Identification and experimental validation of the spinal cord hub genes of aging and neuropathic pain

doi:10.21203/rs.3.rs-3649254/v1

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Identification and experimental validation of the spinal cord hub genes of aging and neuropathic pain

https://doi.org/10.21203/rs.3.rs-3649254/v1

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Pain is considered the most frequent health problem encountered by the elderly. In this study, we used bioinformatics to analyze hub genes related to aging and NP, in order to identify more effective targets for treating NP in older adults. Aging and neuropathic pain (NP) datasets were downloaded from the Gene Expression Omnibus (GEO) database for transcriptome difference analysis. Gene Ontology (GO) functional annotation analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of overlapping genes between aging and NP datasets were made. We constructed the protein-protein interaction (PPI) network based on the STRING database for identification of hub genes, and used the miRDB database to predict the miRNAs that regulated the hub genes. Real-time quantitative polymerase chain reaction (RT-qPCR) analysis was made to verify the expression changes of the hub genes in the spinal cord of aging rats and spared nerve injury (SNI) rats. A total of 56 overlapped differentially expressed genes (DEGs) were identified from GSE18803 and GSE3305 datasets. Eight hub genes were identified by the CytoHubba plugin and MCODE plugin in Cytoscape software, and qPCR confirmed that 6 of them were expressed in the spinal cords of NP and aging rats. Protein tyrosine phosphatase receptor type C (PTPRC), integrin subunit alpha M (ITGAM), CD53, transforming growth factor-β1 (TGFB1), CD68 and CD74 are the hub genes in aging rats and SNI rats.

Aging

Neuropathic pain

Hub genes

Spinal cord

According to the International Academy of Pain, pain is an unpleasant sensory and emotional feeling accompanied by an underlying injury or disease (Raja et al. 2020). NP is one of the most common types of pain caused by damage or dysfunction of the somatosensory system (Treede et al. 2008). With the aging of the global population, managing pain in elderly patients has become increasingly important. A German study has shown that up to 50% of older adults are afflicted by varying degrees of pain, and approximately 30% of the elderly population suffer from moderate to severe pain(Wettstein and Tesarz 2023). Decreased liver and kidney function and coexisting disorders in older adults make the analgesic treatment complex and the treatment selection difficult, compared with the situations of younger adults (Luchting and Azad 2019). Persistent pain is associated with a decline in sleep quality and quality of life of older adults, who are recognized as the population that are the most susceptible to pain considering their high levels of functional loss and depression(Corran et al. 1997). Therefore, it is necessary to explore the unknown pathophysiologic processes and key molecular mechanisms in elderly NP patients.

With the continuous development of high-throughput sequencing technology, bioinformatics analysis has become one of the most important means to explore the underlying mechanisms of diseases (Kanehisa and Bork 2003). DNA microarray is a quartz wafer that contains probe sequences, which detect the expression of corresponding DNAs by situ hybridization (Ragoussis and Elvidge 2006). Currently, microarray analysis has been widely used for high-throughput gene expression detection. It assists researchers in the identification of key molecules associated with disease processes, and provides a new approach to the identification of clinical diagnostic markers for diseases.

The aim of this study is to explore the genes and molecular pathways that play a key role in elderly patients with NP using bioinformatics. Firstly, aging and NP datasets were downloaded from the GEO database, and aging-NP differentially expressed genes (ANPDEGs) were identified by using the R package “limma” and Venn diagrams. Then, GO functional annotation analysis and KEGG pathway enrichment analysis were made to determine the potential biological functions and pathways of ANPDEGs. CytoHubba software and MCODE plugin were used to identify hub genes and key sub-networks from the PPI network constructed based on ANPDEGs. The miRDB database was used to predict the upstream miRNAs that regulated the hub genes. Finally, the expression levels of the hub genes were detected in the dorsal horn of the spinal cord of SNI rats and aging rats.

