MuscleDBs

Introduction

Skeletal muscles have indispensable functions in human body and also possess prominent regenerative ability. The rapid emergence of Next Generation Sequencing (NGS) data in recent years offers us an unprecedented perspective to understand gene regulatory networks governing skeletal muscle development and regeneration. However, the data from public NGS database are often in raw data format or processed with different procedures, causing obstacles to make full use of them (Yuan et al., 2019) [1]. Herein, we have integrated all information about current databases developed to represent disparate and heterogeneous omics data (with a focus on transcriptomics data) generated for skeletal muscle in different species.

Databases

MuscleDB

The Hughes (UMSL) and Esser (Univ. of Kentucky School of Medicine) labs are assembling a database of muscle tissue gene expression in mice and rat. They profiled global gene expression using RNA-sequencing from different muscle tissues, including 8 unique skeletal muscle tissues. In this repository, authors are developing a web-based platform to explore, visualize, and share these data build on a Shiny dashboard. This data set, MuscleDB, reveals extensive transcriptional diversity, with greater than 50% of transcripts differentially expressed among skeletal muscle tissues. Developers detected mRNA expression of hundreds of putative myokines that may underlie the endocrine functions of skeletal muscle. Authors were able to identify candidate genes that may drive tissue specialization, including Smarca4,Vegfa, and Myostatin (Terry et al., 2018 [2]). This resource allow investigators to perform analyses such as generating muscle-specific Cre-recombinase mouse strains for genetically manipulating specific muscle groups. Most importantly, these data provides the foundation for computational modeling of transcription factor networks, a method authors believe will uncover the genetic mechanisms that establish and maintain muscle specialization.

GeneXX

GeneXX has been developed as a new web-based resource to facilitate exploration of skeletal muscle gene responses to exercise (Reibe et al., 2018 [3]). Users can enter any human gene of interest, (e.g., PPARGC1A) and immediately observe log-fold change values, adjusted P values (q value), and the time point post exercise at which the transcript was measured, with color- and shape-coded symbols to indicate statistical significance and sex of participants, respectively. Also included are PubMed scores and a short summary about the gene of interest from the NCBI gene site. The main feature of geneXX is that it provides an accessible and instant insight into the response of a particular gene of interest to exercise in human skeletal muscle. To demonstrate its utility, authors carried out a meta-analysis on the included data sets and show transcript changes in skeletal muscle that persist regardless of sex, exercise mode, and duration, some of which have had minimal attention in the context of exercise. To enable visualization of all data embedded in the new results tables, on a single gene basis, a Shiny web app was created

SKmDB

SKmDB is an integrated skeletal muscle NGS database allowing users to explore overall data organization, to obtain information including gene expression, co-expression subnetwork, lincRNA catalog, enhancer profile, and hotspot regions by querying a gene name or a specific region, and to visualize the data of interest. To compile all the available datasets for the field of skeletal muscle, authors searched keywords related to skeletal muscle and myogenesis in Roadmap, ENCODE, GEO database and collected 11 types of NGS data (CLIP-seq, miRNA-seq, small RNAseq, single cell RNA-seq, RNA-seq, ChIP-seq, AIMS-seq, DNase-seq, ATAC-seq, MNase-seq and Bisulfite-seq) corresponding to 16 mouse and 13 human cell types as well as 9 mouse and 20 human tissues (Figure 1). To search for typical enhancers or super enhancers, SKmDB enables querying for gene associated enhancer regions as well as tissue or cell type-specific enhancers that fall into the queried genome region.

Figure 1 from (Yuan et al., 2019) [1]. Data overview page of SKmDB.

MGS resource

Muscle Gene Sets (MGS) resource is available for [www.sys-myo.com/muscle_gene_sets download] and is accessible through three commonly used functional genomics platforms (GSEA, EnrichR, and WebGestalt). The MGS is a collection of gene sets extracted from expression studies of skeletal muscle cells and tissues, and a smaller number of cardiac studies. These relate to various aspects of muscle molecular physiology and pathology, including myopathies, cardiomyopathies, metabolism, exercise, ageing, development, regeneration, and others. The MGS resource can be used to investigate the behavior of any list of genes across previous comparisons of muscle conditions, to compare previous studies to one another, and to explore the functional relationship of muscle dysregulation to the Gene Ontology. Its major intended use is in enrichment testing for functional genomics analysis (Malatras et al., 2019 [4]).

