum longus, EDL) and mixed muscle (gastrocnemius, GAS) in hibernating ground squirrels (<i>Spermophilus dauricus</i>). Results showed that cytosolic and SR Ca<sup>2+</sup> concentrations in distinct skeletal muscle fibers increased and decreased during late torpor, respectively, but both returned to summer-active levels during early torpor. Ryanodine receptor1 (RyR1) and sarco/endoplasmic reticulum Ca<sup>2+</sup> ATPase isoform 1 (SERCA1) protein expression increased during hibernation. Up-regulation factors of SERCA activity: Phospholamban phosphorylation increased in the SOL and GAS, β-adrenergic receptor-2 protein expression increased in the GAS, and calmodulin kinase-2 phosphorylation increased in the SOL during hibernation. Down-regulation factors of SERCA activity: Sarcolipin and SERCA1 co-localization decreased in the EDL and GAS. These data suggest that SERCA activity in skeletal muscle fibers increases likely during hibernation. FKBP12/calsequestrin1 (negative regulatory factors of RyR1) and RyR1 co-localization decreased in the GAS, indicating that the RyR1 channel opening probability increased during hibernation. Dihydropyridine receptors protein expression and its co-localization with RYR1 decreased during hibernation prompts that the contractility of skeletal muscle was weakened. Protein expression of Ca<sup>2+</sup>-binding proteins calsequestrin1 and calmodulin increased indicating that the ability of intracellular free calcium binding increased during whole hibernation period. These findings confirm that the release, uptake, and binding of free Ca<sup>2+</sup> in the SR were enhanced in different skeletal muscles during hibernation. Up-regulation of muscular sarcoplasmic reticulum function protects skeletal muscle fibers against cytoplasmic calcium overload during hibernation in ground squirrels.</p>
Nutrient Regulation of Amino Acid Transporters in Aging Human Skeletal Muscle
ClinicalTrials.gov study NCT01669590. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Redox Regulation of Satellite Cells and Skeletal Muscle Healing
ClinicalTrials.gov study NCT03711838. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Role of Interleukin-1 in the Regulation of Muscle Derived Interleukin-6 During Exercise
ClinicalTrials.gov study NCT01771445. IPD Sharing: Not stated. Countries: 1. Publications: 1.
HORMA: Hormonal Regulators of Muscle and Metabolism in Aging
ClinicalTrials.gov study NCT00183040. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Effects of Negative Energy Balance on Muscle Mass Regulation
ClinicalTrials.gov study NCT01105143. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Nutritional and Contractile Regulation of Muscle Growth
ClinicalTrials.gov study NCT00891696. IPD Sharing: NO. Countries: 1. Publications: 48.
Up-regulation of sarcoplasmic reticulum function protects skeletal muscle against cytoplasmic calcium overload during hibernation in ground squirrels
Open the record for dataset details and reuse information.
Regulation of Muscle Protein Phenotype in Humans With Obesity
ClinicalTrials.gov study NCT04700800. IPD Sharing: NO. Countries: 1. Publications: 0.
LncRNA ENST00000581911 regulates extraocular muscle remodeling by interacting with KHSRP in Graves’ ophthalmopathy
GEO Series GSE278934. Homo sapiens. 10 samples. Type: Non-coding RNA profiling by array.
Transcriptome analysis of post-hatch breast muscle in legacy and modern broiler chickens reveals enrichment of several regulators of myogenic growth
GEO Series GSE65217. Gallus gallus. 23 samples. Type: Expression profiling by high throughput sequencing.
The Skeletal Muscle Molecular Clock Regulates Sarcomere Length Through Titin Splicing [2]
GEO Series GSE189863. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Hairless regulates heterochromatin maintenance and muscle stem cell function as a histone demethylase antagonist [RNA-seq]
GEO Series GSE132189. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Muscle Ring Finger 1 (MuRF1) and MuRF2 are Necessary but Functionally Redundant During Developmental Cardiac Growth and Regulate E2F1-Mediated Gene Expression In Vivo
GEO Series GSE14512. Mus musculus. 16 samples. Type: Expression profiling by array.
Negative Elongation Factor (NELF) regulation of muscle progenitor expansion assessed via Precision Run-On Sequencing (PRO-seq)
GEO Series GSE149766. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing; Other.
High intensity aerobic exercise suppresses cancer growth by regulating skeletal muscle-derived oncogenes and tumor suppressors (Heart)
GEO Series GSE191283. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
HIF1α Mediates Circadian Regulation of Skeletal Muscle Metabolism and Substrate Preference in Response to Time-of-Day Exercise
GEO Series GSE282641. Mus musculus. 64 samples. Type: Expression profiling by high throughput sequencing.
Yap regulates fatty acid oxidation in skeletal muscle [RNA-seq]
GEO Series GSE144135. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
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