Skip to main content
Log in

In Silico Study of ULK1 Gene as a Susceptible Biomarker for Neurodegeneration

  • Research Article
  • Published:
Proceedings of the National Academy of Sciences, India Section B: Biological Sciences Aims and scope Submit manuscript

Abstract

Neurodegenerative disorders refer to the loss of structure or function of neurons including the death of neurons, whereas cancer refers to the uncontrolled growth of cells. Various studies suggest that there is a strong correlation between various genes involved in neurodegenerative disorders and cancer. ULK1 is an important gene involved in the process of autophagy that leads to the cause of various neurodegenerative disorders. The main objective of the study is to analyze the role of ULK1 in autophagy and determine the ability of the gene to act as a potential biomarker for various neurodegenerative disorders. Mutational and gene expression analysis is done using in silico tools to determine the potency of ULK1 gene to be used as a susceptible marker for neurodegenerative disorders. Tools like mutation taster, polyphen2 and SIFT tool are used for mutational analysis and GWAS Central, GTEx, PhenoScanner and RegulomeDb for the study of gene expression and association of ULK1 gene in diseases and traits. Further validation of ULK1 gene was done through STRING, GeneMania and Cytoscape for the study of association of ULK1 gene with various other genes involved in neurodegeneration and cancer pathways. Result analysis of ULK1 gene through various in silico tools depicts that the mutations occurring in the gene are potentially dangerous and are also predicted to be benign and deleterious. RegulomeDb, GWAS Central, GTEx and PhenoScanner depicted the involvement of ULK1 gene in various parts of brain leading to various mental disorders. The interaction of ULK1 gene and its pathway analysis showed that ULK1 gene interacts with various other genes like ATG, mTOR, RPTOR and many more involved in autophagy pathway. After analysis of results obtained from different in silico tools, it is examined that ULK1 can be used as a potential biomarker for neurodegeneration.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Yang ZJ et al (2011) The role of autophagy in cancer: therapeutic implications. Mol Cancer Therapeut 10:1533–1541

    Article  CAS  Google Scholar 

  2. Morris LGT et al (2010) Genetic determinants at the interface of cancer and neurodegenerative disease. Oncogene 29:3453–3464

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. AmancioCarnero CB-A (2008) The PTEN/PI3K/AKT signalling pathway in cancer, therapeutic implications. Curr Cancer Drug Targets 8:187–198

    Article  Google Scholar 

  4. Timmons S et al (2009) Akt signal transduction dysfunction in Parkinson’s disease. Neurosci Lett 467:30–35

    Article  CAS  PubMed  Google Scholar 

  5. Hall ED (2011) mTOR signaling in disease. Curr Opin Cell Biol 23:744–755

    Article  PubMed  Google Scholar 

  6. Menzies FM et al (2017) Autophagy and neurodegeneration: pathogenic mechanisms and therapeutic opportunities. Neuron 93:1015–1034

    Article  CAS  PubMed  Google Scholar 

  7. Wold MS, Lim J, Lachance V et al (2016) ULK1-mediated phosphorylation of ATG14 promotes autophagy and is impaired in Huntington’s disease models. Mol Neurodegener 11:76. https://doi.org/10.1186/s13024-016-0141-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Ganley MZ (2017) The mammalian ULK1 complex and autophagy initiation. Essays Biochem 61:585–596

    Article  PubMed  PubMed Central  Google Scholar 

  9. Peidu Jiang NM (2014) Autophagy and human diseases. Cell Res 24:69–79

    Article  PubMed  Google Scholar 

  10. Fujikake N, Shin M, Shimizu S (2018) Association between autophagy and neurodegenerative diseases. Front Neurosci 12:255

    Article  PubMed  PubMed Central  Google Scholar 

  11. Button RW, Roberts SL, Willis TL, Hanemann CO, Luo S (2017) Accumulation of autophagosomes confers cytotoxicity. J Biol Chem 292:13599–13614

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Itakura E, Mizushima N (2010) Characterization of autophagosome formation site by a hierarchical analysis of mammalian Atg proteins. Autophagy 6:764–776

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Schwarz J, Rödelsperger C, Schuelke M et al (2010) MutationTaster evaluates disease-causing potential of sequence alterations. Nat Methods 7:575–576. https://doi.org/10.1038/nmeth0810-575

    Article  CAS  PubMed  Google Scholar 

  14. Adzhubei I, Jordan DM, Sunyaev SR. Predicting functional effect of human missense mutations using PolyPhen-2. CurrProtoc Hum Genet. 2013; Chapter 7: Unit7.20