NeuroMuscleDB

The resulting database NeuroMuscleDB is the result of a wide literature survey, database searches, and data curation. NeuroMuscleDB contains information of genes in Homo sapiens, Mus musculus, and Bos Taurus, and their promoter sequences and specified roles at different stages of muscle development and in associated myopathies. The database contains information on ~ 1102 genes, 6030 mRNAs, and 5687 proteins, and embedded analytical tools that can be used to perform tasks related to gene sequence usage. The database carries a short description of genes (location, start and end position of genomic accession, etc.), GeneID, number of exons, PDB ID, protein accession number along with their Uniprot ID in addition to information of Refseq information, GO terms of individual genes and their Pubmed links. NeuroMuscleDB is equipped with flexible search features including user-friendly browser and hyper-text link-outs to nucleotide and protein sequence databases and tools for primers designing, multiple sequence alignment, transcriptional factor identification, and promoter analysis (Figure 2). NeuroMuscleDB covering maximum information on one platform will provide useful information for experimental and computational analyses of myogenesis related genes. The user friendly mode of the database carry information for all sequences submitted in the primary database and focus on the gene sequence, three dimensional structure and other features relevant to the process of myogenesis (Baig et al., 2019 [5]).

Figure 2 from (Baig et al., 2019) [5]. A schematic representation of NeuroMuscleDB.

SkeletalVis

The SkeletalVis data portal provides an exploration and comparison platform for analysed skeletal transcriptomics data (Soul et al., 2019 [6]). SkeletalVis collates 287 cross-species skeletal transcriptomic experiments and is an intuitive data-portal to allow exploration and meta-analysis. Analysis of the raw data through the transcriptomics pipeline generated 739 expression response profiles with quality control and PCA plots, differential expression and comprehensive downstream analysis comprising of pathway, active sub-network, GO Term, drug, transcription factor enrichment (Figure 3). Transcriptomic signatures from gene perturbation experiments were examined to find the top pairwise similar expression responses to highlight examples of association identification enabled by this re-analysis of the transcriptomic datasets. To identify compounds potentially capable of mimicking or reversing the observed differential expression in the identified perturbation groups, authors of the database generated rank-product consensus signatures and performed drug enrichment analysis using the LINCS L1000 drug signatures. Code for the pipeline and post-processing of the data can be found here.

Figure 3 from (Soul et al., 2019) [6]. Schematic diagram of the SkeletalVis transcriptomics pipeline.

TiGER

The TiGER (Tissue-specific Gene Expression and Regulation) is a publicly available a database, which summarizes and provides large scale data sets for tissue-specific gene expression and regulation in a variety of human tissues. The database contains three types of data including tissue-specific gene expression profiles, combinatorial gene regulations, and cis-regulatory module (CRM) detections (Liu et al., 2008 [7]). At present the database contains expression profiles for 19,526 UniGene genes, combinatorial regulations for 7,341 transcription factor pairs and 6,232 putative CRMs for 2,130 RefSeq genes. The TiGER database includes information about 325 RefSeq Genes which are preferentially expressed in muscle and contains 730 transcription factor pairs co-regulating in muscle.

