Literature DB >> 23085959

Functional classification of skeletal muscle networks. I. Normal physiology.

Yu Wang1, Jack Winters, Shankar Subramaniam.   

Abstract

Extensive measurements of the parts list of human skeletal muscle through transcriptomics and other phenotypic assays offer the opportunity to reconstruct detailed functional models. Through integration of vast amounts of data present in databases and extant knowledge of muscle function combined with robust analyses that include a clustering approach, we present both a protein parts list and network models for skeletal muscle function. The model comprises the four key functional family networks that coexist within a functional space; namely, excitation-activation family (forward pathways that transmit a motoneuronal command signal into the spatial volume of the cell and then use Ca(2+) fluxes to bind Ca(2+) to troponin C sites on F-actin filaments, plus transmembrane pumps that maintain transmission capacity); mechanical transmission family (a sophisticated three-dimensional mechanical apparatus that bidirectionally couples the millions of actin-myosin nanomotors with external axial tensile forces at insertion sites); metabolic and bioenergetics family (pathways that supply energy for the skeletal muscle function under widely varying demands and provide for other cellular processes); and signaling-production family (which represents various sensing, signal transduction, and nuclear infrastructure that controls the turn over and structural integrity and regulates the maintenance, regeneration, and remodeling of the muscle). Within each family, we identify subfamilies that function as a unit through analysis of large-scale transcription profiles of muscle and other tissues. This comprehensive network model provides a framework for exploring functional mechanisms of the skeletal muscle in normal and pathophysiology, as well as for quantitative modeling.

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Year:  2012        PMID: 23085959      PMCID: PMC3544495          DOI: 10.1152/japplphysiol.01514.2011

Source DB:  PubMed          Journal:  J Appl Physiol (1985)        ISSN: 0161-7567


  71 in total

1.  TRANSFAC: an integrated system for gene expression regulation.

Authors:  E Wingender; X Chen; R Hehl; H Karas; I Liebich; V Matys; T Meinhardt; M Prüss; I Reuter; F Schacherer
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

Review 2.  Control of actin assembly and disassembly at filament ends.

Authors:  J A Cooper; D A Schafer
Journal:  Curr Opin Cell Biol       Date:  2000-02       Impact factor: 8.382

3.  Muscle specific microRNAs are regulated by endurance exercise in human skeletal muscle.

Authors:  Søren Nielsen; Camilla Scheele; Christina Yfanti; Thorbjörn Akerström; Anders R Nielsen; Bente K Pedersen; Matthew J Laye; Matthew Laye
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4.  Antibody-directed myostatin inhibition enhances muscle mass and function in tumor-bearing mice.

Authors:  Kate T Murphy; Annabel Chee; Ben G Gleeson; Timur Naim; Kristy Swiderski; René Koopman; Gordon S Lynch
Journal:  Am J Physiol Regul Integr Comp Physiol       Date:  2011-06-15       Impact factor: 3.619

Review 5.  Signaling pathways perturbing muscle mass.

Authors:  David J Glass
Journal:  Curr Opin Clin Nutr Metab Care       Date:  2010-05       Impact factor: 4.294

6.  Human exercise-mediated skeletal muscle hypertrophy is an intrinsic process.

Authors:  Daniel W D West; Nicholas A Burd; Aaron W Staples; Stuart M Phillips
Journal:  Int J Biochem Cell Biol       Date:  2010-06-09       Impact factor: 5.085

7.  Analysis of fundamental human movement patterns through the use of in-depth antagonistic muscle models.

Authors:  J M Winters; L Stark
Journal:  IEEE Trans Biomed Eng       Date:  1985-10       Impact factor: 4.538

8.  Effects of detraining on enzymes of energy metabolism in individual human muscle fibers.

Authors:  M M Chi; C S Hintz; E F Coyle; W H Martin; J L Ivy; P M Nemeth; J O Holloszy; O H Lowry
Journal:  Am J Physiol       Date:  1983-03

9.  Functional classification of skeletal muscle networks. II. Applications to pathophysiology.

Authors:  Yu Wang; Jack Winters; Shankar Subramaniam
Journal:  J Appl Physiol (1985)       Date:  2012-10-18

10.  IUPHAR-DB: new receptors and tools for easy searching and visualization of pharmacological data.

Authors:  Joanna L Sharman; Chidochangu P Mpamhanga; Michael Spedding; Pierre Germain; Bart Staels; Catherine Dacquet; Vincent Laudet; Anthony J Harmar
Journal:  Nucleic Acids Res       Date:  2010-11-17       Impact factor: 16.971

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  5 in total

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Journal:  Hum Mol Genet       Date:  2014-02-25       Impact factor: 6.150

2.  Systems analysis of transcriptional data provides insights into muscle's biological response to botulinum toxin.

Authors:  Kavitha Mukund; Margie Mathewson; Viviane Minamoto; Samuel R Ward; Shankar Subramaniam; Richard L Lieber
Journal:  Muscle Nerve       Date:  2014-03-17       Impact factor: 3.217

3.  Functional classification of skeletal muscle networks. II. Applications to pathophysiology.

Authors:  Yu Wang; Jack Winters; Shankar Subramaniam
Journal:  J Appl Physiol (1985)       Date:  2012-10-18

4.  The histone code reader Spin1 controls skeletal muscle development.

Authors:  Holger Greschik; Delphine Duteil; Nadia Messaddeq; Dominica Willmann; Laura Arrigoni; Manuela Sum; Manfred Jung; Daniel Metzger; Thomas Manke; Thomas Günther; Roland Schüle
Journal:  Cell Death Dis       Date:  2017-11-23       Impact factor: 8.469

5.  Community Structure Analysis of Gene Interaction Networks in Duchenne Muscular Dystrophy.

Authors:  Tejaswini Narayanan; Shankar Subramaniam
Journal:  PLoS One       Date:  2013-06-19       Impact factor: 3.240

  5 in total

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