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2000
Volume 21, Issue 2
  • ISSN: 1574-8936
  • E-ISSN: 2212-392X

Abstract

Introduction

An uncommon neurological condition known as Guillain-Barre syndrome (GBS) develops when the body's immunological system unintentionally targets peripheral nerves.

Aim

This work aimed to compare scRNA-seq and transcriptome data to find novel gene biomarkers linked to CD4+ T cells and B cells that might potentially be utilized for the diagnosis and assessment of GBS. It aimed to employ scRNA-seq data and bioinformatics tools analysis to identify cell-specific biomarkers for GBS diagnosis and prognosis.

Methods

scRNA-seq and microarray datasets from the GEO database were utilized to identify differentially expressed genes (DEGs). Pathway enrichment, identification of potential hub genes, and gene regulatory studies were employed using FunRich, DAVID, STRING, and NetworkAnalyst tools.

Results

After integrating the DEGs and performing a comparative analysis, it was discovered that there were 84 DEGs shared between scRNA-seq and microarray datasets. The presence of signal transduction, immune system, cytokine signaling, NOD-like receptor signaling, and focal adhesion was detected in the most significant gene ontology and metabolic pathways. After generating a protein-protein interaction (PPI) network, we used eleven topological algorithms of the cytoHubba plugin for identifying six key hub genes, including CDC42, PTPRC, SRSF1, HNRNPA2B1, NIPBL, and FOS. Several crucial transcription factors (CHD1, IRF1, FOXC1, GATA2, YY1, E2F1, and CREB1) and two significant microRNAs (hsa-mir-20a-5p and hsa-mir-16-5p) were also discovered as hub gene regulators. The receiver operating characteristics (ROC) curve was used to evaluate the prognostic, expression, and diagnostic capabilities of the six major hub genes, indicating a good scoring value.

Conclusion

Finally, functional enrichment pathway analysis, PPI, and regulatory networks analysis demonstrated the critical functions of the identified key hub genes. After further wet lab research is validated, our research work may offer useful predicted potential biomarkers for the diagnosis and prognosis of GBS.

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References

  1. BragazziN.L. KolahiA.A. NejadghaderiS.A. Global, regional, and national burden of Guillain–Barré syndrome and its underlying causes from 1990 to 2019.J. Neuroinflammation202118126410.1186/s12974‑021‑02319‑4 34763713
    [Google Scholar]
  2. KuwabaraS. YukiN. Axonal Guillain-Barré syndrome: Concepts and controversies.Lancet Neurol.201312121180118810.1016/S1474‑4422(13)70215‑1 24229616
    [Google Scholar]
  3. WillisonH.J. JacobsB.C. van DoornP.A. Guillain-Barré syndrome.Lancet20163881004571772710.1016/S0140‑6736(16)00339‑1 26948435
    [Google Scholar]
  4. HuangW.C. LuC.L. ChenS.C.C. A 15-year nationwide epidemiological analysis of Guillain-Barré syndrome in Taiwan.Neuroepidemiology201544424925410.1159/000430917 26088600
    [Google Scholar]
  5. ShahrizailaN. LehmannH.C. KuwabaraS. Guillain-Barré syndrome.Lancet2021397102801214122810.1016/S0140‑6736(21)00517‑1 33647239
    [Google Scholar]
  6. OzdemirH.H. Analysis of the albumin level, neutrophil-lymphocyte ratio, and platelet-lymphocyte ratio in Guillain-Barré syndrome.Arq. Neuropsiquiatr.201674971872210.1590/0004‑282X20160132 27706420
    [Google Scholar]
  7. ZhouQ.L. LiZ.K. XuF. Guillain-Barré syndrome and hemophagocytic syndrome heralding the diagnosis of diffuse large B cell lymphoma: A case report.World J. Clin. Cases202210269502950910.12998/wjcc.v10.i26.9502 36159426
    [Google Scholar]
  8. van den BergB. WalgaardC. DrenthenJ. FokkeC. JacobsB.C. van DoornP.A. Guillain–Barré syndrome: Pathogenesis, diagnosis, treatment and prognosis.Nat. Rev. Neurol.201410846948210.1038/nrneurol.2014.121 25023340
    [Google Scholar]
  9. DestefanoF. AnguloF.J. IskanderJ. ShadomyS.V. WeintraubE. ChenR.T. Surveillance for Adverse Events following Immunization from 2008 to 2011 in Zhejiang Province, China.Methods2004292201210.1128/CVI.00541‑12
    [Google Scholar]
  10. RodríguezY. RojasM. PachecoY. Guillain–Barré syndrome, transverse myelitis and infectious diseases.Cell. Mol. Immunol.201815654756210.1038/cmi.2017.142 29375121
    [Google Scholar]
  11. DahleC. VrethemM. ErnerudhJ. T lymphocyte subset abnormalities in peripheral blood from patients with the Guillain-Barré syndrome.J. Neuroimmunol.199453221922510.1016/0165‑5728(94)90032‑9 7520920
    [Google Scholar]
  12. HartungH-P. ToykaK.V. T‐Cell and macrophage activation in experimental autoimmune neuritis and Guillain-Barré syndrome.Ann. Neurol.199027S57S6310.1002/ana.410270716 2194429
    [Google Scholar]
  13. SiegelG.J. AlbersR.W. Basic neurochemistry: Molecular, cellular, and medical aspects.7th EdNew YorkRaven Press19941016
    [Google Scholar]
  14. WanschitzJ. MaierH. LassmannH. BudkaH. BergerT. Distinct time pattern of complement activation and cytotoxic T cell response in Guillain-Barre syndrome.Brain200312692034204210.1093/brain/awg207 12847075
    [Google Scholar]
  15. ChiL. WangH. ZhangY. WangW. Abnormality of circulating CD4+CD25+ regulatory T cell in patients with Guillain–Barré syndrome.J. Neuroimmunol.20071921-220621410.1016/j.jneuroim.2007.09.034 17997492
    [Google Scholar]
  16. SafaA. AzimiT. SayadA. TaheriM. Ghafouri-FardS. A review of the role of genetic factors in Guillain–Barré syndrome.J. Mol. Neurosci.202171590292010.1007/s12031‑020‑01720‑7 33029737
    [Google Scholar]
  17. van DoornP.A. RutsL. JacobsB.C. Clinical features, pathogenesis, and treatment of Guillain-Barré syndrome.Lancet Neurol.200871093995010.1016/S1474‑4422(08)70215‑1 18848313
    [Google Scholar]
  18. LimJ.P. DevauxJ. YukiN. Peripheral nerve proteins as potential autoantigens in acute and chronic inflammatory demyelinating polyneuropathies.Autoimmun. Rev.201413101070107810.1016/j.autrev.2014.08.005 25172243
    [Google Scholar]
  19. WillisonH.J. Biomarkers in experimental models of antibody-mediated neuropathies.J. Peripher. Nerv. Syst.201116Suppl. 1606210.1111/j.1529‑8027.2011.00310.x 21696502
    [Google Scholar]
  20. WillisonH.J. GoodyearC.S. Glycolipid antigens and autoantibodies in autoimmune neuropathies.Trends Immunol.201334945345910.1016/j.it.2013.05.001 23770405
    [Google Scholar]
  21. BlumS. McCombeP.A. Genetics of Guillain-Barré syndrome (GBS) and chronic inflammatory demyelinating polyradiculoneuropathy (CIDP): Current knowledge and future directions.J. Peripher. Nerv. Syst.20141928810310.1111/jns5.12074 25039604
    [Google Scholar]
  22. ChenG. NingB. ShiT. Single-cell RNA-seq technologies and related computational data analysis.Front. Genet.201910APR31710.3389/fgene.2019.00317 31024627
    [Google Scholar]
  23. SúkeníkováL. MalloneA. SchreinerB. Autoreactive T cells target peripheral nerves in Guillain–Barré syndrome.Nature2024626799716016810.1038/s41586‑023‑06916‑6 38233524
    [Google Scholar]
  24. ButlerA. HoffmanP. SmibertP. PapalexiE. SatijaR. Integrating single-cell transcriptomic data across different conditions, technologies, and species.Nat. Biotechnol.201836541142010.1038/nbt.4096 29608179
    [Google Scholar]
  25. LytalN. RanD. AnL. Normalization methods on single-cell RNA-seq data: An empirical survey.Front. Genet.2020114110.3389/fgene.2020.00041
    [Google Scholar]
  26. ColeM.B. RissoD. WagnerA. Performance assessment and selection of normalization procedures for single-cell RNA-Seq.Cell Syst.201984315328.e810.1016/j.cels.2019.03.010 31022373
    [Google Scholar]
  27. TsuyuzakiK. SatoH. SatoK. NikaidoI. Benchmarking principal component analysis for large-scale single-cell RNA-sequencing.Genome Biol.2020211910.1186/s13059‑019‑1900‑3 31955711
    [Google Scholar]
  28. ChangK.H. ChuangT.J. LyuR.K. Identification of gene networks and pathways associated with Guillain-Barré syndrome.PLoS One201271e2950610.1371/journal.pone.0029506 22253732
    [Google Scholar]
  29. BardouP. MarietteJ. EscudiéF. DjemielC. KloppC. Jvenn: An interactive Venn diagram viewer.BMC Bioinformatics201415129310.1186/1471‑2105‑15‑293 25176396
    [Google Scholar]
  30. PathanM. KeerthikumarS. AngC.S. FunRich: An open access standalone functional enrichment and interaction network analysis tool.Proteomics201515152597260110.1002/pmic.201400515 25921073
    [Google Scholar]
  31. ShermanB.T. HaoM. QiuJ. DAVID: A web server for functional enrichment analysis and functional annotation of gene lists (2021 update).Nucleic Acids Res.202250W1W216-2110.1093/nar/gkac194 35325185
    [Google Scholar]
  32. AthanasiosA. CharalamposV. VasileiosT. AshrafG. Protein-protein interaction (PPI) network: Recent advances in drug discovery.Curr. Drug Metab.201718151010.2174/138920021801170119204832 28889796
    [Google Scholar]
  33. SzklarczykD. GableA.L. NastouK.C. The STRING database in 2021: Customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets.Nucleic Acids Res.202149D1D605D61210.1093/nar/gkaa1074 33237311
    [Google Scholar]
  34. ChinC.H. ChenS.H. WuH.H. HoC.W. KoM.T. LinC.Y. cytoHubba: Identifying hub objects and sub-networks from complex interactome.BMC Syst. Biol.20148S4Suppl. 4S1110.1186/1752‑0509‑8‑S4‑S11 25521941
    [Google Scholar]
  35. MitsisT. EfthimiadouA. BacopoulouF. VlachakisD. ChrousosG. EliopoulosE. Transcription factors and evolution: An integral part of gene expression (Review).World Academy of Sciences Journal2020213810.3892/wasj.2020.32
    [Google Scholar]
  36. DeiuliisJ.A. MicroRNAs as regulators of metabolic disease: Pathophysiologic significance and emerging role as biomarkers and therapeutics.Int. J. Obes.20164018810110.1038/ijo.2015.170 26311337
    [Google Scholar]
  37. ZhouG. SoufanO. EwaldJ. HancockR.E.W. BasuN. XiaJ. NetworkAnalyst 3.0: A visual analytics platform for comprehensive gene expression profiling and meta-analysis.Nucleic Acids Res.201947W1W234-4110.1093/nar/gkz240 30931480
    [Google Scholar]
  38. TurckN. VutskitsL. Sanchez-PenaP. A multiparameter panel method for outcome prediction following aneurysmal subarachnoid hemorrhage.Intens Care Med.20113610711510.1007/s00134‑009‑1641‑y
    [Google Scholar]
  39. WangX.M. YikW.Y. ZhangP. The gene expression profiles of induced pluripotent stem cells from individuals with childhood cerebral adrenoleukodystrophy are consistent with proposed mechanisms of pathogenesis.Stem Cell Res. Ther.2012353910.1186/scrt130 23036268
    [Google Scholar]
  40. BrevilleG. SukockieneE. VargasM.I. LascanoA.M. Emerging biomarkers to predict clinical outcomes in Guillain–Barré syndrome.Expert Rev. Neurother.202323121201121510.1080/14737175.2023.2273386 37902064
    [Google Scholar]
  41. Caruso BavisottoC. ScaliaF. Marino GammazzaA. Extracellular vesicle-mediated cell–cell communication in the nervous system: Focus on neurological diseases.Int. J. Mol. Sci.201920243410.3390/ijms20020434 30669512
    [Google Scholar]
  42. RinkC. GörtzenA. VehR.W. PrüssH. Serum antibodies targeting neurons of the monoaminergic systems in Guillain-Barré syndrome.J. Neurol. Sci.201737231832310.1016/j.jns.2016.11.078 28017237
    [Google Scholar]
  43. GaoH. WangS. DuanH. WangY. ZhuH. Biological analysis of the potential pathogenic mechanisms of Infectious COVID-19 and Guillain-Barré syndrome.Front. Immunol.202314129057810.3389/fimmu.2023.1290578 38115996
    [Google Scholar]
  44. DoetsA.Y. VerboonC. van den BergB. Regional variation of Guillain-Barré syndrome.Brain2018141102866287710.1093/brain/awy232 30247567
    [Google Scholar]
  45. BischoffJ.P. SchulzA. MorrisonH. The role of exosomes in intercellular and inter-organ communication of the peripheral nervous system.FEBS Lett.2022596565566410.1002/1873‑3468.14274 34990014
    [Google Scholar]
  46. HagenK.M. OusmanS.S. The neuroimmunology of guillain-barré syndrome and the potential role of an aging immune system.Front. Aging Neurosci.202112January61362810.3389/fnagi.2020.613628 33584245
    [Google Scholar]
  47. ZhuJ. MixE. LinkH. Cytokine production and the pathogenesis of experimental autoimmune neuritis and Guillain–Barré syndrome.J. Neuroimmunol.1998841405210.1016/S0165‑5728(97)00238‑5 9600707
    [Google Scholar]
  48. GriesM. DaviesL. LiuY. Response of Toll-like receptors in experimental Guillain–Barré syndrome: A kinetic analysis.Neurosci. Lett.2012518215416010.1016/j.neulet.2012.04.077 22579825
    [Google Scholar]
  49. WangC. LiaoS. WangY. HuX. XuJ. Computational identification of Guillain-Barré syndrome-related genes by an mRNA gene expression profile and a protein–protein interaction network.Front. Mol. Neurosci.202215March85020910.3389/fnmol.2022.850209 35370550
    [Google Scholar]
  50. RamanK. Construction and analysis of protein–protein interaction networks.Autom. Exp.201021210.1186/1759‑4499‑2‑2 20334628
    [Google Scholar]