Summarized table of the databases

Database	Short description	Data type	Functionality	Statistics	Current status	Reference
MuscleDB	MuscleDB is a project that uses unbiased RNA sequencing (RNA-seq) to profile global mRNA expression in a wide array of smooth, cardiac, and skeletal muscle tissues from mice and rats.	Expression profiling by high throughput sequencing.	User can filter the database search by: 1. gene symbol (like ‘Per1’); 2. gene ontology (like ‘GTPase activity’); 3. muscle tissue type; 4. expression level; 5. p-value (statistically significant difference between tissues (based on a two-way ANOVA)); 6. change in expression, relative to another tissue type. User can also select which muscle tissues are interest of. By default, all tissues are checked. At the bottom of the plot options, just below ‘advanced filtering’, are the different ways to display the data. User can choose to show: 1. plot (default): a bar graph of the expression levels in the tissues (in FPKM, Fragments Per Kilobase per Million reads) for each transcript, and options to save the plots; 2. table: numeric table with the gene symbols, transcript names, expression levels in the tissues (in FPKM, Fragments Per Kilobase per Million reads), and the q-value (difference between tissues from a two-way ANOVA); 3. volcano plot: volcano plot comparing two muscles, showing the logarithm of q-value versus the logarithm of the fold-change in expression; 4. heat map: a dynamic heat map comparing the expression level of each transcript for each tissue; 5. compare genes: a series of scatter plots comparing the expression levels to a particular reference tissue.	126 samples, 17 mouse tissues (all from males), 2 female rat tissues, 2 male rat tissues. Six replicates for each tissue; each replicate is 3 individual samples pooled. For mouse tissues, 3 are smooth muscle, 3 are cardiac muscle and 11 are skeletal muscle. For male and female rat samples, both tissues are skeletal.	The beta-version is alive. The last update is 2 ya.	Terry et al., 2018 [2]
GeneXX	GeneXX is an online tool for the exploration of transcript changes in skeletal muscle associated with exercise.	Expression profiling by microarray (Illumina, Affymetrix, or Agilent) and high throughput sequencing (Illumina HiSeq 2000).	Users can enter any human gene of interest, (e.g., PPARGC1A) and immediately observe log-fold change values, adjusted P values (q value), and the time point postexercise at which the transcript was measured, with color- and shape-coded symbols to indicate statistical significance and sex of participants, respectively. Also included are PubMed scores and a short summary about the gene of interest from the NCBI gene site.	In total database includes 19 data sets from GEO the info about which is summarized in Table 1 of the article [3]. Criteria for inclusion were that the data were collected on healthy participants completing an acute bout of endurance or resistance exercise, before or at the end of a chronic training regime, as defined by the conductors of each study. Included are both cross-sectional (exercise vs. sedentary) or within subjects (pre- vs. postexercise) comparisons analyzing biopsies of the vastus lateralis or biceps brachii (GSE24235 only) and in both male and female participants of all ages.	The current version of the database is alive, but this tool is still in it's trial phase. The last update was 1 ya.	Reibe et al., 2018 [3]
SKmDB	SKmDB is an integrated database of NGS information in skeletal muscle. SKmDB not only includes all NGS datasets available in the human and mouse skeletal muscle tissues and cells, but also provide preliminary data analyses including gene/isoform expression levels, gene co-expression subnetworks, as well as assembly of putative lincRNAs, typical and super enhancers and transcription factor hotspots.	SKmDB is gathering all NGS datasets available in the human and mouse skeletal muscle cells and tissue including CLIP-seq, miRNA-seq, small RNAseq, single cell RNA-seq, RNA-seq, ChIP-seq, AIMS-seq, DNase-seq, ATAC-seq, MNase-seq and Bisulfite-seq.	Users can efficiently search, browse and visualize the information with the well-designed user interface and server side. Track visualization of all the RNA-seq, small RNA-seq, miRNA-seq, ChIP-seq, DNase-seq, ATAC-seq and MNase-seq data on mm9, mm10, hg19, hg38 reference genome are provided through a genome visualization tool called Biodalliance.	To compile all the available datasets for the field of skeletal muscle, authors searched keywords related to skeletal muscle and myogenesis in Roadmap, ENCODE, GEO database and collected 11 types of NGS data corresponding to 16 mouse and 13 human cell types as well as 9 mouse and 20 human tissues.	The current version of the database is alive, but the server is occasionally not available.	Yuan et al., 2019 [1]
MGS resource	MGS resource is a collection of phenotypic-level gene sets in which genes share actual connections in the form of differential expression in the same transcriptomic comparison. The MGS resource can be used to investigate the behaviour of any list of genes across > 1100 previous comparisons of muscle conditions, to compare previous studies to one another, and to explore the functional relationship of muscle dysregulation to the gene ontology.	Expression profiling by microarray (Affymetrix).	Its major intended use is in enrichment testing for functional genomics analysis, for which purpose it has been made accessible through three commonly used analytical tools (GSEA, EnrichR, and WebGestalt).	The current download is comprised of 1,517 Gene Sets. Of these, 1,156 were derived from authors recent analysis of 302 studies of muscle physiology and disease published from 2005-present, including 4305 separate samples. A further 122 were derived from published in vitro muscle microarray studies carried out from 2005-present, as used in their previous work, and 185 were derived from a previous meta-analysis carried out by Jelier et al., 2008 [8]. The remaining 54 are from muscle-related gene ontology terms, but also several other muscle-relevant entries in the MSigDB database (mostly comprising muscle-related pathways from Reactome or Biocarta databases).	The current version 3 is released March 2019.	Malatras et al., 2019 [4]
NeuroMuscleDB	Neuro-Muscle Database (NeuroMuscleDB) is an authoritative collection of genes related to muscle development from Homo sapiens, Mus musculus and Bos taurus. NeuroMuscleDB is the first comprehensive database developed to catalog and categorizes available information of muscle related genes to facilitate easy retrieval of information according to their involvement at different stages of muscle development.	Detailed gene information retrieved from NCBI and related sources, including Gene IDs, locations, short descriptions of genes (e.g., number of exons and start and end positions of genomic accession), PDB IDs, protein accession numbers, and UniProt IDs.	NeuroMuscleDB is equipped with flexible search features including user-friendly browser and hyper-text link-outs to nucleotide and protein sequence databases and tools for primers designing, multiple sequence alignment, transcriptional factor identification, and promoter analysis. The homepage of the NeuroMuscleDB web interface includes a search button that enables the retrieval of information matching query keywords. Users can search for information using the gene or species name, gene type, functional stage, etc.	At present, NeuroMuscleDB contains organized and curated information of about 1102 genes, 6030 mRNAs, and 5687 proteins that participate in muscle development in three mammalian species.	The most recent version released on Jan 2019 is alive.	Baig et al., 2019 [5]
SkeletalVis	SkeletalVis is a user friendly web application for exploration of skeletal biology related expression datasets.	Expression profiling based on public microarray and RNA-Seq data from ArrayExpress and GEO.	SkeletalVis provides users with a platform to explore the wealth of available expression data, develop consensus signatures and the ability to compare gene signatures from new experiments to the analysed data to facilitate meta-analysis.	It currently hosts 300 analysed experiments with 779 perturbation responses.	The most recent version released on 30th Nov 2018 is alive.	Soul et al., 2019 [6]
TiGER	TiGER is a database developed by the Bioinformatics Lab at Wilmer Eye Institute of Johns Hopkins University. The database contains tissue-specific gene expression profiles or expressed sequence tag (EST) data, cis-regulatory module (CRM) data, and combinatorial gene regulation data.	TiGER contains three types of data including tissue-specific gene expression profiles, TF interactions and CRMs.	The database provides three views (gene view, TF view, and tissue view) to allow users to conveniently retrieve information about genes, TFs or tissues of interest. Users can also select a tissue name to retrieve a list of genes preferentially expressed in the tissue. The database provides visualizations of the gene expression profiles, TF interactions and CRM detections. Sortable summary tables, links to raw data and links to external databases are also provided for user reference.	It currently includes information about 325 RefSeq Genes which are preferentially expressed in muscle, 169 Cis-Regulatory Module (CRM) detections in muscle and contains 730 transcription factor pairs co-regulating in muscle.	The last version was released on Jun 2008 and is still alive.	Liu et al., 2008 [7]

