Skip to main content

Advertisement

Log in

Key Genes in Olfactory Disorder in Experimental Autoimmune Encephalomyelitis Identified by Transcriptomic Analysis of the Olfactory Bulbs

  • Published:
Molecular Neurobiology Aims and scope Submit manuscript

Abstract

Experimental autoimmune encephalomyelitis (EAE) is an animal model of multiple sclerosis that shows demyelination in the central nervous system and functional deficits, including olfactory impairment. However, the genes related to olfactory impairment in EAE are unknown. We evaluated hub genes of the olfactory bulb in EAE mice. Differentially expressed genes (cut-offs, fold change > 2 and adjusted p < 0.05) and their related pathways in olfactory bulbs were subjected to gene ontology (GO) pathway analysis, gene set enrichment analysis (GSEA). Protein-protein interactions with selected genes were evaluated using the Search Tool for the Retrieval of Interacting Genes/Proteins. Gene regulatory networks (GRNs) which were constructed at the post-transcriptional level, including the genes-transcription factors (TFs) and gene-microRNAs (miRNAs) interaction networks. Twelve hub genes were found, three of which (Ctss, Itgb2, and Tlr2) were validated by RT-qPCR to be related to GO pathways such as immune response and regulation of immune response. GSEA showed that neuron-related genes—including Atp6v1g2, Egr1, and Gap43—and their pathways were significantly downregulated. GRNs analysis of six genes (Ctss, Itgb2, Tlr2, Atp6v1g2, Egr1, and Gap43) revealed 37 TFs and 84 miRNAs were identified as potential regulators of six genes, indicating significant interaction among six genes, TFs, and miRNAs. Collectively, these results suggest that transcriptomic analysis of the olfactory bulb of EAE mice can provide insight into olfactory dysfunction and reveal therapeutic targets for olfactory impairment.

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
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data Availability

Data will be made available on request.

Abbreviations

BP:

Biological process

CC:

Cellular component

cDNA:

Complementary deoxyribonucleic acid

CNS:

Central nervous system

DEG:

Differentially expressed gene

EAE:

Experimental autoimmune encephalomyelitis

FDR:

False discovery rate

GAPDH:

Glyceraldehyde 3-phosphate dehydrogenase

GO:

Gene ontology

GSEA:

Gene set enrichment analysis

Iba1:

Ionized calcium-binding adapter molecule 1

KEGG:

Kyoto encyclopedia of genes and genomes

MCC:

Maximal clique centrality

MF:

Molecular function

MHC:

Major histocompatibility complex

MNC:

Maximum neighborhood component

MOG:

Myelin oligodendrocyte glycoprotein

MS:

Multiple sclerosis

MSigDB:

Molecular signatures database

NOM:

Nominal

PC:

Principal component

PCA:

Principal component analysis

References

  1. Shin T, Ahn M, Matsumoto Y (2012) Mechanism of experimental autoimmune encephalomyelitis in Lewis rats: recent insights from macrophages. Anat Cell Biol 45(3):141–148. https://doi.org/10.5115/acb.2012.45.3.141

    Article  PubMed  PubMed Central  Google Scholar 

  2. Shin T, Kojima T, Tanuma N, Ishihara Y, Matsumoto Y (1995) The subarachnoid space as a site for precursor T cell proliferation and effector T cell selection in experimental autoimmune encephalomyelitis. J Neuroimmunol 56(2):171–178. https://doi.org/10.1016/0165-5728(94)00144-d

    Article  CAS  PubMed  Google Scholar 

  3. Shrestha B, Jiang X, Ge S, Paul D, Chianchiano P, Pachter JS (2017) Spatiotemporal resolution of spinal meningeal and parenchymal inflammation during experimental autoimmune encephalomyelitis. Neurobiol Dis 108:159–172. https://doi.org/10.1016/j.nbd.2017.08.010

    Article  CAS  PubMed  Google Scholar 

  4. Villarroya H, Violleau K, Ben Younes-Chennoufi A, Baumann N (1996) Myelin-induced experimental allergic encephalomyelitis in Lewis rats: tumor necrosis factor alpha levels in serum and cerebrospinal fluid immunohistochemical expression in glial cells and macrophages of optic nerve and spinal cord. J Neuroimmunol 64(1):55–61. https://doi.org/10.1016/0165-5728(95)00151-4

