Abstract
The present study aimed to identify the genes and underlying mechanisms critical to the pathology of spinal cord injury (SCI). Gene expression profiles of spinal cord tissues of trkB.T1 knockout (KO) mice following SCI were accessible from the Gene Expression Omnibus database. Compared with trkB.T1 wild type (WT) mice, the differentially expressed genes (DEGs) in trkB.T1 KO mice following injury at different time points were screened out. The significant DEGs were subjected to function, co-expression and protein-protein interaction (PPI) network analyses. A total of 664 DEGs in the sham group and SCI groups at days 1, 3, and 7 following injury were identified. Construction of a Venn diagram revealed the overlap of several DEGs in trkB.T1 KO mice under different conditions. In total, four modules (Magenta, Purple, Brown and Blue) in a co-expression network were found to be significant. Protein tyrosine phosphatase, receptor type C (PTPRC), coagulation factor II, thrombin (F2), and plasminogen (PLG) were the most significant nodes in the PPI network. ‘Fc γ R-mediated phagocytosis’ and ‘complement and coagulation cascades’ were the significant pathways enriched by genes in the PPI and co-expression networks. The results of the present study identified PTPRC, F2 and PLG as potential targets for SCI treatment, which may further improve the general understanding of SCI pathology.
Keywords: spinal cord injury, differentially expressed genes, weighted correlation network analysis, protein-protein interaction, significant nodes
Introduction
Spinal cord injury (SCI) is a disabling condition with significant morbidity and mortality, that affects ~11,000 individuals annually worldwide (1). The mean age for patients with SCI is 33 years worldwide, and the incidence of SCI is approximately four times higher among males (2). Spinal cord damage may result in pain and paralysis, as well as loss of sensation and physical function. The causes of SCI are varied and include accidents, falls, infections and tumors (3). In addition, patients with SCI may develop spondylosis, a common degenerative change in the cervical spine (4) and its incidence has increased over the past several decades, but management strategies to reduce SCI prevalence have not yet been developed.
The current management strategies for SCI are limited. A better understanding of the physiological mechanisms underlying the disease may lead to the identification of novel interventions. A study exploring the mechanisms underlying SCI has reported that overexpression of signal transducer and activator of transcription 3 (STAT3) promotes neuron regeneration and functional recovery following SCI (5). In addition, connexin 43 (Cx43) expression has been demonstrated to aggravate secondary injury following SCI, leading to researchers proposing connexins as potential targets for SCI treatment (6).
Tropomyosin-related kinase B.T1 (trkB.T1), which is highly expressed in the nervous system of adult mammals, has been identified to accumulate in astrocytes, white matter and ependymal cells following SCI (7,8). Although trkB.T1 lacks a kinase activation domain, it is active in signal transduction (9,10). Wu et al (11) performed whole genome analysis for trkB.T1 knockout (KO) mice and reported that trkB.T1 serves a critical role in SCI pain and progression by regulating pathways associated with the cell cycle (11).
Herein, a bioinformatic approach was used to analyze the microarray data compiled by Wu et al (11), in order to further investigate the differentially expressed genes (DEGs) in trkB.T1 KO mice at different time points following SCI. As genes with differential expression may be closely associated with SCI pathogenesis, the functions and interactions of the identified DEGs were explored. In addition, the present study aimed to identify potential target genes and their involvement in the biological functions underlying SCI pathogenesis.
Materials and methods
Data collection
Spinal cord tissues of SCI mice were profiled based on the Affymetrix Mouse Genome 430 2.0 Array platform (11). Microarray data were provided by Wu et al (11); these data were generated from trkB.T1 KO and trkB.T1 wild type (WT) mice under different conditions, including sham operations, and at days 1, 3, and 7 following SCI. This dataset (GSE47681) was downloaded from the Gene Expression Omnibus database (www.ncbi.nlm.nih.gov/geo/).
Data preprocessing and DEG analysis
Differences in gene expression between trkB.T1 KO and trkB.T1 WT mice in sham groups (sham KO vs. sham WT) and in SCI groups at days 1, 3, and 7 after injury (day 1 KO vs. day 1 WT; day 3 KO vs. day 3 WT; and day 7 KO vs. day 7 WT) were respectively compared via unpaired t-tests using the R package ‘limma’ (12). Genes for which met the P<0.05 and |log2 fold change (FC)|≥0.4 cutoff points were selected as DEGs, following which gene expression profiles of DEGs were visualized via the ‘gplots’ in R package version 3.0. 1 (13).
Venn diagram analysis
In order to mine the feature genes from different datasets, a Venn diagram analysis was conducted using VennPlex version 1.0.0.2 software (www.irp.nia.nih.gov/bioinformatics/vennplex.html) (14). The DEGs and their respective log2FC values were uploaded to the VennPlex version 1.0.0.2 tool, from which differences in the expression levels of DEGs at several time points were obtained, and the number of upregulated, downregulated and contraregulated genes were calculated.
Co-expression module and functional analysis
Weighted correlation network analysis (WGCNA) was used to identify highly correlated genes based on gene expression patterns across the microarray samples (15). DEGs in trkB.T1 KO mice of the sham group and SCI groups at days 1, 3 and 7 following injury were subjected to co-expression analysis using the R package ‘WGCNA’ version 1.19 (15) based on the WGCNA algorithm. Significant gene co-expression modules were screened out using the clustering method. The |correlation coefficient|≥0.65 and P<0.05 were set as the cutoff values.
Gene Ontology (GO) (16) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) (17) pathway analyses were conducted for the genes in the co-expression modules by the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool (18) with new fuzzy classification algorithms. GO and KEGG terms with counts ≥2 and P<0.05 was considered to indicate a statistically significant difference.
Protein-protein interaction network
PPI network analysis was performed to analyze the functional interactions between proteins encoded by DEGs associated with the co-expression network. the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database (19) includes an extensive list of protein interacting pairs collected from neighborhood, gene fusion, co-occurrence, co-expression experiments, databases and text mining. Genes from the co-expression network were input into the STRING online tool to identify highly associated gene pairs. The protein pairs with medium confidence (≥0.4) were collected and the PPI network was visualized using the package ‘Cytoscape’ version 3 (20). Significant nodes with high degrees of connectivity were screened out.
Module analysis
PPI networks contain several densely connected network modules, with genes in each module commonly involved in the same biological processes. ClusterONE (21) is a graph-clustering algorithm that incorporates weighted graphs and readily generates overlapping clusters (www.paccanarolab.org/cluster-one/). As such, ClusterONE version 1.0 was used to cluster the PPI network, with P<1×10−4 set as the threshold value. Further KEGG pathway analysis of the module genes was subsequently undertaken to identify the significant pathways (P<0.05).
Results
Differential expression analysis
Based on P<0.05 and |log2FC| ≥0.4, DEGs of trkB.T1 KO mice in the sham, day 1, day 3 and day 7 SCI groups were respectively identified. As presented in Table I, the smallest number of DEGs occurred in the sham group, consisting of 45 upregulated and 21 downregulated genes. The day 3 SCI group contained the largest number of differentially expressed genes, consisting of 206 upregulated and 149 downregulated genes. Gene expression profiles of the various DEGs in the different groups are presented in Fig. 1. This analysis suggested that DEG expression levels may be used to distinguish between trkB.T1 KO and WT samples.
Table I.
Differentially expressed genes in the sham and spinal cord injury groups.
| Group | Upregulated gene count | Mean value logFC (up) | Downregulated gene count | Mean value logFC (down) | Total |
|---|---|---|---|---|---|
| Sham KO | 45 | 0.882 | 21 | −0.5569 | 66 |
| Day 1 KO | 73 | 0.7096 | 132 | −0.6407 | 205 |
| Day 3 KO | 206 | 0.6201 | 149 | −0.8843 | 355 |
| Day 7 KO | 74 | 0.54 | 32 | −0.5852 | 106 |
KO, knockout; FC, fold change.
Figure 1.
Heat map of the differentially expressed genes in trkB.T1 KO mice following injury at different time points. Red represents relatively high levels of expression; green represents relatively low levels of expression. Purple and brown bars indicated the trkB.T1 WT samples and trkB.T1 KO samples, respectively; yellow and blue bars indicated that upregulated and downregulated genes respectively in trkB.T1 KO mice following injury. The heat map demonstrated that the samples from trkB.T1 KO were distinct from those from the trkB.T1 WT mice. KO, knockout; WT, wild type.
Venn diagram analysis
The relationships between the different DEG groups are depicted in Fig. 2. Compared with the sham group, the number of overlapping DEGs of trkB.T1 KO mice at days 1, 3 and 7 after SCI were 7, 5 and 10, respectively. In addition, 26 DEGs overlapped between the day 1 and day 3 SCI groups; 17 overlapped between the day 1 and day 7 SCI groups; and 11 overlapped between the day 3 and day 7 SCI groups.
Figure 2.
Venn diagram of the differentially expressed genes among the different groups. KO, knockout.
