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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.

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.

Table I. Differentially expressed genes in the sham and spinal cord injury groups.

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

[i] KO, knockout; FC, fold change.

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.

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

[i] 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.

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.

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.

References