Skip to main content

A System Biology and Bioinformatics Approach to Determine the Molecular Signature, Core Ontologies, Functional Pathways, Drug Compounds in Between Stress and Type 2 Diabetes

  • Conference paper
  • First Online:
Bioinformatics and Biomedical Engineering (IWBBIO 2023)

Abstract

Bioinformatics is the application of computer science and information technology to the field of biology and medicine. It involves the analysis of large amounts of biological data, such as DNA sequences, protein structures, and gene expression patterns. Bioinformatics is used to develop new methods for understanding and analyzing biological data, as well as to develop new tools and technologies for biological research. Bioinformatics is used in a variety of fields, including genomics, proteomics, and drug discovery. In this study, focus on two severe diseases which affect millions of people globally such as stress and type 2 diabetes. Stress can have a significant impact on people with type 2 diabetes. Stress can cause blood sugar levels to rise, making it difficult to manage diabetes. The purpose of this research is to use various bioinformatics methods to discover potential therapeutic drugs and functional pathways between stress and type 2 diabetes. The microarray datasets GSE183648 and GSE20966 are used for the analysis of stress and type 2 diabetes samples respectively. After the datasets have been preprocessed and filtered through the use of the R programming language, identified the common DEGs. The depiction of common DEGs is shown by venn diagram. Next, the most active genes are identified through topological properties, and PPIs are built from the similar differential expressed genes (DEGs). These five genes NTRK2, SOCS3, NEDD9, MAP3K8, and SIRPA are the most important hub genes with in the interaction network of protein-protein. According to the common DEGs, GO terms molecular function (MF), KEGG and WikiPathways are shown in this study. Gene-miRNA interaction, TF-gene regulatory network, module analysis, GO terms (Biological Process, Cellular Component), Pathways (Reactome, BioCarta, BioPlanet) are all things that could be done with this research work in the future. In last, a therapeutic drug compounds are recommended on the basis of common DEGs.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Rahal, A., et al.: Oxidative stress, prooxidants, and antioxidants: the interplay. BioMed Res. Int. (2014)

    Google Scholar 

  2. Selye, H.: Stress without distress. In: Serban, G. (ed.) Psychopathology of Human Adaptation, pp. 137–146. Springer, Boston (1976). https://doi.org/10.1007/978-1-4684-2238-2_9

    Chapter  Google Scholar 

  3. Basar, M.A., Hosen, M.F., Paul, B.K., Hasan, M.R., Shamim, S.M., Bhuyian, T.: Identification of drug and protein-protein interaction network among stress and depression: a bioinformatics approach. Inform. Med. Unlocked 101174 (2023)

    Google Scholar 

  4. Theodore, W.H., et al.: Epilepsy in North America: a report prepared under the auspices of the global campaign against epilepsy, the International Bureau for Epilepsy, the International League Against Epilepsy, and the World Health Organization. Epilepsia 47(10), 1700–1722 (2006)

    Article  PubMed  Google Scholar 

  5. Cosgrove, M.P., Sargeant, L.A., Caleyachetty, R., Griffin, S.J.: Work-related stress and Type 2 diabetes: systematic review and meta-analysis. Occup. Med. 62(3), 167–173 (2012)

    Article  CAS  Google Scholar 

  6. Hosen, M.F., Basar, M.A., Paul, B.K., Hasan, M.R., Uddin, M.S.: A bioinformatics approach to identify candidate biomarkers and common pathways between bipolar disorder and stroke. In: 2022 12th International Conference on Electrical and Computer Engineering (ICECE), pp. 429–432. IEEE (2022)

    Google Scholar 

  7. Joint National Committee on Prevention, Evaluation, Treatment of High Blood Pressure and National High Blood Pressure Education Program: Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure, vol. 6. Public Health Service, National Institutes of Health, National Heart, Lung, and Blood Institute (1997)

    Google Scholar 

  8. Goyal, S., Morita, P., Lewis, G.F., Yu, C., Seto, E., Cafazzo, J.A.: The systematic design of a behavioural mobile health application for the self-management of type 2 diabetes. Can. J. Diabetes 40(1), 95–104 (2016)

    Article  PubMed  Google Scholar 

  9. Gesinde, B.: An Avatar video intervention on type 2 diabetes for women of color using brief motivational interviewing: predictors of self-efficacy post-video for performing the American Association of Diabetes Educator’s Seven Self-care Behaviors. Doctoral dissertation, Teachers College, Columbia University (2019)

