Identification of Regeneration and Hub Genes and Pathways at Different Time Points after Spinal Cord Injury

Fang, Sheng; Wang, An-Quan; Zhang, Hui; Yin, Zhou

doi:10.1007/s12035-021-02289-x

Cited by 12 publications

(7 citation statements)

References 107 publications

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“…Bioinformatic analysis was an important means of exploring the complex molecular mechanism of SCI. A previous bioinformatic study found that ANXA1, SNAP25 and SPP1 were closely related to spinal cord repair and regeneration after injuried [17]. A sequence analysis of SCI revealed the effect of noncoding RNA in SCI and a ceRNA regulation network with miR-21 as the center was constructed [25].…”

Section: Discussionmentioning

confidence: 99%

“…The Database for Annotation, Visualization and Integrated Discovery (DAVID v6.8, https://david.ncifcrf.gov/home.jsp) was used in this step to carry the Gene Ontology (GO) functional enrichment analysis on DEGs [16]. The GO annotation analysis was focused on biological process (BP), molecular function (MF), and cellular component (CC) [17]. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichmennt analysis was performed using the KOBAS online tool (http://kobas.cbi.pku.edu.cn/) [18].…”

Section: Go and Kegg Enrichment Analysis Of Degsmentioning

confidence: 99%

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Identification of Ferroptotic Genes in Spinal Cord Injury at Different Time Points: Bioinformatics and Experimental Validation

Kang

Zhu

et al. 2022

Mol Neurobiol

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Programmed cell death (PCD) is an important pathologic process after spinal cord injury (SCI), and as a newly type of PCD, ferroptosis is also involved in the secondary SCI, however, the underlying molecular mechanisms remain unclear. Integrating animal experiment and bioinformatics, we validated the ferroptotic phenotype in SCI rst, and then bioinformatic analyses, including Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, gene set enrichment analysis and protein-protein interaction analysis were performed to investigate the ferroptotic genes at 1 day, 3 days, 7 days, 14 days and 56 days post-SCI, nally, the ferroptotic genes in SCI were identi ed and expression of 5 key genes were validated by western blot. The ferroptotic symbols including iron overload, lipid peroxidation, shrunken mitochondria and ROS accumulation were detected in the acute and sub-acute phase of SCI. The outcomes of bioinformatics suggested that mTOR signaling pathway, HIF-1 signaling pathway, VEGF signaling pathway, Protein processing in endoplasmic reticulum were involved in ferroptotic regulation and ATF-3, XBP-1, HO-1, DDIT-3 and CHAC-1 were selected as the ferroptotic key genes in SCI. Besides, response to oxidative stress, amide metabolic process, cation transport and cytokine production were showed as the essential biological process in ferroptosis after SCI.The ferroptotic phenotype following SCI was validated and the ferroptotic genes and signaling pathways were identi ed. The results contribute to exploring the ferroptotic mechanism underlying secondary SCI and to providing potential target for clinical treatment.

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Section: Discussionmentioning

confidence: 99%

Section: Go and Kegg Enrichment Analysis Of Degsmentioning

confidence: 99%

Identification of Ferroptotic Genes in Spinal Cord Injury at Different Time Points: Bioinformatics and Experimental Validation

Kang

Zhu

et al. 2022

Mol Neurobiol

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“…Gnai1 belong to the family of heterotrimeric signaltransducing molecules consisting of alpha, beta and gamma subunits, and the encoded proteins are part of a complex that responds to beta-adrenergic signals. This gene also emerged as hub gene in a protein-protein interaction network based on Gene Expression Omnibus (GEO) database, including 1, 3, 7 and 14 DPL [35]. While several preclinical and clinical studies indicated substantial differences in the anatomical outcome of the SC segments rostral and caudal to the lesion [36,37], the supporting mechanisms are not clear.…”

Section: Discussionmentioning

confidence: 99%

A Time-Course Study of the Expression Level of Synaptic Plasticity-Associated Genes in Un-Lesioned Spinal Cord and Brain Areas in a Rat Model of Spinal Cord Injury: A Bioinformatic Approach

Baldassarro

Sanna

Bighinati

et al. 2021

IJMS

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“Neuroplasticity” is often evoked to explain adaptation and compensation after acute lesions of the Central Nervous System (CNS). In this study, we investigated the modification of 80 genes involved in synaptic plasticity at different times (24 h, 8 and 45 days) from the traumatic spinal cord injury (SCI), adopting a bioinformatic analysis. mRNA expression levels were analyzed in the motor cortex, basal ganglia, cerebellum and in the spinal segments rostral and caudal to the lesion. The main results are: (i) a different gene expression regulation is observed in the Spinal Cord (SC) segments rostral and caudal to the lesion; (ii) long lasting changes in the SC includes the extracellular matrix (ECM) enzymes Timp1, transcription regulators (Egr, Nr4a1), second messenger associated proteins (Gna1, Ywhaq); (iii) long-lasting changes in the Motor Cortex includes transcription regulators (Cebpd), neurotransmitters/neuromodulators and receptors (Cnr1, Gria1, Nos1), growth factors and related receptors (Igf1, Ntf3, Ntrk2), second messenger associated proteins (Mapk1); long lasting changes in Basal Ganglia and Cerebellum include ECM protein (Reln), growth factors (Ngf, Bdnf), transcription regulators (Egr, Cebpd), neurotransmitter receptors (Grin2c). These data suggest the molecular mapping as a useful tool to investigate the brain and SC reorganization after SCI.

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“…Based on the systematic bioinformatics analysis, microarray and high throughput sequencing techniques have emerged as essential tools for exploring the pathogenesis of complex human diseases [14,15]. Many bioinformatics studies have been conducted in the field of oncology and some non-tumor diseases, but relatively few have focused explicitly on traumatic nerve injuries such as TBI [16]. The Gene Expression Omnibus (GEO) is a publicly available database consisting of an invaluable resource of mass gene expression data that can be systematically analyzed for biomarker or therapeutic target discovery [17].…”

Section: Introductionmentioning

confidence: 99%

Identification of Key Genes and Pathways in the Hippocampus after Traumatic Brain Injury: Bioinformatics Analysis and Experimental Validation

Zeng¹,

Zhao²,

Zhao³

et al. 2023

J. Integr. Neurosci.

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Background: Traumatic brain injury (TBI) is a common brain injury with a high morbidity and mortality. The complex injury cascade triggered by TBI can result in permanent neurological dysfunction such as cognitive impairment. In order to provide new insights for elucidating the underlying molecular mechanisms of TBI, this study systematically analyzed the transcriptome data of the rat hippocampus in the subacute phase of TBI. Methods: Two datasets (GSE111452 and GSE173975) were downloaded from the Gene Expression Omnibus (GEO) database. Systematic bioinformatics analyses were performed, including differentially expressed genes (DEGs) analysis, gene set enrichment analysis (GSEA), Gene Ontology (GO) enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, protein-protein interaction (PPI) network construction, and hub gene identification. In addition, hematoxylin and eosin (HE), Nissl, and immunohistochemical staining were performed to assess the injured hippocampus in a TBI rat model. The hub genes identified by bioinformatics analyses were verified at the mRNA expression level. Results: A total of 56 DEGs were shared in the two datasets. GSEA results suggested significant enrichment in the MAPK and PI3K/Akt pathways, focal adhesion, and cellular senescence. GO and KEGG analyses showed that the common DEGs were predominantly related to immune and inflammatory processes, including antigen processing and presentation, leukocyte-mediated immunity, adaptive immune response, lymphocyte-mediated immunity, phagosome, lysosome, and complement and coagulation cascades. A PPI network of the common DEGs was constructed, and 15 hub genes were identified. In the shared DEGs, we identified two transcription co-factors and 15 immune-related genes. The results of GO analysis indicated that these immune-related DEGs were mainly enriched in biological processes associated with the activation of multiple cells such as microglia, astrocytes, and macrophages. HE and Nissl staining results demonstrated overt hippocampal neuronal damage. Immunohistochemical staining revealed a marked increase in the number of Iba1-positive cells in the injured hippocampus. The mRNA expression levels of the hub genes were consistent with the transcriptome data. Conclusions: This study highlighted the potential pathological processes in TBI-related hippocampal impairment. The crucial genes identified in this study may serve as novel biomarkers and therapeutic targets, accelerating the pace of developing effective treatments for TBI-related hippocampal impairment.

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Identification of Regeneration and Hub Genes and Pathways at Different Time Points after Spinal Cord Injury

Cited by 12 publications

References 107 publications

Identification of Ferroptotic Genes in Spinal Cord Injury at Different Time Points: Bioinformatics and Experimental Validation

Identification of Ferroptotic Genes in Spinal Cord Injury at Different Time Points: Bioinformatics and Experimental Validation

A Time-Course Study of the Expression Level of Synaptic Plasticity-Associated Genes in Un-Lesioned Spinal Cord and Brain Areas in a Rat Model of Spinal Cord Injury: A Bioinformatic Approach

Identification of Key Genes and Pathways in the Hippocampus after Traumatic Brain Injury: Bioinformatics Analysis and Experimental Validation

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