  51. SevimogluT. ArgaK.Y. The role of protein interaction networks in systems biomedicine.Comput. Struct. Biotechnol. J.20141118222710.1016/j.csbj.2014.08.008 25379140
    [Google Scholar]
  52. EversE.E. ZondagG.C.M. MalliriA. Rho family proteins in cell adhesion and cell migration.Eur. J. Cancer200036101269127410.1016/S0959‑8049(00)00091‑5 10882865
    [Google Scholar]
  53. LamanJ.D. HuizingaR. BoonsG.J. JacobsB.C. Guillain-Barré syndrome: Expanding the concept of molecular mimicry.Trends Immunol.202243429630810.1016/j.it.2022.02.003 35256276
    [Google Scholar]
  54. SheikhK.A. Autoantobodies activate small GTPase RhoA to modulate neurite outgrowth.Small GTPases20112423323810.4161/sgtp.2.4.17115 22145097
    [Google Scholar]
  55. Al BarashdiM.A. AliA. McMullinM.F. MillsK. Protein tyrosine phosphatase receptor type C (PTPRC or CD45).J. Clin. Pathol.202174954855210.1136/jclinpath‑2020‑206927 34039664
    [Google Scholar]
  56. Costa Lima de SouzaD. Basílio GuimarãesR. Alves de Siqueira CarvalhoA. Guillain-Barre syndrome related to COVID-19: Muscle and nerve biopsy findings.J. Hum. Growth Dev.202131346546910.36311/jhgd.v31.12183
    [Google Scholar]
  57. NingK. Sandoval-CastellanosA.M. BhargavaA. ZhaoM. XuJ. Serine and arginine rich splicing factor 1: A potential target for neuroprotection and other diseases.Neural Regen. Res.20231871411141610.4103/1673‑5374.360243 36571335
    [Google Scholar]
  58. MartinezF.J. PrattG.A. Van NostrandE.L. Protein-RNA networks regulated by normal and ALS-associated mutant HNRNPA2B1 in the nervous system.Neuron201692478079510.1016/j.neuron.2016.09.050 27773581
    [Google Scholar]
  59. PuisacB. Teresa-RodrigoM.E. Hernández-MarcosM. mRNA quantification of NIPBL isoforms A and B in adult and fetal human tissues, and a potentially pathological variant affecting only isoform a in two patients with Cornelia de Lange syndrome.Int. J. Mol. Sci.201718348110.3390/ijms18030481 28241484
    [Google Scholar]
  60. SkeneP.J. HernandezA.E. GroudineM. HenikoffS. The nucleosomal barrier to promoter escape by RNA polymerase II is overcome by the chromatin remodeler Chd1.eLife20143e0204210.7554/eLife.02042 24737864
    [Google Scholar]
  61. Guzman-AyalaM. SachsM. KohF.M. Chd1 is essential for the high transcriptional output and rapid growth of the mouse epiblast.Development2015142111812710.1242/dev.114843 25480920
    [Google Scholar]
  62. WeiY. QiK. YuY. Analysis of differentially expressed genes in the dentate gyrus and anterior cingulate cortex in a mouse model of depression.BioMed Res. Int.2021202111710.1155/2021/5013565 33628784
    [Google Scholar]
  63. WatanabeT. AsanoN. Fichtner-FeiglS. NOD1 contributes to mouse host defense against Helicobacter pylori via induction of type I IFN and activation of the ISGF3 signaling pathway.J. Clin. Invest.201012051645166210.1172/JCI39481 20389019
    [Google Scholar]
  64. ZaheerR.S. ProudD. Human rhinovirus-induced epithelial production of CXCL10 is dependent upon IFN regulatory factor-1.Am. J. Respir. Cell Mol. Biol.201043441342110.1165/rcmb.2009‑0203OC 19880820
    [Google Scholar]
  65. RenZ. WangY. LiebensonD. IRF-1 signaling in central nervous system glial cells regulates inflammatory demyelination.J. Neuroimmunol.20112331-214715910.1016/j.jneuroim.2011.01.001 21257209
    [Google Scholar]
  66. KenzelS. Santos-SierraS. DeshmukhS.D. Role of p38 and early growth response factor 1 in the macrophage response to group B streptococcus.Infect. Immun.20097762474248110.1128/IAI.01343‑08 19332535
    [Google Scholar]