WGCNA co-expression analysis
A total of 664 DEGs in the sham group and SCI groups at days 1, 3, and 7 following injury were identified in trkB.T1 KO mice. Subjecting the DEGs to WGCNA analysis revealed that they were clustered into 11 modules (represented by the different colors in Fig. 3). The top four modules-those with the highest correlation coefficients (CC) and lowest P-values-were, in the following order: Magenta (CC=0.66, P=3.67×10−3; gene count, 23), Purple (CC=0.65, P=4.69×10−3; gene count, 15), Brown (CC=0.9, P=7.20×10-7; gene count, 96) and Blue (CC=0.88, P=3.62×10−6; gene count, 110) modules.
Figure 3.
Hierarchical clustering tree for genes with co-expression interactions. The branches of the dendrogram indicate the gene clusters; co-expressed modules are represented by different colors.
Functional analysis revealed that the genes in the Magenta module were significantly enriched in ‘response to virus’, ‘immune response’, and ‘cytosolic DNA-sensing pathway’; genes in the Purple module were strongly associated with ‘extracellular region’ and ‘drug metabolism’; genes in the Blue module were significantly associated with ‘immune response’, ‘Fc γ R-mediated phagocytosis’ and ‘complement and coagulation cascades’; and genes in the Brown module were markedly enriched in ‘oxidation reduction’, ‘drug metabolism’ and ‘complement and coagulation cascades’ (Table II).
Table II.
Significant GO and KEGG pathways for genes in co-expression modules.
| Module | Category | Term | Count | P-value |
|---|---|---|---|---|
| Magenta | BP | GO:0009615~response to virus | 5 | 1.85×10−7 |
| GO:0006955~immune response | 6 | 1.07×10−5 | ||
| BP | GO:0006952~defense response | 5 | 2.09×10−4 | |
| CC | GO:0005783~endoplasmic reticulum | 5 | 5.95×10−4 | |
| MF | GO:0032555~purine ribonucleotide binding | 8 | 1.17×10−3 | |
| GO:0032553~ribonucleotide binding | 8 | 1.17×10−3 | ||
| MF | GO:0017076~purine nucleotide binding | 8 | 1.50×10−3 | |
| KEGG_PATHWAY | mmu04623:Cytosolic DNA-sensing pathway | 3 | 1.84×10−3 | |
| mmu04622:RIG-I-like receptor signaling pathway | 3 | 2.80×10−3 | ||
| Purple | CC | GO:0044421~extracellular region part | 4 | 1.50×10−2 |
| GO:0005576~extracellular region | 5 | 2.34×10−2 | ||
| KEGG_PATHWAY | mmu00983:Drug metabolism | 2 | 4.11×10−2 | |
| Blue | BP | GO:0006955~immune response | 15 | 8.03×10−8 |
| GO:0009611~response to wounding | 11 | 9.24×10−6 | ||
| BP | GO:0001775~cell activation | 9 | 3.32×10−5 | |
| CC | GO:0034358~plasma lipoprotein particle | 4 | 4.65×10−4 | |
| GO:0032994~protein-lipid complex | 4 | 4.65×10−4 | ||
| CC | GO:0005886~plasma membrane | 28 | 3.60×10−3 | |
| MF | GO:0001871~pattern binding | 6 | 4.28×10−4 | |
| GO:0030247~polysaccharide binding | 6 | 4.28×10−4 | ||
| MF | GO:0005539~glycosaminoglycan binding | 5 | 2.46×10−3 | |
| KEGG_PATHWAY | mmu04666:Fc γ R-mediated phagocytosis | 6 | 3.71×10−4 | |
| mmu04610:Complement and coagulation cascades | 5 | 1.28×10−3 | ||
| mmu04650:Natural killer cell mediated cytotoxicity | 5 | 7.47×10−3 | ||
| Brown | BP | GO:0055114~oxidation reduction | 10 | 2.03×10−3 |
| GO:0002526~acute inflammatory response | 4 | 4.97×10−3 | ||
| BP | GO:0055090~acylglycerol homeostasis | 2 | 8.52×10−3 | |
| CC | GO:0005576~extracellular region | 22 | 7.15×10−5 | |
| GO:0005615~extracellular space | 10 | 1.23×10−3 | ||
| GO:0044421~extracellular region part | 12 | 1.97×10−3 | ||
| MF | GO:0016712~oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen, reduced flavin or flavoprotein as one donor, and incorporation of one atom of oxygen | 4 | 9.68×10−4 | |
| GO:0009055~electron carrier activity | 6 | 1.99×10−3 | ||
| MF | GO:0043176~amine binding | 4 | 2.49×10−3 | |
| KEGG_PATHWAY | mmu00982:Drug metabolism | 5 | 1.04×10−3 | |
| mmu04610:Complement and coagulation cascades | 4 | 1.04×10−2 | ||
| mmu00591:Linoleic acid metabolism | 3 | 3.16×10−2 |
MF, molecular function; BP, biological process; CC, cellular component; KEGG, Kyoto Encyclopedia of Genes and Genomes.