    Article  CAS  PubMed  Google Scholar 

  5. Goektas O, Schmidt F, Bohner G, Erb K, Ludemann L, Dahlslett B, Harms L, Fleiner F (2011) Olfactory bulb volume and olfactory function in patients with multiple sclerosis. Rhinology 49(2):221–226. https://doi.org/10.4193/Rhino10.136

    Article  CAS  PubMed  Google Scholar 

  6. Caminiti F, De Salvo S, De Cola MC, Russo M, Bramanti P, Marino S, Ciurleo R (2014) Detection of olfactory dysfunction using olfactory event related potentials in young patients with multiple sclerosis. PLoS One 9(7):e103151. https://doi.org/10.1371/journal.pone.0103151

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Kapadia M, Stanojcic M, Earls AM, Pulapaka S, Lee J, Sakic B (2012) Altered olfactory function in the MRL model of CNS lupus. Behav Brain Res 234(2):303–311. https://doi.org/10.1016/j.bbr.2012.07.005

    Article  PubMed  Google Scholar 

  8. Gaillard I, Rouquier S, Giorgi D (2004) Olfactory receptors. Cell Mol Life Sci 61(4):456–469. https://doi.org/10.1007/s00018-003-3273-7

    Article  CAS  PubMed  Google Scholar 

  9. Kovács T (2004) Mechanisms of olfactory dysfunction in aging and neurodegenerative disorders. Ageing Res Rev 3(2):215–232. https://doi.org/10.1016/j.arr.2003.10.003

    Article  PubMed  Google Scholar 

  10. Kim J, Ahn M, Choi Y, Shin T (2020) Upregulation of cathepsins in olfactory bulbs is associated with transient olfactory dysfunction in mice with experimental autoimmune encephalomyelitis. Molecular neurobiology 57(8):3412–3423. https://doi.org/10.1007/s12035-020-01952-z

    Article  CAS  PubMed  Google Scholar 

  11. Kim J, Ahn M, Choi Y, Ekanayake P, Park CM, Moon C, Jung K, Tanaka A, Matsuda H, Shin T (2019) Gene expression profile of olfactory transduction signaling in an animal model of human multiple sclerosis. Exp Neurobiol 28(1):74–84. https://doi.org/10.5607/en.2019.28.1.74

    Article  PubMed  PubMed Central  Google Scholar 

  12. Kim J, Choi Y, Ahn M, Jung K, Shin T (2018) Olfactory dysfunction in autoimmune central nervous system neuroinflammation. Molecular neurobiology 55(11):8499–8508. https://doi.org/10.1007/s12035-018-1001-4

    Article  CAS  PubMed  Google Scholar 

  13. Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9(4):357–359. https://doi.org/10.1038/nmeth.1923

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15(12):550. https://doi.org/10.1186/s13059-014-0550-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Team RC (2013) R: a language and environment for statistical computing.

  16. Ge SX, Jung D, Yao R (2020) ShinyGO: a graphical gene-set enrichment tool for animals and plants. Bioinformatics 36(8):2628–2629. https://doi.org/10.1093/bioinformatics/btz931

    Article  CAS  PubMed  Google Scholar 

  17. Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, Doncheva NT, Legeay M, Fang T, Bork P, Jensen LJ, von Mering C (2021) The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res 49(D1):D605–D612. https://doi.org/10.1093/nar/gkaa1074

    Article  CAS  PubMed  Google Scholar 

  18. Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY (2014) cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 8:S11. https://doi.org/10.1186/1752-0509-8-s4-s11

    Article  PubMed  PubMed Central  Google Scholar 

  19. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504. https://doi.org/10.1101/gr.1239303

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102(43):15545–15550. https://doi.org/10.1073/pnas.0506580102

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Zhou G, Soufan O, Ewald J, Hancock REW, Basu N, Xia J (2019) NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res 47(W1):W234–w241. https://doi.org/10.1093/nar/gkz240

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Karagkouni D, Paraskevopoulou MD, Chatzopoulos S, Vlachos IS, Tastsoglou S, Kanellos I, Papadimitriou D, Kavakiotis I, Maniou S, Skoufos G, Vergoulis T, Dalamagas T, Hatzigeorgiou AG (2018) DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA-gene interactions. Nucleic Acids Res 46(D1):D239–d245. https://doi.org/10.1093/nar/gkx1141