Table1. Summarized table of the databases with transcriptomics data generated for skeletal muscle in different species.

Available transcriptomic datasets at GEO

Dataset	Organism	Experiment type	Reference
GSE126296	Homo sapiens	Expression profiling by array.	Rundqvist et al., 2019 [9]
GSE123879	Mus musculus	Genome binding/occupancy profiling by high throughput sequencing. Expression profiling by high throughput sequencing.	Ramachandran et al., 2019 [10]
GSE120862	Homo sapiens	Expression profiling by high throughput sequencing.	Popov et al., 2019 [11]
GSE86931	Homo sapiens	Expression profiling by high throughput sequencing.	Popov et al., 2018 [12]
GSE107934	Homo sapiens	Expression profiling by high throughput sequencing.	Dickinson et al., 2018 [13]
GSE100505	Mus musculus; Rattus norvegicus	Expression profiling by high throughput sequencing.	Terry et al., 2018 [14]
GSE60590	Homo sapiens	Expression profiling by high throughput sequencing.	Lindholm et al., 2016 [15]
GSE75448	Mus musculus	Expression profiling by array.	Martínez-Redondo et al., 2016 [16]
GSE63887	Homo sapiens	Expression profiling by high throughput sequencing.	Väremo et al., 2015 [17], Väremo et al., 2017 [18]
GSE60591	Homo sapiens	Expression profiling by high throughput sequencing.	Lindholm et al., 2014 [19]
GSE60655	Homo sapiens	Methylation profiling by genome tiling array.	Lindholm et al., 2014 [19]
GSE58608	Homo sapiens	Expression profiling by high throughput sequencing.	Lindholm et al., 2014 [20]
GSE59088	Homo sapiens	Expression profiling by array.	Vissing and Schjerling, 2014 [21]
GSE43856	Homo sapiens	Expression profiling by array.	Neubauer et al., 2013 [22]
GSE40439	Mus musculus	Expression profiling by array.	Pérez-Schindler et al., 2012 [23]
GSE42473	Mus musculus	Expression profiling by array.	Ruas et al., 2012 [24]

Table2. All current GSE datasets which are available for mouse and human.

