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NeuroMuscleDB: a Database of Genes Associated with Muscle Development, Neuromuscular Diseases, Ageing, and Neurodegeneration

  • Mohammad Hassan Baig
  • Iliyas Rashid
  • Prachi Srivastava
  • Khurshid Ahmad
  • Arif Tasleem Jan
  • Gulam Rabbani
  • Dukhwan Choi
  • George E. Barreto
  • Ghulam Md Ashraf
  • Eun Ju LeeEmail author
  • Inho ChoiEmail author
Article

Abstract

Skeletal muscle is a highly complex, heterogeneous tissue that serves a multitude of biological functions in living organisms. With the advent of methods, such as microarrays, transcriptome analysis, and proteomics, studies have been performed at the genome level to gain insight of changes in the expression profiles of genes during different stages of muscle development and of associated diseases. In the present study, a database was conceived for the straightforward retrieval of information on genes involved in skeletal muscle formation, neuromuscular diseases (NMDs), ageing, and neurodegenerative disorders (NDs). The resulting database named NeuroMuscleDB (http://yu-mbl-muscledb.com/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 authors believe NeuroMuscleDB provides a platform for obtaining desired information on genes related to myogenesis and their associations with various diseases (NMDs, ageing, and NDs). NeuroMuscleDB is freely available on the web at http://yu-mbl-muscledb.com/NeuroMuscleDB and supports all major browsers.

Keywords

Database Genes Myogenesis Skeletal muscle Ageing Neuromuscular dystrophies Neurodegenerative disorders 

Notes

Author Contributions

IC conceived the idea; IR and MHB programmed for data manipulation, developed database, and application modules for browsing and analyzing the information; MHB, EJL, GR, and DC collected the data; and IC, ATJ, EJL, PS, GEB, and GMA helped compile the biological aspects of the database. IC, ATJ, GEB, GMA, MHB, IR, PS, and KA drafted the manuscript.

Funding Information

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (#2016R1C1B1011478) and by the Creative Economy Leading Technology Development Program through the Gyeongsangbuk-Do and Gyeongbuk Science and Technology Promotion Center of Korea (#SF316001A).

