Molecular Medicine Reports Molecular Medicine Reports 1791-2997 1791-3004 D.A. Spandidos 10.3892/mmr.2019.9884 mmr-19-03-2013 Articles Identification of critical genes associated with spinal cord injury based on the gene expression profile of spinal cord tissues from trkB.T1 knockout mice WeiLi 1 * HeFei 1 * ZhangWen 1 ChenWenhua 1 2 YuBo 1 2 Department of Rehabilitation, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai 200080, P.R. China School of International Medical Technology, Shanghai Sanda University, Shanghai 201209, P.R. China Correspondence to: Dr Bo Yu, Department of Rehabilitation, Shanghai General Hospital, Shanghai Jiao Tong University, 100 Haining Road, Shanghai 200080, P.R. China, E-mail: bobby_JTU@163.com

Contributed equally

 032019 22012019 19 3 2013 2020 28122017 03102018 Copyright: © Wei et al. 2019 This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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.

 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 <sec> <title>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 <sec> <title>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.

 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.

 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.

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).

 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).

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’.

 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 CreweNMKrauseJSSpinal cord injury. In: Medical, Psychosocial and Vocational Aspects of DisabilityBrodwinMGSiuFWHowardJBrodwinER3rdElliott and Fitzpatrick, Inc.Athens, GA2893042009 WyndaeleMWyndaeleJJIncidence, prevalence and epidemiology of spinal cord injury: What learns a worldwide literature survey?Spinal Cord44523529200610.1038/sj.sc.310189316389270 ChenYTangYVogelLCDeVivoMJCauses of spinal cord injuryTop Spinal Cord Inj Rehabil1918201310.1310/sci1901-1236782803584795 ShahRRTishermanSASpinal cord injuryJ Imaging the ICU Patient377380201410.1007/978-0-85729-781-5_41 LangCBradleyPMJacobiAKerschensteinerMBareyreFMSTAT3 promotes corticospinal remodelling and functional recovery after spinal cord injuryEMBO Rep14931937201310.1038/embor.2013.117239288113807223 HuangCHanXLiXLamEPengWLouNTorresAYangMGarreJMTianGFCritical role of connexin 43 in secondary expansion of traumatic spinal cord injuryJ Neurosci3233333338201210.1523/JNEUROSCI.1216-11.2012223997553569730 KingVRBradburyEJMcMahonSBPriestleyJVChanges in truncated trkB and p75 receptor expression in the rat spinal cord following spinal cord hemisection and spinal cord hemisection plus neurotrophin treatmentExp Neurol165327341200010.1006/exnr.2000.748010993692 LieblDJHuangWYoungWParadaLFRegulation of Trk receptors following contusion of the rat spinal cordExp Neurol1671526200110.1006/exnr.2000.754811161589 DorseySGRennCLCarimtoddLBarrickCABambrickLKruegerBKWardCWTessarolloLIn vivo restoration of physiological levels of truncated TrkB.T1 receptor rescues neuronal cell death in a trisomic mouse modelNeuron512128200610.1016/j.neuron.2006.06.00916815329 Carim-ToddLBathKGFulgenziGYanpallewarSJingDBarrickCABeckerJBuckleyHDorseySGLeeFSTessarolloLEndogenous truncated TrkB.T1 receptor regulates neuronal complexity and TrkB kinase receptor function in vivoJ Neurosci29678685200910.1523/JNEUROSCI.5060-08.2009191582942719435 WuJRennCLFadenAIDorseySGTrkB. T1 contributes to neuropathic pain after spinal cord injury through regulation of cell cycle pathwaysJ Neurosci331244712463201310.1523/JNEUROSCI.0846-13.2013238849493721848 SmythGKLimma: Linear models for microarray dataJ Bioinform Comput Biol Solut Using R and Bioconductor3974202005 WarnesGRBolkerBBonebakkerLGentlemanRLiawWHALumleyTMaechlerMMagnussonAMoellerSSchwartzMVenablesBgplots: Various R programming tools for plotting data. R package version 3.0.1The Comprehensive R Archive Network2016 CaiHChenHYiTDaimonCMBoyleJPPeersCMaudsleySMartinBVennPlex-A novel Venn diagram program for comparing and visualizing