References

1. Yuan J, Zhou J, Wang H, and Sun H. SKmDB: an integrated database of next generation sequencing information in skeletal muscle. Bioinformatics. 2019 Mar 1;35(5):847-855. DOI:10.1093/bioinformatics/bty705 |
2. Terry EE, Zhang X, Hoffmann C, Hughes LD, Lewis SA, Li J, Wallace MJ, Riley LA, Douglas CM, Gutierrez-Monreal MA, Lahens NF, Gong MC, Andrade F, Esser KA, and Hughes ME. Transcriptional profiling reveals extraordinary diversity among skeletal muscle tissues. Elife. 2018 May 29;7. DOI:10.7554/eLife.34613 |
3. Reibe S, Hjorth M, Febbraio MA, and Whitham M. GeneXX: an online tool for the exploration of transcript changes in skeletal muscle associated with exercise. Physiol Genomics. 2018 May 1;50(5):376-384. DOI:10.1152/physiolgenomics.00127.2017 |
4. Malatras A, Duguez S, and Duddy W. Muscle Gene Sets: a versatile methodological aid to functional genomics in the neuromuscular field. Skelet Muscle. 2019 May 3;9(1):10. DOI:10.1186/s13395-019-0196-z |
5. Baig MH, Rashid I, Srivastava P, Ahmad K, Jan AT, Rabbani G, Choi D, Barreto GE, Ashraf GM, Lee EJ, and Choi I. NeuroMuscleDB: a Database of Genes Associated with Muscle Development, Neuromuscular Diseases, Ageing, and Neurodegeneration. Mol Neurobiol. 2019 Aug;56(8):5835-5843. DOI:10.1007/s12035-019-1478-5 |
6. Soul J, Hardingham TE, Boot-Handford RP, and Schwartz JM. SkeletalVis: an exploration and meta-analysis data portal of cross-species skeletal transcriptomics data. Bioinformatics. 2019 Jul 1;35(13):2283-2290. DOI:10.1093/bioinformatics/bty947 |
7. Liu X, Yu X, Zack DJ, Zhu H, and Qian J. TiGER: a database for tissue-specific gene expression and regulation. BMC Bioinformatics. 2008 Jun 9;9:271. DOI:10.1186/1471-2105-9-271 |
8. Jelier R, 't Hoen PA, Sterrenburg E, den Dunnen JT, van Ommen GJ, Kors JA, and Mons B. Literature-aided meta-analysis of microarray data: a compendium study on muscle development and disease. BMC Bioinformatics. 2008 Jun 24;9:291. DOI:10.1186/1471-2105-9-291 |
9. Rundqvist HC, Montelius A, Osterlund T, Norman B, Esbjornsson M, and Jansson E. Acute sprint exercise transcriptome in human skeletal muscle. PLoS One. 2019;14(10):e0223024. DOI:10.1371/journal.pone.0223024 |
10. Ramachandran K, Senagolage MD, Sommars MA, Futtner CR, Omura Y, Allred AL, and Barish GD. Dynamic enhancers control skeletal muscle identity and reprogramming. PLoS Biol. 2019 Oct;17(10):e3000467. DOI:10.1371/journal.pbio.3000467 |
11. Popov DV, Makhnovskii PA, Shagimardanova EI, Gazizova GR, Lysenko EA, Gusev OA, and Vinogradova OL. Contractile activity-specific transcriptome response to acute endurance exercise and training in human skeletal muscle. Am J Physiol Endocrinol Metab. 2019 Apr 1;316(4):E605-E614. DOI:10.1152/ajpendo.00449.2018 |
12. Popov DV, Makhnovskii PA, Kurochkina NS, Lysenko EA, Vepkhvadze TF, and Vinogradova OL. Intensity-dependent gene expression after aerobic exercise in endurance-trained skeletal muscle. Biol Sport. 2018 Sep;35(3):277-289. DOI:10.5114/biolsport.2018.77828 |
13. Dickinson JM, D'Lugos AC, Naymik MA, Siniard AL, Wolfe AJ, Curtis DR, Huentelman MJ, and Carroll CC. Transcriptome response of human skeletal muscle to divergent exercise stimuli. J Appl Physiol (1985). 2018 Jun 1;124(6):1529-1540. DOI:10.1152/japplphysiol.00014.2018 |