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Cittadella Vigodarzere G, Mantero S (2014) Skeletal muscle tissue engineering: Strategies for volumetric constructs. Front Physiol 5:362.  https://doi.org/10.3389/fphys.2014.00362 CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Malik A, Lee EJ, Jan AT, Ahmad S, Cho KH, Kim J, Choi I (2015) Network analysis for the identification of differentially expressed hub genes using myogenin knock-down muscle satellite cells. PLoS One 10(7):e0133597.  https://doi.org/10.1371/journal.pone.0133597 CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Yin H, Price F, Rudnicki MA (2013) Satellite cells and the muscle stem cell niche. Physiol Rev 93(1):23–67.  https://doi.org/10.1152/physrev.00043.2011 CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Grefte S, Kuijpers-Jagtman AM, Torensma R, Von den Hoff JW (2007) Skeletal muscle development and regeneration. Stem Cells Dev 16(5):857–868.  https://doi.org/10.1089/scd.2007.0058 CrossRefPubMedGoogle Scholar
  5. 5.
    Jan AT, Lee EJ, Ahmad S, Choi I (2016) Meeting the meat: delineating the molecular machinery of muscle development. J Anim Sci Technol 58:18.  https://doi.org/10.1186/s40781-016-0100-x CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Sharma S, Thind SS, Kaur A (2015) In vitro meat production system: why and how? J Food Sci Technol 52(12):7599–7607.  https://doi.org/10.1007/s13197-015-1972-3 CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Listrat A, Lebret B, Louveau I, Astruc T, Bonnet M, Lefaucheur L, Picard B, Bugeon J (2016) How muscle structure and composition influence meat and flesh quality. ScientificWorldJournal 2016:3182746–3182714.  https://doi.org/10.1155/2016/3182746 CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Hocquette JF, Lehnert S, Barendse W, Cassar-Malek I, Picard B (2007) Recent advances in cattle functional genomics and their application to beef quality. Animal 1(1):159–173.  https://doi.org/10.1017/S1751731107658042 CrossRefPubMedGoogle Scholar
  9. 9.
    D'Alessandro A, Zolla L (2013) Meat science: From proteomics to integrated omics towards system biology. J Proteome 78:558–577.  https://doi.org/10.1016/j.jprot.2012.10.023 CrossRefGoogle Scholar
  10. 10.
    Kalyani RR, Corriere M, Ferrucci L (2014) Age-related and disease-related muscle loss: the effect of diabetes, obesity, and other diseases. Lancet Diabetes Endocrinol 2(10):819–829CrossRefGoogle Scholar
  11. 11.
    Manini TM, Hong SL, Clark BC (2013) Aging and muscle: a neuron’s perspective. Curr Opin Clin Nutr Metab Care 16(1):21–26.  https://doi.org/10.1097/MCO.0b013e32835b5880 CrossRefPubMedGoogle Scholar
  12. 12.
    Skalsky AJ, McDonald CM (2012) Prevention and management of limb contractures in neuromuscular diseases. Phys Med Rehabil Clin N Am 23(3):675–687.  https://doi.org/10.1016/j.pmr.2012.06.009 CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Lee EJ, Jan AT, Baig MH, Ashraf JM, Nahm SS, Kim YW, Park SY, Choi I (2016) Fibromodulin: a master regulator of myostatin controlling progression of satellite cells through a myogenic program. FASEB J 30(8):2708–2719.  https://doi.org/10.1096/fj.201500133R CrossRefPubMedGoogle Scholar
  14. 14.
    Braun T, Gautel M (2011) Transcriptional mechanisms regulating skeletal muscle differentiation, growth and homeostasis. Nat Rev Mol Cell Biol 12(6):349–361.  https://doi.org/10.1038/nrm3118 CrossRefPubMedGoogle Scholar
  15. 15.
    Gunning P, Hardeman E (1991) Multiple mechanisms regulate muscle fiber diversity. FASEB J 5(15):3064–3070CrossRefGoogle Scholar
  16. 16.
    Haider S, Pal R (2013) Integrated analysis of transcriptomic and proteomic data. Curr Genomics 14(2):91–110.  https://doi.org/10.2174/1389202911314020003 CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10(1):57–63.  https://doi.org/10.1038/nrg2484 CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, Szczesniak MW, Gaffney DJ et al (2016) A survey of best practices for RNA-seq data analysis. Genome Biol 17:13.  https://doi.org/10.1186/s13059-016-0881-8 CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Carninci P (2009) Is sequencing enlightenment ending the dark age of the transcriptome? Nat Methods 6(10):711–713CrossRefGoogle Scholar
  20. 20.
    Manzoni C, Kia DA, Vandrovcova J, Hardy J, Wood NW, Lewis PA, Ferrari R (2016) Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences. Brief Bioinform 19:286–302.  https://doi.org/10.1093/bib/bbw114 CrossRefPubMedCentralGoogle Scholar
  21. 21.
    Zou D, Ma L, Yu J, Zhang Z (2015) Biological databases for human research. Genomics Proteomics Bioinformatics 13(1):55–63.  https://doi.org/10.1016/j.gpb.2015.01.006 CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Stelzer G, Rosen N, Plaschkes I, Zimmerman S, Twik M, Fishilevich S, Stein TI, Nudel R, Lieder I, Mazor Y, Kaplan S, Dahary D, Warshawsky D, Guan-Golan Y, Kohn A, Rappaport N, Safran M, Lancet D (2016) The GeneCards Suite: from Gene Data Mining to Disease Genome Sequence Analyses. Curr Protoc Bioinformatics 54:1 30 31–31 30 33.  https://doi.org/10.1002/cpbi.5
  23. 23.