datasets with differentially regulated datapointsPLoS One8e53388201310.1371/journal.pone.0053388233082103538763 LangfelderPHorvathSWGCNA: An R package for weighted correlation network analysisBMC Bioinformatics9559200810.1186/1471-2105-9-559191140082631488 AshburnerMBallCABlakeJABotsteinDButlerHCherryJMDavisAPDolinskiKDwightSSEppigJTGene ontology: Tool for the unification of biology. The gene ontology consortiumNat Genet252529200010.1038/75556108026513037419 KanehisaMGotoSKEGG: Kyoto encyclopedia of genes and genomesNucleic Acids Res282730200010.1093/nar/28.1.2710592173102409 Huang daWShermanBTLempickiRASystematic and integrative analysis of large gene lists using DAVID bioinformatics resourcesNat Protoc44457200910.1038/nprot.2008.21119131956 SzklarczykDFranceschiniAWyderSForslundKHellerDHuerta-CepasJSimonovicMRothASantosATsafouKPSTRING v10: Protein-protein interaction networks, integrated over the tree of lifeNucleic Acids Res43D447D452201510.1093/nar/gku100325352553 ShannonPMarkielAOzierOBaligaNSWangJTRamageDAminNSchwikowskiBIdekerTCytoscape: A software environment for integrated models of biomolecular interaction networksGenome Res1324982504200310.1101/gr.123930314597658403769 NepuszTYuHPaccanaroADetecting overlapping protein complexes in protein-protein interaction networksNat Methods9471472201210.1038/nmeth.1938224264913543700 DefrinROhryABlumenNUrcaGCharacterization of chronic pain and somatosensory function in spinal cord injury subjectsPain89253263200110.1016/S0304-3959(00)00369-911166482 YangYKLuXBWangYHYangMMJiangDMIdentification crucial genes in peripheral neuropathic pain induced by spared nerve injuryEur Rev Med Pharmacol Sci1821522159201425070821 KodaMOkadaSNakayamaTKoshizukaSKamadaTNishioYSomeyaYYoshinagaKOkawaAMoriyaHYamazakiMHematopoietic stem cell and marrow stromal cell for spinal cord injury in miceNeuroreport1617631767200510.1097/01.wnr.0000183329.05994.d716237323 DavidSKronerARepertoire of microglial and macrophage responses after spinal cord injuryNat Rev Neurosci12388399201110.1038/nrn305321673720 JinLWuZXuWHuXZhangJXueZChengLIdentifying gene expression profile of spinal cord injury in rat by bioinformatics strategyMol Biol Rep4131693177201410.1007/s11033-014-3176-824595446 OhriSSMaddieMAZhangYShieldsCBHetmanMWhittemoreSRDeletion of the pro-apoptotic endoplasmic reticulum stress response effector CHOP does not result in improved locomotor function after severe contusive spinal cord injuryJ Neurotrauma29579588201210.1089/neu.2011.1940219330123282015 XuWChiLXuRKeYLuoCCaiJQiuMGozalDLiuRIncreased production of reactive oxygen species contributes to motor neuron death in a compression mouse model of spinal cord injurySpinal Cord43204213200510.1038/sj.sc.310167415520836 UeyamaTLennartzMRNodaYKobayashiTShiraiYRikitakeKYamasakiTHayashiSSakaiNSeguchiHSuperoxide production at phagosomal cup/phagosome through βI protein kinase C during FcγR-mediated phagocytosis in microgliaJ Immunol17345824589200410.4049/jimmunol.173.7.458215383592 FurlanJCFehlingsMGCardiovascular complications after acute spinal cord injury: Pathophysiology, diagnosis, and managementNeurosurg Focus25E13200810.3171/FOC.2008.25.11.E1318980473 BravoGGuízar-SahagúnGIbarraACenturiónDVillalónCMCardiovascular alterations after spinal cord injury: An overviewCurr Med Chem Cardiovasc Hematol Agents2133148200410.2174/156801604347724215320796

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 of the differentially expressed genes among the different groups. KO, knockout.

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.

Protein-protein interaction network of the differentially expressed genes. Gene modules are represented by different colors.

Significant modules of the protein-protein interaction network. Gene modules are represented by different colors.

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.

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.

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

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