14. Terry EE, Zhang X, Hoffmann C, Hughes LD, Lewis SA, Li J, Wallace MJ, Riley LA, Douglas CM, Gutierrez-Monreal MA, Lahens NF, Gong MC, Andrade F, Esser KA, and Hughes ME. Transcriptional profiling reveals extraordinary diversity among skeletal muscle tissues. Elife. 2018 May 29;7. DOI:10.7554/eLife.34613 |
15. Lindholm ME, Giacomello S, Werne Solnestam B, Fischer H, Huss M, Kjellqvist S, and Sundberg CJ. The Impact of Endurance Training on Human Skeletal Muscle Memory, Global Isoform Expression and Novel Transcripts. PLoS Genet. 2016 Sep;12(9):e1006294. DOI:10.1371/journal.pgen.1006294 |
16. Martínez-Redondo V, Jannig PR, Correia JC, Ferreira DM, Cervenka I, Lindvall JM, Sinha I, Izadi M, Pettersson-Klein AT, Agudelo LZ, Gimenez-Cassina A, Brum PC, Dahlman-Wright K, and Ruas JL. Peroxisome Proliferator-activated Receptor γ Coactivator-1 α Isoforms Selectively Regulate Multiple Splicing Events on Target Genes. J Biol Chem. 2016 Jul 15;291(29):15169-84. DOI:10.1074/jbc.M115.705822 |
17. Väremo L, Scheele C, Broholm C, Mardinoglu A, Kampf C, Asplund A, Nookaew I, Uhlén M, Pedersen BK, and Nielsen J. Proteome- and transcriptome-driven reconstruction of the human myocyte metabolic network and its use for identification of markers for diabetes. Cell Rep. 2015 May 12;11(6):921-933. DOI:10.1016/j.celrep.2015.04.010 |
18. Väremo L, Henriksen TI, Scheele C, Broholm C, Pedersen M, Uhlén M, Pedersen BK, and Nielsen J. Type 2 diabetes and obesity induce similar transcriptional reprogramming in human myocytes. Genome Med. 2017 May 25;9(1):47. DOI:10.1186/s13073-017-0432-2 |
19. Lindholm ME, Marabita F, Gomez-Cabrero D, Rundqvist H, Ekström TJ, Tegnér J, and Sundberg CJ. An integrative analysis reveals coordinated reprogramming of the epigenome and the transcriptome in human skeletal muscle after training. Epigenetics. 2014 Dec;9(12):1557-69. DOI:10.4161/15592294.2014.982445 |
20. Lindholm ME, Huss M, Solnestam BW, Kjellqvist S, Lundeberg J, and Sundberg CJ. The human skeletal muscle transcriptome: sex differences, alternative splicing, and tissue homogeneity assessed with RNA sequencing. FASEB J. 2014 Oct;28(10):4571-81. DOI:10.1096/fj.14-255000 |
21. Vissing K and Schjerling P. Simplified data access on human skeletal muscle transcriptome responses to differentiated exercise. Sci Data. 2014;1:140041. DOI:10.1038/sdata.2014.41 |
22. Neubauer O, Sabapathy S, Lazarus R, Jowett JB, Desbrow B, Peake JM, Cameron-Smith D, Haseler LJ, Wagner KH, and Bulmer AC. Transcriptome analysis of neutrophils after endurance exercise reveals novel signaling mechanisms in the immune response to physiological stress. J Appl Physiol (1985). 2013 Jun 15;114(12):1677-88. DOI:10.1152/japplphysiol.00143.2013 |
23. Pérez-Schindler J, Summermatter S, Salatino S, Zorzato F, Beer M, Balwierz PJ, van Nimwegen E, Feige JN, Auwerx J, and Handschin C. The corepressor NCoR1 antagonizes PGC-1α and estrogen-related receptor α in the regulation of skeletal muscle function and oxidative metabolism. Mol Cell Biol. 2012 Dec;32(24):4913-24. DOI:10.1128/MCB.00877-12 |
24. Ruas JL, White JP, Rao RR, Kleiner S, Brannan KT, Harrison BC, Greene NP, Wu J, Estall JL, Irving BA, Lanza IR, Rasbach KA, Okutsu M, Nair KS, Yan Z, Leinwand LA, and Spiegelman BM. A PGC-1α isoform induced by resistance training regulates skeletal muscle hypertrophy. Cell. 2012 Dec 7;151(6):1319-31. DOI:10.1016/j.cell.2012.10.050 |