2.1 Microarray datasets

As an international public database created by the National Center for Biotechnology Information (NCBI), the GEO database contains high-throughput microarray data, second-generation sequencing and other sequencing data submitted by research institutes around the world. We retrieved relevant datasets from the GEO database using the keywords "neuropathic pain" and "aging". The datasets that met the following criteria were included in this study. (i) The NP group includes peripheral nerve injury-induced NP rats older than 8 weeks. (ii) The aging group includes rats older than 18 mouths. (iii) Datasets contain both treatment and control groups, each containing more than 3 rats. (iv) The samples are collected from the central nerve system (CNS) of rats. Finally, we screened the NP dataset GSE18803 and the aging-related dataset GSE3305. The specific information of the datasets is summarized in the table below.

Table 1

The specific information of the datasets.
	Treatment group	Sample number	Sample source
GSE18803	SNI	SNI group n = 6 Sham group n = 6	Spinal cord levels L4-L6 in rats
GSE3305	Age ≥ 30 months	Aged group: n = 3 Young group: n = 3	Spinal cord

2.2 Identification of ANPDEGs

We used the "GEOquery" and "Biobase" packages to download the gene expression matrices, annotation information and clinical information of the GSE18803 and GSE3305 datasets, and to transform the probe IDs to gene names. If multiple probes were used to detect the same gene, the average value of multiple probes was taken as the final expression of the gene. After deleting the duplicate measurement data, we used the R package "limma" for differential analysis, and obtained up-regulated and down-regulated genes. The gene with |logFC|༞0.5 and P-value༜0.05 were identified as DEGs. Venn diagrams were created online on the website VENNY 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/index.html), and the ANPDEGs were finally identified by overlapping the DEGs of GSE18803 and GSE3305 datasets.

2.3 GO functional annotation analysis and KEGG pathway enrichment analysis

The DAVID database (https://david.ncifcrf.gov/) was used for GO functional annotation analysis and KEGG pathway enrichment analysis. The ANPDEGs identified were entered into the DAVID database to obtain the cellular mechanisms and pathways associated with these genes.

2.4 Construction of the PPI network and identification of hub genes

The ANPDEGs were imported into the STRING database for the construction of the PPI network, which was then visualized by the Cytoscape software. The ANPDEGs were analyzed by using the CytoHubba plugin in Cytoscape software, and then the top 10 genes ranked by scores were overlapped with the significant module genes identified by the MCODE plugin, so as to obtain the hub genes.

2.5 Related miRNA prediction

The miRDB database (https://mirdb.org) was used to predict the miRNAs paired with the hub genes. The miRNA-ANPDEG network was visualized by Cytoscape.

2.6 Animals

All animal experiments were approved by the Animal Ethics Committee of Zhujiang Hospital of Southern Medical University. 32 adult male Sprague Dawley rats (weighing 200–250 g) were purchased for the RT-qPCR analysis. The rats were divided into 4 groups, namely, sham surgery group (n = 8), SNI group (n = 8), young group (n = 8), and aged group (n = 8). Rats were anesthetized with 2% tribromoethanol (290 mg/kg) during surgery. The aged group was intraperitoneally injected with D-galactose (300 mg/kg/day) for 6 weeks, and the young group was injected with the 0.9% saline solution. Spinal cord levels L4-L6 were collected from SNI and sham surgery rats on the 7th day after surgery.

2.7 RT-qPCR analysis

RNA was isolated by Trizol reagent from the spinal cord levels L4-L6 at the operated side of each rat. Gene differential expression multiplicity was calculated using the 2-∆∆CT method. The primer sequences are shown in supplementary information.

2.8 Statistical analysis

Graphpad Prism 9 was used for statistical analysis and plotting. Data were represented with mean ± SEM. Student's t-test was conducted to analyze the expression of the hub genes. P < 0.05 indicated statistically significant difference.

3.1 Normalization of the expression of genes

Median normalization of the data downloaded from the GEO database was performed to ensure the reliability of results (Fig. 1A and B).