    Google Scholar 

  10. Hasan, M.R., Paul, B.K., Ahmed, K., Bhuyian, T.: Design protein-protein interaction network and protein-drug interaction network for common cancer diseases: a bioinformatics approach. Inform. Med. Unlocked 18, 100311 (2020)

    Article  Google Scholar 

  11. Clough, E., Barrett, T.: The gene expression omnibus database. In: Statistical Genomics, pp. 93–110. Humana Press, New York (2016)

    Google Scholar 

  12. Edgar, R., Domrachev, M., Lash, A.E.: Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 30(1), 207–210 (2002)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Akter, J., et al.: Loss of P53 suppresses replication stress-induced DNA damage in ATRX-deficient neuroblastoma. Oncogenesis 10(11), 73 (2021)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Marselli, L., et al.: Gene expression profiles of Beta-cell enriched tissue obtained by laser capture microdissection from subjects with type 2 diabetes. PLoS ONE 5(7), e11499 (2010)

    Article  PubMed  PubMed Central  Google Scholar 

  15. Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Stat. Soc.: Ser. B (Methodol.) 57(1), 289–300 (1995)

    Google Scholar 

  16. Shadhin, K.A., et al.: Analysis of topological properties and drug discovery for bipolar disorder and associated diseases: a bioinformatics approach. Cell Mol. Biol. (Noisy-le-grand) 66(7), 152–160 (2020)

    Article  PubMed  Google Scholar 

  17. Šikić, M., Tomić, S., Vlahoviček, K.: Prediction of protein-protein interaction sites in sequences and 3D structures by random forests. PLoS Comput. Biol. 5(1), e1000278 (2009)

    Article  PubMed  PubMed Central  Google Scholar 

  18. Pagel, P., et al.: The MIPS mammalian protein-protein interaction database. Bioinformatics 21(6), 832–834 (2005)

    Article  CAS  PubMed  Google Scholar 

  19. Mering, C.V., Huynen, M., Jaeggi, D., Schmidt, S., Bork, P., Snel, B.: STRING: a database of predicted functional associations between proteins. Nucleic Acids Res. 31(1), 258–261 (2003)

    Article  Google Scholar 

  20. Xia, J., Gill, E.E., Hancock, R.E.: NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nat. Protoc. 10(6), 823–844 (2015)

    Article  CAS  PubMed  Google Scholar 

  21. Shannon, P., et al.: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13(11), 2498–2504 (2003)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Chin, C.H., Chen, S.H., Wu, H.H., Ho, C.W., Ko, M.T., Lin, C.Y.: cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst. Biol. 8(4), 1–7 (2014)

    Google Scholar 

  23. Subramanian, A., Kuehn, H., Gould, J., et al.: GSEA-P: a desktop application for gene set enrichment analysis. Bioinformatics 23(23), 3251–3 (2007)

    Article  CAS  PubMed  Google Scholar 

  24. Delfs, R., Doms, A., Kozlenkov, A., Schroeder, M.: GoPubMed: ontology-based literature search applied to Gene Ontology and PubMed. In: German Conference on Bioinformatics 2004, GCB 2004. Gesellschaft fur Informatik eV (2004)

    Google Scholar 

  25. Kuleshov, M.V., et al.: Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44(W1), W90–W97 (2016)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Ye, Z., et al.: Bioinformatic identification of candidate biomarkers and related transcription factors in nasopharyngeal carcinoma. World J. Surg. Oncol. 17(1), 1–10 (2019)

    Article  Google Scholar 

  27. Davis, C.A., et al.: The encyclopedia of DNA elements (ENCODE): data portal update. Nucleic Acids Res. 46(D1), D794–D801 (2018)

    Article  CAS  PubMed  Google Scholar 

  28. Yoo, M., Shin, J., Kim, J., et al.: DSigDB: drug signatures database for gene set analysis. Bioinformatics 31(18), 3069–71 (2015)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This paper is neither published nor being considered for publication anywhere else at this time. Each and every person who helped with this study is greatly appreciated by the authors.

Funding

This work is not financially supported.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Md. Rakibul Hasan .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Basar, M.A., Hasan, M.R., Paul, B.K., Shadhin, K.A., Mollah, M.S. (2023). A System Biology and Bioinformatics Approach to Determine the Molecular Signature, Core Ontologies, Functional Pathways, Drug Compounds in Between Stress and Type 2 Diabetes. In: Rojas, I., Valenzuela, O., Rojas Ruiz, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2023. Lecture Notes in Computer Science(), vol 13919. Springer, Cham. https://doi.org/10.1007/978-3-031-34953-9_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-34953-9_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34952-2

  • Online ISBN: 978-3-031-34953-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics