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Integrated Bioinformatics Analysis for the Identification of Key Molecules and Pathways in the Hippocampus of Rats After Traumatic Brain Injury

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Abstract

High-throughput and bioinformatics technology have been broadly applied to demonstrate the key molecules involved in traumatic brain injury (TBI), while no study has integrated the available TBI-related datasets for analysis. In this study, four available expression datasets of fluid percussion injury (FPI) and sham samples from the hippocampus of rats were analysed. A total of 248 differentially expressed genes (DEGs) and 10 differentially expressed microRNAs (DEMIs) were identified. Then, functional annotation was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Most of the DEGs were enriched for the term inflammatory immune response. The MCODE plug-in in the Cytoscape software was applied to build a protein–protein interaction (PPI) network, and 18 hub genes were demonstrated to be enriched in the cell cycle pathway. Besides, time sequence (3 h, 6 h, 12 h, 24 h, and 48 h) profile analysis was performed using short time-series expression miner (STEM). The significantly expressed genes were assigned into 24 pattern clusters with four significant uptrend clusters. Four DEGs, Fcgr2a, Bcl2a1, Cxcl16, and Gbp2, were found to be differentially expressed at all time-points. Fifty-three DEGs and eight DEMIs were identified to form a miRNA-mRNA negative regulatory network using miRWalk3.0 and Cytoscape. Moreover, the mRNA levels of eight hub genes were validated by qRT-PCR. These DEGs, DEMIs, and time-dependent expression patterns facilitate our knowledge of the molecular mechanisms underlying the process of TBI in the hippocampus of rats and have the potential to improve the diagnosis and treatment of TBI.

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References

  1. Johnson WD, Griswold DP (2017) Traumatic brain injury: a global challenge. Lancet Neurol 16(12):949. https://doi.org/10.1016/s1474-4422(17)30362-9

    Article  PubMed  Google Scholar 

  2. Maas AIR, Menon DK, Adelson PD et al (2017) Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research. Lancet Neurol 16:987–1048. https://doi.org/10.1016/S1474-4422(17)30371-X

    Article  PubMed  Google Scholar 

  3. Simon DW, McGeachy MJ, Baylr H et al (2017) The far-reaching scope of neuroinflammation after traumatic brain injury. Nat Rev Neurol 13:171–191

    Article  PubMed  PubMed Central  Google Scholar 

  4. McInnes K, Friesen CL, MacKenzie DE et al (2017) Mild traumatic brain injury (mTBI) and chronic cognitive impairment: a scoping review. PLoS ONE 12(4):e0174847

    Article  PubMed  PubMed Central  Google Scholar 

  5. McGinn MJ, Povlishock JT (2016) Pathophysiology of traumatic brain injury. Neurosurg Clin N Am 27(4):397–407

    Article  PubMed  Google Scholar 

  6. Di PV, Yakoub KM, Scarpa U et al (2018) MicroRNA signature of traumatic brain injury: from the biomarker discovery to the point-of-care. Front Neurol 9:429

    Article  Google Scholar 

  7. White TE, Ford GD, Surles-Zeigler MC et al (2013) Gene expression patterns following unilateral traumatic brain injury reveals a local pro-inflammatory and remote anti-inflammatory response. BMC Genomics 14(1):282. https://doi.org/10.1186/1471-2164-14-282

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Michael DB, Byers DM, Irwin LN (2005) Gene expression following traumatic brain injury in humans: analysis by microarray. J Clin Neurosci 12(3):284–290. https://doi.org/10.1016/j.jocn.2004.11.003

    Article  CAS  PubMed  Google Scholar 

  9. Algattas H, Huang JH (2013) Traumatic brain injury pathophysiology and treatments: early, intermediate, and late phases post-injury. Int J Mol Sci 15(1):309–341

    Article  PubMed  PubMed Central  Google Scholar 

  10. Jassam YN, Izzy S, Whalen M et al (2017) Neuroimmunology of traumatic brain injury: time for a paradigm shift. Neuron 95(6):1246–1265

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Sun TY, Chen XR, Liu ZL et al (2014) Expression profiling of MicroRNAs in hippocampus of rats following traumatic brain injury. J Huazhong Univ Sci Technol: Med Sci 34(4):548–553. https://doi.org/10.1007/s11596-014-1313-1

    Article  CAS  Google Scholar 

  12. Barrett T, Suzek TO, Troup DB et al (2005) NCBI GEO: mining millions of expression profiles—database and tools. Nucleic Acids Res. 33:D562–D566. https://doi.org/10.1093/nar/gki022

    Article  CAS  PubMed  Google Scholar 

  13. Sean D, Meltzer PS (2007) GEOquery: a bridge between the gene expression omnibus (GEO) and BioConductor. Bioinformatics 23(14):1846–1847. https://doi.org/10.1093/bioinformatics/btm254

    Article  CAS  Google Scholar 

  14. Harris MA, Clark JI, Ireland A et al (2006) The gene ontology (GO) project in 2006. Nucleic Acids Res 34:D322–D326. https://doi.org/10.1093/nar/gkj021

    Article  CAS  Google Scholar 

  15. Ogata H, Goto S, Sato K et al (1999) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 27(1):29–34

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Huang DW, Sherman BT, Lempicki RA (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37(1):1–13. https://doi.org/10.1093/nar/gkn923

    Article  CAS  Google Scholar 

  17. Huang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4(1):44. https://doi.org/10.1038/nprot.2008.211

    Article  CAS  Google Scholar 

  18. Szklarczyk D, Franceschini A, Wyder S et al (2015) STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res https://doi.org/10.1093/nar/gku1003

    Article  PubMed  PubMed Central  Google Scholar 

  19. Shannon P, Markiel A, Ozier O et al (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. Bader GD, Hogue CWV (2003) An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4(1):2

    Article  PubMed  PubMed Central  Google Scholar 

  21. Ernst J, Bar-Joseph Z (2006) STEM: A tool for the analysis of short time series gene expression data. BMC Bioinformatics 7(1):191. https://doi.org/10.1186/1471-2105-7-191

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Sticht C, De La Torre C, Parveen A, Gretz N (2018) Mirwalk: an online resource for prediction of microrna binding sites. PLoS ONE13(10). https://doi.org/10.1371/journal.pone.0206239

  23. Xiao X, Jiang Y, Liang W, Wang Y, Cao S, Yan H, Gao L, Zhang L (2019) miR-212-5p attenuates ferroptotic neuronal death after traumatic brain injury by targeting Ptgs2. Mol Brain 12(1):78. https://doi.org/10.1186/s13041-019-0501-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Thomson DW, Dinger ME (2016) Endogenous microRNA sponges: evidence and controversy. Nat Rev Genet 17:272–283. https://doi.org/10.1038/nrg.2016.20

    Article  CAS  PubMed  Google Scholar 

  25. Chen Z, Wang H, Zhong J et al (2019) Significant changes in circular RNA in the mouse cerebral cortex around an injury site after traumatic brain injury. Exp Neurol 313:37–48. https://doi.org/10.1016/j.expneurol.2018.12.003

    Article  CAS  PubMed  Google Scholar 

  26. Xie B, Wang Y, Lin Y et al (2018) Circular RNA expression profiles alter significantly after traumatic brain injury in rats. J Neurotrauma 35(14):1659–1666. https://doi.org/10.1089/neu.2017.5468

    Article  PubMed  Google Scholar 

  27. Karnati HK, Garcia JH, Tweedie D et al (2018) Neuronal enriched extracellular vesicle proteins as biomarkers for traumatic brain injury. J Neurotrauma 36(7):975–987. https://doi.org/10.1089/neu.2018.5898

    Article  PubMed  Google Scholar 

  28. Goetzl EJ, Elahi FM, Mustapic M et al (2019) Altered levels of plasma neuron-derived exosomes and their cargo proteins characterize acute and chronic mild traumatic brain injury. FASEB J 33(4):5082–5088. https://doi.org/10.1096/fj.201802319R

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Ou S, Liu GD, Zhou LS et al (2014) Bioinformatics analysis of gene expression profiles in the rat cerebral cortex following traumatic brain injury. Eur Rev Med Pharmacol Sci 18(1):101–107

    CAS  PubMed  Google Scholar 

  30. Taklimie FR, Gasterich N, Scheld M et al (2019) Hypoxia induces astrocyte-derived lipocalin-2 in ischemic stroke. Int J Mol Sci 20(6):1271. https://doi.org/10.3390/ijms20061271

    Article  CAS  Google Scholar 

  31. Hop HT, Arayan LT, Huy TXN et al (2018) Lipocalin 2 (Lcn2) interferes with iron uptake by Brucella abortus and dampens immunoregulation during infection of RAW 264,7 macrophages. Cell Microbiol 20(3):e12813. https://doi.org/10.1111/cmi.12813

    Article  CAS  Google Scholar 

  32. Chia WJ, Tan FCK, Ong WY, Dawe GS (2015) Expression and localisation of brain-type organic cation transporter (BOCT/24p3R/LCN2R) in the normal rat hippocampus and after kainate-induced excitotoxicity. Neurochem Int 87:43–59. https://doi.org/10.1016/j.neuint.2015.04.009

    Article  CAS  PubMed  Google Scholar 

  33. Merkel SF, Andrews AM, Lutton EM et al (2017) Dexamethasone attenuates the enhanced rewarding effects of cocaine following experimental traumatic brain injury. Cell Transplant 26(7):1178–1192. https://doi.org/10.1177/0963689717714341

    Article  PubMed  PubMed Central  Google Scholar 

  34. Truettner JS, Bramlett HM, Dietrich WD (2017) Posttraumatic therapeutic hypothermia alters microglial and macrophage polarization toward a beneficial phenotype. J Cereb Blood Flow Metab 37(8):2952–2962. https://doi.org/10.1177/0271678X16680003

    Article  CAS  PubMed  Google Scholar 

  35. Yang T, Liu YW, Zhao L et al (2017) Metabotropic glutamate receptor 5 deficiency inhibits neutrophil infiltration after traumatic brain injury in mice. Sci Rep 7(1):1–12. https://doi.org/10.1038/s41598-017-10201-8

    Article  CAS  Google Scholar 

  36. Shi WZ, Ju JY, Xiao HJ et al (2017) Dynamics of MMP-9, MMP-2 and TIMP-1 in a rat model of brain injury combined with traumatic heterotopic ossification. Mol Med Rep 15(4):2129–2135. https://doi.org/10.3892/mmr.2017.6275

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Almeida-Suhett CP, Li Z, Marini AM et al (2013) Temporal course of changes in gene expression suggests a cytokine-related mechanism for long-term hippocampal alteration after controlled cortical impact. J Neurotrauma 31(7):683–690. https://doi.org/10.1089/neu.2013.3029

    Article  Google Scholar 

  38. Zhang H-M, Liu P, Jiang C et al (2018) Notch signaling inhibitor DAPT provides protection against acute craniocerebral injury. PLoS ONE 13(2):e0193037. https://doi.org/10.1371/journal.pone.0193037

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Lee B-C, Lee H, Park H-K et al (2009) Susceptibility for ischemic stroke in four constitution medicine is associated with polymorphisms of FCGR2A and IL1RN genes. Neurol Res 32(supp1):43–47. https://doi.org/10.1179/016164109x12537002793922

    Article  Google Scholar 

  40. Badr R, Hashemi M, Javadi G et al (2016) Assessment of global ischemic/reperfusion and Tacrolimus administration on CA1 region of hippocampus: gene expression profiles of BAX and BCL2 genes. Bratislava Med J 117(6):358–362. https://doi.org/10.4149/BLL_2016_071

    Article  CAS  Google Scholar 

  41. Wang L, Yao X, Li Q, Sun S (2018) Effect of Simvastatin on lipid accumulation and the expression of CXCL16 and nephrin in podocyte induced by oxidized LDL. J Investig Surg 31(2):69–74. https://doi.org/10.1080/08941939.2016.1278057

    Article  CAS  Google Scholar 

  42. Rosito M, Lauro C, Chece G et al (2014) Trasmembrane chemokines CX3CL1 and CXCL16 drive interplay between neurons, microglia and astrocytes to counteract pMCAO and excitotoxic neuronal death. Front Cell Neurosci 8:193. https://doi.org/10.3389/fncel.2014.00193

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Miao Q, Ge M, Huang L (2017) Up-regulation of GBP2 is associated with neuronal apoptosis in rat brain cortex following traumatic brain injury. Neurochem Res 42(5):1515–1523. https://doi.org/10.1007/s11064-017-2208-x

    Article  CAS  PubMed  Google Scholar 

  44. Wang N, Yang L, Zhang H et al (2018) MicroRNA-9a-5p alleviates ischemia injury after focal cerebral ischemia of the rat by targeting ATG5-mediated autophagy. Cell Physiol Biochem 45(1):78–87. https://doi.org/10.1159/000486224

    Article  CAS  PubMed  Google Scholar 

  45. Russell NH, Black RT, Lee NN, Doperalski AE, Reeves TM, Phillips LL (2019) Time-dependent hemeoxygenase-1, lipocalin-2 and ferritin induction after non-contusion traumatic brain injury. Brain Res 1725:146466

    Article  CAS  PubMed  Google Scholar 

  46. Chio CC, Lin HJ, Tian YF, Chen YC, Lin MT, Lin CH, Chang CP, Hsu CC (2017) Exercise attenuates neurological deficits by stimulating a critical HSP70/NF-κB/IL-6/synapsin I axis in traumatic brain injury rats. J Neuroinflammation 14(1):90

    Article  PubMed  PubMed Central  Google Scholar 

  47. Wang Z, Yao W, Deng Q, Zhang X, Zhang J (2013) Protective effects of BDNF overexpression bone marrow stromal cell transplantation in rat models of traumatic brain injury. J Mol Neurosci 49(2):409–416

    Article  CAS  PubMed  Google Scholar 

  48. Phipps HW (2016) Systematic review of traumatic brain injury animal models. In: Kobeissy FH (ed) Injury models of the central nervous system: methods and protocols. Springer, New York, pp 61–88

    Chapter  Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 81974226), the Fundamental Research Funds for the Central Universities (No. 2018SCUH0053), the Science and Technology Major Projects of Sichuan Province of China (No. 2017SZDZX0013), and the Science and Technology Support Program of Sichuan Province of China (No. 2016SZ0013).

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Correspondence to Linbo Gao or Lin Zhang.

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Xiao, X., Bai, P., Cao, S. et al. Integrated Bioinformatics Analysis for the Identification of Key Molecules and Pathways in the Hippocampus of Rats After Traumatic Brain Injury. Neurochem Res 45, 928–939 (2020). https://doi.org/10.1007/s11064-020-02973-9

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