    Article  CAS  PubMed  Google Scholar 

  23. Chen Y, Wang X (2020) miRDB: an online database for prediction of functional microRNA targets. Nucleic Acids Res 48(D1):D127–d131. https://doi.org/10.1093/nar/gkz757

    Article  CAS  PubMed  Google Scholar 

  24. Janky R, Verfaillie A, Imrichová H, Van de Sande B, Standaert L, Christiaens V, Hulselmans G, Herten K, Naval Sanchez M, Potier D, Svetlichnyy D, Kalender Atak Z, Fiers M, Marine JC, Aerts S (2014) iRegulon: from a gene list to a gene regulatory network using large motif and track collections. PLoS Comput Biol 10(7):e1003731. https://doi.org/10.1371/journal.pcbi.1003731

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Consortium EP (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489(7414):57

    Article  Google Scholar 

  26. Reichardt HM, Lühder F (2012) The ambivalent role of apoptosis in experimental autoimmune encephalomyelitis and multiple sclerosis. Curr Pharm Des 18(29):4453–4464. https://doi.org/10.2174/138161212802502224

    Article  CAS  PubMed  Google Scholar 

  27. Tepavčević V, Lazarini F, Alfaro-Cervello C, Kerninon C, Yoshikawa K, Garcia-Verdugo JM, Lledo PM, Nait-Oumesmar B, Baron-Van Evercooren A (2011) Inflammation-induced subventricular zone dysfunction leads to olfactory deficits in a targeted mouse model of multiple sclerosis. J Clin Invest 121(12):4722–4734. https://doi.org/10.1172/jci59145

    Article  PubMed  PubMed Central  Google Scholar 

  28. Erkal B, Vural Korkut S (2022) Identification of miRNAs and their potential effects on multiple sclerosis related pathways using ın silico analysis. Mult Scler Relat Disord 59:103642. https://doi.org/10.1016/j.msard.2022.103642

    Article  CAS  PubMed  Google Scholar 

  29. Weerasinghe-Mudiyanselage PDE, Kang S, Kim JS, Kim JC, Kim SH, Wang H, Shin T, Moon C (2022) Transcriptome profiling in the hippocampi of mice with experimental autoimmune encephalomyelitis. Int J Mol Sci 23(23). https://doi.org/10.3390/ijms232314829

  30. Wang C, Zhou W, Su G, Hu J, Yang P (2022) Progranulin Suppressed autoimmune uveitis and autoimmune neuroinflammation by inhibiting Th1/Th17 cells and promoting treg cells and M2 macrophages. Neurol Neuroimmunol Neuroinflamm 9(2). https://doi.org/10.1212/nxi.0000000000001133

  31. Liu SH, Wang YL, Jiang SM, Wan XJ, Yan JH, Liu CF (2022) Identifying the hub gene and immune infiltration of Parkinson’s disease using bioinformatical methods. Brain Res 1785:147879. https://doi.org/10.1016/j.brainres.2022.147879

    Article  CAS  PubMed  Google Scholar 

  32. Liu C, Xu S, Liu Q, Chai H, Luo Y, Li S (2023) Identification of immune cells infiltrating in hippocampus and key genes associated with Alzheimer’s disease. BMC Med Genomics 16(1):53. https://doi.org/10.1186/s12920-023-01458-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Shippy DC, Ulland TK (2023) Genome-wide identification of murine interferon genes in microglial-mediated neuroinflammation in Alzheimer’s disease. J Neuroimmunol 375:578031. https://doi.org/10.1016/j.jneuroim.2023.578031

    Article  CAS  PubMed  Google Scholar 

  34. Xie Y, Luo X, He H, Tang M (2021) Novel insight into the role of immune dysregulation in amyotrophic lateral sclerosis based on bioinformatic analysis. Front Neurosci 15:657465. https://doi.org/10.3389/fnins.2021.657465

    Article  PubMed  PubMed Central  Google Scholar 

  35. Benowitz LI, Routtenberg A (1997) GAP-43: an intrinsic determinant of neuronal development and plasticity. Trends Neurosci 20(2):84–91. https://doi.org/10.1016/s0166-2236(96)10072-2

    Article  CAS  PubMed  Google Scholar 

  36. Mason JL, Angelastro JM, Ignatova TN, Kukekov VG, Lin G, Greene LA, Goldman JE (2005) ATF5 regulates the proliferation and differentiation of oligodendrocytes. Mol Cell Neurosci 29(3):372–380. https://doi.org/10.1016/j.mcn.2005.03.004