    Pinero J, Queralt-Rosinach N, Bravo A, Deu-Pons J, Bauer-Mehren A, Baron M, Sanz F, Furlong LI (2015) DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database (Oxford) 2015:bav028.  https://doi.org/10.1093/database/bav028 CrossRefGoogle Scholar
  24. 24.
    Kaplan JC, Hamroun D (2015) The 2016 version of the gene table of monogenic neuromuscular disorders (nuclear genome). Neuromuscul Disord 25(12):991–1020.  https://doi.org/10.1016/j.nmd.2015.10.010 CrossRefPubMedGoogle Scholar
  25. 25.
    Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA (2005) Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res 33(Database issue):D514–D517.  https://doi.org/10.1093/nar/gki033 CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Rashid I, Nagpure NS, Srivastava P, Kumar R, Pathak AK, Singh M, Kushwaha B (2017) HRGFish: a database of hypoxia responsive genes in fishes. Sci Rep 7:42346.  https://doi.org/10.1038/srep42346 CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Brown GR, Hem V, Katz KS, Ovetsky M, Wallin C, Ermolaeva O, Tolstoy I, Tatusova T et al (2015) Gene: a gene-centered information resource at NCBI. Nucleic Acids Res 43(Database issue):D36–D42.  https://doi.org/10.1093/nar/gku1055 CrossRefPubMedGoogle Scholar
  28. 28.
    Coordinators NR (2015) Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 43(Database issue):D6–D17.  https://doi.org/10.1093/nar/gku1130 CrossRefGoogle Scholar
  29. 29.
    Dreos R, Ambrosini G, Cavin Perier R, Bucher P (2013) EPD and EPDnew, high-quality promoter resources in the next-generation sequencing era. Nucleic Acids Res 41(Database issue):D157–D164.  https://doi.org/10.1093/nar/gks1233 CrossRefPubMedGoogle Scholar
  30. 30.
    de Magalhaes JP, Toussaint O (2004) GenAge: a genomic and proteomic network map of human ageing. FEBS Lett 571(1–3):243–247.  https://doi.org/10.1016/j.febslet.2004.07.006 CrossRefPubMedGoogle Scholar
  31. 31.
    Tacutu R, Craig T, Budovsky A, Wuttke D, Lehmann G, Taranukha D, Costa J, Fraifeld VE et al (2013) Human Ageing Genomic Resources: integrated databases and tools for the biology and genetics of ageing. Nucleic Acids Res 41(Database issue):D1027–D1033.  https://doi.org/10.1093/nar/gks1155 CrossRefPubMedGoogle Scholar
  32. 32.
    Untergasser A, Cutcutache I, Koressaar T, Ye J, Faircloth BC, Remm M, Rozen SG (2012) Primer3—new capabilities and interfaces. Nucleic Acids Res 40(15):e115.  https://doi.org/10.1093/nar/gks596 CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Nagpure NS, Rashid I, Pati R, Pathak AK, Singh M, Singh SP, Sarkar UK (2013) FishMicrosat: a microsatellite database of commercially important fishes and shellfishes of the Indian subcontinent. BMC Genomics 14:630.  https://doi.org/10.1186/1471-2164-14-630 CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Nagpure NS, Rashid I, Pathak AK, Singh M, Pati R, Singh SP, Sarkar UK (2015) FMiR: a curated resource of mitochondrial DNA information for fish. PLoS One 10(8):e0136711.  https://doi.org/10.1371/journal.pone.0136711 CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215(3):403–410.  https://doi.org/10.1016/S0022-2836(05)80360-2 CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Mount DW (2007) Using the basic local alignment search tool (BLAST). CSH Protoc 2007:pdb top17.  https://doi.org/10.1101/pdb.top17 CrossRefPubMedGoogle Scholar
  37. 37.
    Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, Valentin F, Wallace IM et al (2007) Clustal W and Clustal X version 2.0. Bioinformatics 23(21):2947–2948.  https://doi.org/10.1093/bioinformatics/btm404 CrossRefPubMedGoogle Scholar
  38. 38.
    Krauss RS (2010) Regulation of promyogenic signal transduction by cell-cell contact and adhesion. Exp Cell Res 316(18):3042–3049.  https://doi.org/10.1016/j.yexcr.2010.05.008 CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    de Magalhaes JP, Costa J, Toussaint O (2005) HAGR: the human ageing genomic resources. Nucleic Acids Res 33(Database issue):D537–D543.  https://doi.org/10.1093/nar/gki017 CrossRefPubMedGoogle Scholar
  40. 40.
    Kalyani RR, Corriere M, Ferrucci L (2014) Age-related and disease-related muscle loss: the effect of diabetes, obesity, and other diseases. Lancet Diabetes Endocrinol 2(10):819–829.  https://doi.org/10.1016/S2213-8587(14)70034-8 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Mohammad Hassan Baig
    • 1
  • Iliyas Rashid
    • 2
  • Prachi Srivastava
    • 2
  • Khurshid Ahmad
    • 1
  • Arif Tasleem Jan
    • 3
  • Gulam Rabbani
    • 1
  • Dukhwan Choi
    • 1
  • George E. Barreto
    • 4
    • 5
  • Ghulam Md Ashraf
    • 6
  • Eun Ju Lee
    • 1
    Email author
  • Inho Choi
    • 1
    Email author
  1. 1.Department of Medical BiotechnologyYeungnam UniversityGyeongsanRepublic of Korea
  2. 2.Amity Institute of BiotechnologyAmity UniversityLucknowIndia
  3. 3.School of Biosciences and BiotechnologyBaba Ghulam Shah Badshah UniversityRajouriIndia
  4. 4.Departamento de Nutrición y Bioquímica, Facultad de CienciasPontificia Universidad JaverianaBogotá D.C.Colombia
  5. 5.Instituto de Ciencias BiomédicasUniversidad Autónoma de ChileSantiagoChile
  6. 6.King Fahd Medical Research CenterKing Abdulaziz UniversityJeddahSaudi Arabia

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