SP1, MYC, CTNNB1, CREB1, JUN genes as potential therapy targets for neuropathic pain of brain

2.1 DEGs identification and principal component analysis of brain tissues with NP

RNA-Seq data was downloaded from the GEO data set (https://www.ncbi.nlm.nih.gov/geo/) on NCBI with the keyword “Neuropathic Pain” and “Brain.” The expression data of genes associated with data set samples were extracted out. The expression data of genes in the different location of the brain was identified with the NP with the threshold of 1.5 fold change. The distribution of up- and downregulated genes in different tissues of the brain was analyzed, and the coexpression genes in these tissues were extracted out. Furthermore, Princomp function in R language was used to perform the principal component analysis (PCA) based on the expression situation of these coexpression genes.

2.2 Functional enrichment analysis of DEGs of brain samples after NP stimulation

The information of DEGs in every brain tissue dealt with NP stimulation was mapped to the DAVID (The Database for Annotation, Visualization, and Integrated Discovery) data set (https://david.ncifcrf.gov/summary.jsp; Dennis et al., 2003). GO (Ashburner et al., 2000) was used to analyze the biological processes, and KEGG (Kanehisa & Goto, 2000; Kanehisa, Goto, Kawashima, Okuno, & Hattori, 2004) was used to analyze the metabolic pathways, which was based on the condition that the p-value after correction was less than 0.05. If the number of records was more than five, the top five would be selected for the analysis. String software (https://string-db.org/; Jensen et al., 2008) was then used to perform the PPI network analysis on the genes in the identified important metabolic pathways.

2.3 Transcript factors analysis of DEGs of brain samples after NP stimulation

The information of DEGs in each brain tissue was mapped to Tfacts data set (Essaghir et al., 2010) (http://www.tfacts.org/). The transcript factor genes that affected the DEGs were analyzed. The distribution of transcript factors in different tissues was analyzed. Besides, the number of genes that each transcription factor gene regulated was calculated to determine the most critical transcript factor gene.

3.1 Identification of DEGs and PCA of brain tissues dealt with NP stimulation

An available RNA-seq data set (GSE91396) (Descalzi et al., 2017) in NCBI was selected in this study to analyze the association between NP and brain disease, as well as the effect of NP on the metabolic pathway of brain genes. The DEGs of three parts of the brain (nucleus accumbens [NAc] , periaqueductal gray [PAG], and prefrontal cortex [PFC]) dealt with NP stimulation were identified. Furthermore, the main metabolic pathways and transcript factors were identified based on the analysis of the result of functional enrichment analysis and transcript factors of these DEGs. The specific analysis protocol flow is shown in Figure 1.

The RNA-Seq data set GSE91396 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE91396; Descalzi et al., 2017) was determined after the exploration using the keywords “Neuropathic Pain” and “Brain.” A total of 36 samples were included in the data set. RNA-seq samples were generated from three brain regions (NAc, medial PFC, and PAG) of adult male mice, 2.5 months after sham or spared nerve injury to the sciatic nerve. The platform of sequencing was GPL13112 (IlluminaHiSeq. 2000; Mus musculus).

The fold change of expression value equal to 1.5 was determined as the threshold of the determination of DEGs. A total of 1,341 genes were found to be upregulated in brain tissue and seven genes, Plin4, Lrg1, Mslnl, Ch25h, Bcl3, Capn11, and n-R5s197, were found to be all upregulated in three parts of the brain (Figure 2a). Meanwhile, a total of 1,006 genes were found to be downregulated in the brain tissue, and among these, one gene, Gm26583, was found to be downregulated in all three parts of the brain (Figure 2b). The PCA of the above eight coexpression genes showed that they could clearly separate the samples treated with NP from those untreated (Figure 2c). The distribution rate of the first principal component was 47.0%, whereas that of the second principal component was 20.1%, which meant that the cumulative rate was 67.1%.

3.2 Functional enrichment analysis of the DEGs

GO ontology enrichment analysis was performed on these DEGs of the brain dealt with NP stimulation (Figure 3 and Table 1). There was an obvious difference in the biological effect of NP on different parts of the brain (Figure 3). The upregulated genes were mainly enriched in pathways as GO:0006954–inflammatory response and GO:0019722–calcium-mediated signaling, whereas the downregulated genes were mainly enriched in pathways as GO:0030199–collagen fibril organization, GO:0002376–immune system process, and GO:0006955–immune response. In PAG tissue, the upregulated genes were mainly enriched in GO:0030154–cell differentiation and GO:0038083–peptidyl-tyrosine autophosphorylate biological processes dealt with NP stimulation, whereas the downregulated genes were mainly enriched in pathways as GO:0032496–response to lipopolysaccharide, the GO:0006644–phospholipid metabolic process, and GO:0006644–phospholipid metabolic process.

In PFC tissue, the upregulated genes were mainly enriched in the GO:0002376–immune system process, the GO:0006955–immune response, the GO:0009615–response to the virus, and the GO:0051384–response to glucocorticoid dealt with NP stimulation, whereas the downregulated genes were just mainly enriched in the GO:0051897–positive regulation of protein kinase B signaling.

KEGG enrichment analysis was further performed on these DEGs (Figure 4 and Table 2). There was an obvious difference in the effect of NP on different parts of the brain. In Nac part, the upregulated genes dealt with NP stimulation were mainly enriched in pathways as mmu04060: Cytokine-cytokine receptor interaction whereas the downregulated genes were mainly enriched in pathways as mmu04514: Cell adhesion molecules (CAMs), mmu04145: Phagosome and mmu00980: Metabolism of xenobiotics by cytochrome P450.

In the PAG part, the upregulated genes dealt with NP stimulation were mainly enriched in pathways as mmu04974: Protein digestion and absorption, mmu04512: extracellular matrix (ECM)-receptor interaction, and mmu04151: PI3K-Akt signaling pathway, whereas the downregulated genes were mainly enriched in pathways as mmu00591: Linoleic acid metabolism and mmu00590: Arachidonic acid metabolism.

In the PFC part, the upregulated genes dealt with NP stimulation were mainly enriched in pathways as mmu04060: Cytokine-cytokine receptor interaction, mmu04080: Neuroactive ligand-receptor interaction, mmu04668: tumor necrosis factor (TNF) signaling pathway and mmu04062: Chemokine signaling pathway whereas the downregulated genes were not found to be enriched in any metabolic pathways (Figure 5).

The result of the protein-protein interaction network (PPI) analysis showed that there were strong interactions among these DEGs enriched in the above pathways, indicating their potential impact on the molecular mechanism of the brain dealt with NP stimulation. (Figure 5)

3.3 Transcript factors analysis

The transcript factors of the DEGs of the brain dealt with NP stimulation were analyzed (Figure 6). There were 135 transcript factors for the upregulated genes, among which 27 transcript factor genes were found to play regulatory roles in all three parts of the brain (Figure 6a). For the downregulated genes, there were 98 transcript factors and among which one gene, SMAD4, was found to play regulatory roles in all three parts of the brain (Figure 6b).

The number of the genes that were regulated by corresponding transcript factors was further analyzed. Among the upregulated genes of the brain dealt with NP stimulation, SP1, MYC, CTNNB1, CREB1, and NFKB1 were found to have a stronger effect and the number of the genes they regulated was larger (Figure 6c), whereas SP1, CREB1, CTNNB1, JUN, and GLI1 genes were found to regulate a larger number of genes among the downregulated genes (Figure 6d). Therefore, it can be concluded that SP1, MYC, CTNNB1, CREB1, and JUN were the most important transcript factors in the regulation of brain genes dealt with NP stimulation.