    Article  CAS  PubMed  Google Scholar 

  37. Greene LA, Lee HY, Angelastro JM (2009) The transcription factor ATF5: role in neurodevelopment and neural tumors. J Neurochem 108(1):11–22. https://doi.org/10.1111/j.1471-4159.2008.05749.x

    Article  CAS  PubMed  Google Scholar 

  38. Angelastro JM, Mason JL, Ignatova TN, Kukekov VG, Stengren GB, Goldman JE, Greene LA (2005) Downregulation of activating transcription factor 5 is required for differentiation of neural progenitor cells into astrocytes. The Journal of neuroscience: the official journal of the Society for Neuroscience 25(15):3889–3899. https://doi.org/10.1523/jneurosci.3447-04.2005

    Article  CAS  PubMed  Google Scholar 

  39. Yang T, Zhang Y, Chen L, Thomas ER, Yu W, Cheng B, Li X (2023) The potential roles of ATF family in the treatment of Alzheimer’s disease. Biomed Pharmacother 161:114544. https://doi.org/10.1016/j.biopha.2023.114544

    Article  CAS  PubMed  Google Scholar 

  40. Hernández IH, Torres-Peraza J, Santos-Galindo M, Ramos-Morón E, Fernández-Fernández MR, Pérez-Álvarez MJ, Miranda-Vizuete A, Lucas JJ (2017) The neuroprotective transcription factor ATF5 is decreased and sequestered into polyglutamine inclusions in Huntington’s disease. Acta Neuropathol 134(6):839–850. https://doi.org/10.1007/s00401-017-1770-2

    Article  CAS  PubMed  Google Scholar 

  41. Li WX, Li GH, Tong X, Yang PP, Huang JF, Xu L, Dai SX (2020) Systematic metabolic analysis of potential target, therapeutic drug, diagnostic method and animal model applicability in three neurodegenerative diseases. Aging 12(10):9882–9914. https://doi.org/10.18632/aging.103253

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Dos Santos N, Novaes LS, Dragunas G, Rodrigues JR, Brandão W, Camarini R, Peron JPS, Munhoz CD (2019) High dose of dexamethasone protects against EAE-induced motor deficits but impairs learning/memory in C57BL/6 mice. Scientific reports 9(1):6673. https://doi.org/10.1038/s41598-019-43217-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Becquart P, Vilariño-Güell C, Quandt JA (2020) Enhanced expression of complement and microglial-specific genes prior to clinical progression in the MOG-experimental autoimmune encephalomyelitis model of multiple sclerosis. Brain Res Bull 165:63–69. https://doi.org/10.1016/j.brainresbull.2020.09.010

    Article  CAS  PubMed  Google Scholar 

  44. Cocchiaro P, De Pasquale V, Della Morte R, Tafuri S, Avallone L, Pizard A, Moles A, Pavone LM (2017) The multifaceted role of the lysosomal protease cathepsins in kidney disease. Front Cell Dev Biol 5:114. https://doi.org/10.3389/fcell.2017.00114

    Article  PubMed  PubMed Central  Google Scholar 

  45. Lutzner N, Kalbacher H (2008) Quantifying cathepsin S activity in antigen presenting cells using a novel specific substrate. J Biol Chem 283(52):36185–36194. https://doi.org/10.1074/jbc.M806500200

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Baugh M, Black D, Westwood P, Kinghorn E, McGregor K, Bruin J, Hamilton W, Dempster M, Claxton C, Cai J, Bennett J, Long C, McKinnon H, Vink P, den Hoed L, Gorecka M, Vora K, Grant E, Percival MD et al (2011) Therapeutic dosing of an orally active, selective cathepsin S inhibitor suppresses disease in models of autoimmunity. J Autoimmun 36(3-4):201–209. https://doi.org/10.1016/j.jaut.2011.01.003

    Article  CAS  PubMed  Google Scholar 

  47. Arnaout MA (1990) Structure and function of the leukocyte adhesion molecules CD11/CD18. Blood 75(5):1037–1050

    Article  CAS  PubMed  Google Scholar 

  48. Geng XD, Wang WW, Feng Z, Liu R, Cheng XL, Shen WJ, Dong ZY, Cai GY, Chen XM, Hong Q, Wu D (2019) Identification of key genes and pathways in diabetic nephropathy by bioinformatics analysis. J Diabetes Investig 10(4):972–984. https://doi.org/10.1111/jdi.12986