PPI construction and module analysis
The PPI network connected 161 nodes through 1,051 edges; the color scheme used for the nodes was the same as that used for the modules described in the previous section (Fig. 4). The top 20 nodes (Table III) were determined to have a degree ≥28 in the PPI network, including protein tyrosine phosphatase, receptor type C (PTPRC; degree, 43; Blue), coagulation factor II, thrombin (F2; degree, 41; Brown), plasminogen (PLG; degree, 38; Blue), and thymocyte selection associated family member 2 (Themis2; degree, 37; Blue).
Figure 4.
Protein-protein interaction network of the differentially expressed genes. Gene modules are represented by different colors.
Table III.
Top 20 genes with the highest degree in the PPI network.
| Gene | Module type | Degree |
|---|---|---|
| PTPRC | Blue | 43 |
| F2 | Brown | 41 |
| PLG | Blue | 38 |
| THEMIS2 | Blue | 37 |
| SERPINC1 | Blue | 35 |
| SLC27A5 | Blue | 34 |
| SLC15A3 | Blue | 33 |
| AKR1C6 | Brown | 32 |
| CD53 | Blue | 32 |
| TRIM30A | Magenta | 32 |
| PTPN6 | Blue | 32 |
| GPR65 | Blue | 32 |
| SLCO1B2 | Blue | 32 |
| APOF | Brown | 32 |
| FCGR4 | Blue | 30 |
| AIF1 | Brown | 30 |
| APOA5 | Brown | 28 |
| IFI47 | Magenta | 28 |
| FGG | Brown | 28 |
| FCGR1 | Blue | 28 |
ClusterOne analysis resulted in three significant gene clusters (Fig. 5), and the DAVID online tool, which facilitates classification of functionally associated genes in different KEGG pathways (Table IV), demonstrated that the genes in cluster 1 were closely associated with ‘complement and coagulation cascades’, ‘drug metabolism’, and ‘phenylalanine, tyrosine, and tryptophan biosynthesis’; the genes in cluster 2 were significantly involved in ‘Fc γ R-mediated phagocytosis’, ‘B cell receptor signaling pathway’ and ‘natural killer cell mediated cytotoxicity’; genes in cluster 3 were involved in the ‘cytosolic DNA-sensing’ and ‘RIG-I-like receptor signaling pathways’.
Figure 5.
Significant modules of the protein-protein interaction network. Gene modules are represented by different colors.
Table IV.
Significant pathways for genes in the protein-protein interaction network.
| Cluster | Term | P-value | Genes |
|---|---|---|---|
| Cluster 1 | mmu04610: Complement and coagulation cascades | 1.02×10−8 | F13B, MBL2, FGG, HC, SERPINF2, F2, SERPINC1, PLG |
| mmu00982: Drug metabolism | 3.15×10−3 | CYP2D9, CYP2A12, CYP2D10, CYP2D26 | |
| mmu00400: Phenylalanine, tyrosine and tryptophan biosynthesis | 1.99×10−2 | PAH, TAT | |
| Cluster 2 | mmu04666: Fc γ R-mediated phagocytosis | 1.24×10−4 | PTPRC, NCF1, PLCG2, WAS, FCGR1 |
| mmu04662: B cell receptor signaling pathway | 1.28×10−3 | PTPN6, PLCG2, CD72, BLNK | |
| mmu04650: Natural killer cell mediated cytotoxicity | 4.29×10−3 | CD48, PTPN6, PLCG2, FCGR4 | |
| mmu05340: Primary immunodeficiency | 4.35×10−3 | PTPRC, IL7R, BLNK | |
| mmu04670: Leukocyte transendothelial migration | 4.23×10−2 | CYBB, NCF1, PLCG2 | |
| Cluster 3 | mmu04623: Cytosolic DNA-sensing pathway | 8.22×10−6 | DDX58, TMEM173, IRF7, ZBP1 |
| mmu04622: RIG-I-like receptor signaling pathway | 1.57×10−5 | DDX58, IFIH1, TMEM173, IRF7 |
Discussion
SCI often results in chronic pain and loss of physical function (22). A previous study demonstrated that TrkB.T1, as a receptor for brain-derived neurotrophic factor, serves a critical role in neuropathic pain and SCI progression (11). In the present study, the significant DEGs at different time points following SCI were identified and potential targets for SCI therapy were suggested based on the differential expression profiles induced by TrkB.T1 KO.
