Skip to main content
SpringerLink
Log in

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

  • Published:
Molecular Neurobiology Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  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

    Article  PubMed  PubMed Central  Google Scholar 

  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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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

    Article  PubMed  PubMed Central  Google Scholar 

  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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  Google Scholar 

  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):819–829

    Article  PubMed  PubMed Central  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  PubMed  PubMed Central  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  15. Gunning P, Hardeman E (1991) Multiple mechanisms regulate muscle fiber diversity. FASEB J 5(15):3064–3070

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Carninci P (2009) Is sequencing enlightenment ending the dark age of the transcriptome? Nat Methods 6(10):711–713

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed Central  Google Scholar 

  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

    Article  PubMed  PubMed Central  Google Scholar 

  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. 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

    Article  CAS  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  36. Mount DW (2007) Using the basic local alignment search tool (BLAST). CSH Protoc 2007:pdb top17. https://doi.org/10.1101/pdb.top17

    Article  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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

    Article  CAS  PubMed  Google Scholar 

  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

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

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).

Author information

Author notes

  1. Mohammad Hassan Baig and Iliyas Rashid contributed equally to this work.

Authors and Affiliations

  1. Department of Medical Biotechnology, Yeungnam University, Gyeongsan, 38541, Republic of Korea

    Mohammad Hassan Baig, Khurshid Ahmad, Gulam Rabbani, Dukhwan Choi, Eun Ju Lee & Inho Choi

  2. Amity Institute of Biotechnology, Amity University, Lucknow, Uttar Pradesh, 226 028, India

    Iliyas Rashid & Prachi Srivastava

  3. School of Biosciences and Biotechnology, Baba Ghulam Shah Badshah University, Rajouri, 185236, India

    Arif Tasleem Jan

  4. Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá D.C., Colombia

    George E. Barreto

  5. Instituto de Ciencias Biomédicas, Universidad Autónoma de Chile, Santiago, Chile

    George E. Barreto

  6. King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia

    Ghulam Md Ashraf

Authors
  1. Mohammad Hassan Baig
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Iliyas Rashid
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Prachi Srivastava
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Khurshid Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Arif Tasleem Jan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gulam Rabbani
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Dukhwan Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. George E. Barreto
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ghulam Md Ashraf
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Eun Ju Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Inho Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Corresponding authors

Correspondence to Eun Ju Lee or Inho Choi.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Baig, M.H., Rashid, I., Srivastava, P. et al. NeuroMuscleDB: a Database of Genes Associated with Muscle Development, Neuromuscular Diseases, Ageing, and Neurodegeneration. Mol Neurobiol 56, 5835–5843 (2019). https://doi.org/10.1007/s12035-019-1478-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12035-019-1478-5

Keywords

Search

Navigation