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Wasko NJ, Nichols F, Clark RB (2020) Multiple sclerosis, the microbiome, TLR2, and the hygiene hypothesis. Autoimmun Rev 19(1):102430. https://doi.org/10.1016/j.autrev.2019.102430

    Article  CAS  PubMed  Google Scholar 

  50. Yuan D, Huang B, Gu M, Qin B-E, Su Z, Dai K, Peng F-H, Jiang Y (2023) Exploring shared genetic signatures of Alzheimer’s disease and multiple sclerosis: a bioinformatic analysis study. European Neurology

  51. Kiefer L, Chiosso A, Langen J, Buckley A, Gaudin S, Rajkumar SM, Servito GIF, Cha ES, Vijay A, Yeung A, Horta A, Mui MH, Canzio D (2023) WAPL functions as a rheostat of Protocadherin isoform diversity that controls neural wiring. Science 380(6651):eadf8440. https://doi.org/10.1126/science.adf8440

    Article  CAS  PubMed  Google Scholar 

  52. Coleman JH, Lin B, Schwob JE (2017) Dissecting LSD1-dependent neuronal maturation in the olfactory epithelium. J Comp Neurol 525(16):3391–3413. https://doi.org/10.1002/cne.24259

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Takaki E, Fujimoto M, Sugahara K, Nakahari T, Yonemura S, Tanaka Y, Hayashida N, Inouye S, Takemoto T, Yamashita H, Nakai A (2006) Maintenance of olfactory neurogenesis requires HSF1, a major heat shock transcription factor in mice. J Biol Chem 281(8):4931–4937. https://doi.org/10.1074/jbc.M506911200

    Article  CAS  PubMed  Google Scholar 

  54. Peng Q, Mechanic J, Shoieb A, Pardo ID, Schaevitz L, Fenyk-Melody J, Vitsky A, Boucher M, Somps C, Cook JC, Liu CN (2019) Circulating microRNA and automated motion analysis as novel methods of assessing chemotherapy-induced peripheral neuropathy in mice. PLoS One 14(1):e0210995. https://doi.org/10.1371/journal.pone.0210995

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Paraboschi EM, Soldà G, Gemmati D, Orioli E, Zeri G, Benedetti MD, Salviati A, Barizzone N, Leone M, Duga S, Asselta R (2011) Genetic association and altered gene expression of mir-155 in multiple sclerosis patients. Int J Mol Sci 12(12):8695–8712. https://doi.org/10.3390/ijms12128695

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Jevtić B, Timotijević G, Stanisavljević S, Momčilović M, Mostarica Stojković M, Miljković D (2015) Micro RNA-155 participates in re-activation of encephalitogenic T cells. Biomed Pharmacother 74:206–210. https://doi.org/10.1016/j.biopha.2015.08.011

    Article  CAS  PubMed  Google Scholar 

Download references

Author Contributions

Conceptualization, T.S. and C.M.; methodology, J.K., K.J., and M.A.; investigation, T.S., S.H., and J.K.; writing—original draft, S.H.; writing—review and editing, C.M., C.A., and P.S.Q.; funding acquisition, T.S.

Funding

This research was supported by the 2024 scientific promotion program funded by Jeju National University. The funders had no role in the design, analysis, write-up, or decision to submit for publication.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Taekyun Shin.

Ethics declarations

Ethics Approval

All experimental procedures were conducted in accordance with the Guidelines for the Care and Use of Laboratory Animals of Jeju National University (Nos. 2017-0019, 2022-0062). The animal protocols also conformed to the National Institutes of Health Guide for the Care and Use of Laboratory Animals (No. 85-23; 1985, rev. 1996).

Consent to Participate

Not applicable

Consent for Publication

Not applicable

Competing Interests

The authors declare no competing interests.

Additional information

Publisher’s Note

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

Supplementary Information

ESM 1

(DOCX 19 kb)

ESM 2

(DOCX 21 kb)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) 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

Hong, S., Kim, J., Ahn, M. et al. Key Genes in Olfactory Disorder in Experimental Autoimmune Encephalomyelitis Identified by Transcriptomic Analysis of the Olfactory Bulbs. Mol Neurobiol (2024). https://doi.org/10.1007/s12035-024-03923-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12035-024-03923-0

Keywords

Navigation