In total, 664 genes were differentially expressed in the sham group and SCI groups at different time points following injury. Gene expression profiles of the DEGs differed significantly between TrkB.T1 KO and TrkB.T1 WT samples, and construction of a Venn diagram indicated a lower number of overlapping DEGs under the different conditions. Based on these results, it was concluded that the DEGs screened out were significant.
Analysis of the PPI network suggested that PTPRC, F2, and PLG were the most significant nodes, with multiple interactions with other nodes, and PTPRC with a degree of 43 was identified to be the single most significant node in the PPI network. Previous research has shown PTPRC to be a critical DEG in the PPI network following spared nerve injury, and PTPRC might thus represent a potential target for peripheral neuropathic pain intervention (23). Based on the results obtained in the present study, it was speculated that PTPRC may also have a critical role in the pathology of SCI.
Protein tyrosine phosphatase, the receptor type C encoded by PTPRC, is a member of the protein tyrosine phosphatase family. PTPRC, also known as CD45 antigen, is highly expressed in hematopoietic cells in particular and is strongly associated with cellular growth and proliferation. Transplanted hematopoietic stem cells have been demonstrated to persist in SCI lesions and contribute to functional recovery following SCI (24). The expression of CD45 has been revealed to be upregulated in macrophages and microglia during the inflammatory response following SCI (25). The anti-inflammatory activation of macrophages and microglia promotes tissue and function repair following SCI (25).
In addition, PTPRC in cluster 2 was significantly enriched in Fc γ R-mediated phagocytosis, a finding consistent with previous results; Jin et al (26) also reported that the genes upregulated post-SCI are significantly enriched in Fc γ R-mediated phagocytosis. Furthermore, Ohri et al (27) observed that the Fc γ R-mediated phagocytosis pathway is dysregulated in C/EBP-homologous protein 10-null mice following severe SCI. Previous evidence further suggests that superoxide is produced during Fc γ R-mediated phagocytosis, and is strongly associated with neuronal death following SCI (28,29). In the present study, co-expression analysis also revealed that PTPRC was clustered in the Blue module, and the genes contained therein are known to be associated with Fc γ R-mediated phagocytosis. Taken together, these results suggested that PTPRC serves a critical role in SCI progression via interactions with other genes.
F2 and PLG were also identified to be critical genes in the PPI network. PPI module analysis suggested that F2 and PLG composed cluster 2, and were both significantly enriched in the complement and coagulation cascades. Co-expression analysis clustered F2 in the Brown module and PLG in the Blue module, and subsequent KEGG pathway analysis revealed that the genes in the Brown and Blue modules were closely associated with the complement and coagulation cascades, which suggested that the findings of the present study were of particular significance.
Coagulation factor II, also known as thrombin, is encoded by F2. Thrombin functions in coagulation-associated reactions and thus in reducing blood loss. Like thrombin, plasmin encoded by PLG is a serine protease present in the blood (27). It has been reported that patients with SCI are often afflicted with coagulation-related disorders, including altered platelet function and coagulation factor concentrations, which may lead to cardiovascular disorders (30,31). Therefore, focused targeting of F2 and PLG expression may aid in the mitigation of cardiovascular disorders associated with SCI.
Due to a lack of clinical materials and funding, the present study was unable to provide experimental validation of these findings, but these results warrant future research involving both cellular and clinical samples.
In conclusion, PTPRC, F2 and PLG were identified as significant nodes in the PPI network, and therefore may have critical function in SCI progression through their involvement in inflammatory responses and coagulation disorders. As such, PTPRC, F2 and PLG may be candidate targets for SCI gene therapy. The findings of the present study may lead to a better understanding of the pathogenesis of SCI and shed light on the identification of novel therapeutic targets. However, clinical trials on gene therapy are necessary to assess potential genetic strategies for SCI.
Acknowledgments
Not applicable.
Funding
The study was funded by the Shanghai Science and Technology Committee program (no 16411955200) and the Scientific Research and Innovation Team Funding Plan of Shanghai Sanda University.
Availability of data and materials
All data generated or analyzed during the present study are included in this published article.
Authors' contributions
LW and FH were responsible for the conception and design of the research, and drafting the manuscript. WZ performed the data acquisition. WC performed the data analysis and interpretation. BY participated in the design of the study and performed the statistical analysis. All authors have read and approved the manuscript.
Ethics approval and consent to participate
Not applicable.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
All data generated or analyzed during the present study are included in this published article.