  15. Kumar P, Henikoff S, Ng PC (2009) Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc 4:1073–1081

    Article  CAS  PubMed  Google Scholar 

  16. Boyle AP, Hong EL, Hariharan M, Cheng Y, Schaub MA, Kasowski M, Karczewski KJ, Park J, Hitz BC, Weng S, Cherry JM (2012) Annotation of functional variation in personal genomes using RegulomeDB. Genome Res 22:1790–1797

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Staley JR, PhenoScanner BJ (2016) a database of human genotype-phenotype associations. Bioinformatics 32:3207–3209

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Mele MFP (2015) Human genomics. The human transcriptome across tissues and individuals. Science 348:660–665

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Carithers L.J., A. K. A novel approach to high-quality postmortem tissue procurement: the GTEx project. Biopreserv. Biobank 2015; 13311–13319

  20. Szklarczyk D, Gable AL, Lyon D et al (2019) STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 47(D1):D607–D613. https://doi.org/10.1093/nar/gky1131

    Article  CAS  PubMed  Google Scholar 

  21. Jason Montojo, K. Z. (2014) GeneMANIA: Fast gene network construction and function prediction for Cytoscape. F1000Research

  22. Lopes CT, Franz M, Kazi F et al (2010) Cytoscape Web: an interactive web-based network browser. Bioinformatics (Oxford, England) 26(18):2347–2348. https://doi.org/10.1093/bioinformatics/btq430

    Article  CAS  PubMed  Google Scholar 

  23. Singh S, Seth PK (2019) Functional association between NUCKS1 gene and Parkinson disease: a potential susceptibility biomarker. Bioinformation. 15(8):548–556. https://doi.org/10.6026/97320630015548

    Article  PubMed  PubMed Central  Google Scholar 

  24. Solomon T, Lapek JD, Jensen SB, Greenwald WW, Hindberg K, Matsui H, Latysheva N, Braekken SK, Gonzalez DJ, Frazer KA, Smith EN, Hansen J-B (2018) Identification of common and rare genetic variation associated with plasma protein levels using whole-exome sequencing and mass spectrometry. Circul Genom Precis Med 11(12):e002170

    Article  CAS  Google Scholar 

  25. Al-Khelaifi F, Diboun I, Donati F et al (2019) Metabolic GWAS of elite athletes reveals novel genetically-influenced metabolites associated with athletic performance. Sci Rep 9:19889. https://doi.org/10.1038/s41598-019-56496-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Boyle AP, Hong EL, Hariharan M, Cheng Y, Schaub MA, Kasowski M, Karczewski KJ, Park J, Hitz BC, Weng S, Cherry JM, Snyder M (2012) Annotation of functional variation in personal genomes using RegulomeDB. Genome Res 22:1790–1797

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Gupta PK, Prabhakar S, Abburi C et al (2011) Vascular endothelial growth factor-A and chemokine ligand (CCL2) genes are upregulated in peripheral blood mononuclear cells in Indian amyotrophic lateral sclerosis patients. J Neuroinflammation 8:114

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Vinish M, Prabhakar S, Khullar M, Verma I, Anand A (2010) Genetic screening reveals high frequency of PARK2 mutations and reduced Parkin expression conferring risk for Parkinsonism in North West India. J Neurol Neurosurg Psychiat 81(2):166–170. https://doi.org/10.1136/jnnp.2008.157255 (Epub 2009 Sep 3 PMID: 19734163)

    Article  PubMed  Google Scholar 

  29. Kamal Sharma N, Gupta A, Prabhakar S, Singh R, Sharma S, Anand A (2012) Single nucleotide polymorphism and serum levels of VEGFR2 are associated with age related macular degeneration. Curr Neurovascular Res 9:256

    Article  Google Scholar 

  30. Anand A, Gupta PK, Sharma NK, Prabhakar S (2012) Soluble VEGFR1 (sVEGFR1) as a novel marker of amyotrophic lateral sclerosis (ALS) in the North Indian ALS patients. Eur J Neurol 19(5):788–792

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

We owe deep gratitude to Prof. P.K. Seth, NASI Senior Scientist, Biotech Park, Lucknow, & the C.E.O, Biotech Park, Lucknow, for their encouragement and support in the study. The support provided by Bioinformatics tools, software’s and databases in the work is also gratefully acknowledged.

Funding

No funding has been received for this work.

Author information

Authors and Affiliations

Authors

Contributions

Each author has contributed equally to the study.

Corresponding author

Correspondence to Prachi Srivastava.

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

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Garg, P., Srivastava, N., Seth, P.K. et al. In Silico Study of ULK1 Gene as a Susceptible Biomarker for Neurodegeneration. Proc. Natl. Acad. Sci., India, Sect. B Biol. Sci. 93, 325–335 (2023). https://doi.org/10.1007/s40011-022-01419-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40011-022-01419-2

Keywords

Navigation