  67. LiJ. WangY. MengX. LiangH. Modulation of transcriptional activity in brain lower grade glioma by alternative splicing.PeerJ20186MAYe468610.7717/peerj.4686 29780667
    [Google Scholar]
  68. HanB. BhowmickN. QuY. ChungS. GiulianoA.E. CuiX. FOXC1: An emerging marker and therapeutic target for cancer.Oncogene201736283957396310.1038/onc.2017.48 28288141
    [Google Scholar]
  69. XiaL. HuangW. TianD. Overexpression of forkhead box C1 promotes tumor metastasis and indicates poor prognosis in hepatocellular carcinoma.Hepatology201357261062410.1002/hep.26029 22911555
    [Google Scholar]
  70. KurzawskiM. BiałeckaM. SławekJ. Kłodowska-DudaG. DroździkM. Association study of GATA-2 transcription factor gene (GATA2) polymorphism and Parkinson’s disease.Parkinsonism Relat. Disord.201016428428710.1016/j.parkreldis.2009.10.006 19864173
    [Google Scholar]
  71. HeY. Casaccia-BonnefilP. The Yin and Yang of YY1 in the nervous system.J. Neurochem.200810641493150210.1111/j.1471‑4159.2008.05486.x 18485096
    [Google Scholar]
  72. LuL. LiuL.P. GuiR. Discovering common pathogenetic processes between COVID-19 and sepsis by bioinformatics and system biology approach.Front. Immunol.202213August97584810.3389/fimmu.2022.975848 36119022
    [Google Scholar]
  73. AhmedF.F. RezaS. SarkerS. Identification of host transcriptome-guided repurposable drugs for SARS-CoV-1 infections and their validation with SARS-CoV-2 infections by using the integrated bioinformatics approaches.PLoS One2022174e026612410.1371/journal.pone.0266124 35390032
    [Google Scholar]
  74. FauciA.S. MorensD.M. Zika virus in the americas — yet another arbovirus threat.N. Engl. J. Med.2016374760160410.1056/NEJMp1600297 26761185
    [Google Scholar]
  75. TangH. HammackC. OgdenS.C. Zika virus infects human cortical neural progenitors and attenuates their growth.Cell Stem Cell201618558759010.1016/j.stem.2016.02.016 26952870
    [Google Scholar]
  76. WuJ. SabirzhanovB. StoicaB.A. Ablation of the transcription factors E2F1-2 limits neuroinflammation and associated neurological deficits after contusive spinal cord injury.Cell Cycle201514233698371210.1080/15384101.2015.1104436 26505089
    [Google Scholar]
  77. SuL. SongX. XueZ. ZhengC. YinH. WeiH. Network analysis of microRNAs, transcription factors, and target genes involved in axon regeneration.J. Zhejiang Univ. Sci. B201819429330410.1631/jzus.B1700179 29616505
    [Google Scholar]
  78. LiuL. ZhangY. ChenY. Therapeutic prospects of ceRNAs in COVID-19.Front. Cell. Infect. Microbiol.202212September99874810.3389/fcimb.2022.998748 36204652
    [Google Scholar]
  79. DengX. LuoY. GuanT. GuoX. Identification of the genetic influence of SARS-CoV-2 infections on IgA nephropathy based on bioinformatics method.Kidney Blood Press. Res.202348136738410.1159/000529687 37040729
    [Google Scholar]
  80. KimW.R. ParkE.G. KangK.W. LeeS.M. KimB. KimH.S. Expression analyses of micrornas in hamster lung tissues infected by SARS-CoV-2.Mol. Cells2020431195396310.14348/molcells.2020.0177 33199671
    [Google Scholar]
  81. AsadiM.R. TalebiM. GharesouranJ. Analysis of ROQUIN, Tristetraprolin (TTP), and BDNF/miR-16/TTP regulatory axis in late onset Alzheimer’s disease.Front. Aging Neurosci.202214August93301910.3389/fnagi.2022.933019 36016853
    [Google Scholar]
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  • Article Type:
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Keyword(s): CDC42; FOXC1; Guillain-Barre syndrome; hsa-mir-16-5p; hub gene; pathway; ROC curve
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