3.2 Identification of ANPDEGs

The R package "limma" was used to analyze the DEGs between the experimental group and the control group. We identified 524 DEGs in the GSE18803 dataset, including 401 up-regulated and 123 down-regulated DEGs (Fig. 2A). A total of 645 DEGs were identified in the GSE3305 dataset, including 306 up-regulated and 339 down-regulated DEGs (Fig. 2B). The top 100 genes ranked by |logFC| values were visualized in heat maps (Fig. 2C and D). It was found that 50 up-regulated genes and 6 down-regulated genes were expressed in both datasets (Fig. 2E and F).

3.3 GO functional annotation analysis and KEGG pathway enrichment analysis

The ANPDEGs were entered into the DAVID database for the GO functional annotation analysis (Fig. 3A) and KEGG pathway enrichment analysis (Fig. 3B). The most enriched BP GO term, CC GO term, and MF GO term were positive regulation of the ERK1 and ERK2 cascade (GO: 0070374), cell surface (GO: 0009986) and macromolecular complex binding (GO: 0044877), respectively. Furthermore, the most significant pathway was tuberculosis (rno05152), according to the KEGG pathway enrichment analysis.

3.4 Construction of the PPI network and identification of hub genes

The STRING database was used to construct the PPI network, which contained 34 nodes and 76 edges (Fig. 4A). The top 10 genes were determined based on the scores calculated by the MCC algorithm of the CytoHubba plugin (Fig. 4B). Significant module genes were identified by the MCODE plugin in Cytoscape (Fig. 4C). We overlapped the top 10 genes and the significant module genes to obtain the hub genes, which were PTPRC, CD53, CD74, CD68, ITGAM, LGALS3, TGFB1, and ICAM1.

3.5 miRNA-mRNA network

miRNAs are small endogenous non-coding RNAs of about 21–25 nucleotides in length. miRNAs combine with the 3'-untranslated region of target mRNAs to induce mRNA translational inhibition or degradation, thereby suppressing the expression of target proteins (Hayashi et al. 2008). An mRNA-miRNA network was constructed based on the miRDB database. It was predicted that 51 miRNAs played a role in regulating the expression of the hub genes. The network was visualized by Cytoscape (Fig. 5).

3.6 Confirmation of ANPDEG expression in NP and aging rats

To confirm the expression of the hub genes in NP and aging rats, qPCR was performed using spinal cord tissues from SNI rats and aging rats. The expression of PTPRC (Fig. 6A), ITGAM (Fig. 6B), CD53 (Fig. 6C), TGFB1 (Fig. 6D), CD68 (Fig. 6E), and CD74 (Fig. 6F) was significantly increased in SNI rats and aging rats. The expression of ICAM was up-regulated in the SNI and aging rats, but no statistically significant difference was observed (Fig. 6G). The expression of LGALS3 in the SNI group and aging group was not significantly different from that in the corresponding control groups (Fig. 6H).

With the accelerated aging of the global population, aging-related diseases have received increasing attention. Pain is acknowledged as the commonest problem in the elderly. Approximately 25–76% of the elderly population suffer from chronic pain, and up to 90% of elderly patients require hospitalized care(Abdulla et al. 2013; Boerlage et al. 2008; Torrance et al. 2013). The aging process is accompanied by physiological changes in multiple systems. Thus, the elderly and the young respond significantly differently to medications (Mullins et al. 2022). It is difficult for doctors to decide which analgesic medication regimen to use to treat elderly patients with concurrent underlying diseases as they have to take the patients’ adverse reactions to drugs into account(Liu and Chong 2022). To find the key genes involved in aging and NP is particularly urgent. With the rapid development of modern biotechnology, high-throughput sequencing emerges as an important tool for analyzing the potential mechanisms of disease. In this study, we analyzed aging- and NP-related datasets from the GEO database and explored potential therapeutic targets of NP in the elderly.

In this study, PTPRC, ITGAM, CD53, TGFB1, CD68 and CD74 were identified as hub genes associated with both aging and NP. It should be notated that all of these genes except TGFB1 were related to microglia. PTPRC, also known as CD45, is recognized as a therapeutic target for many immune system disorders(Rheinländer et al. 2018). CD45 is expressed in all hematopoietic stem cell-derived cells (Al Barashdi et al. 2021). Microglia are derived from hematopoietic stem cells in the bone marrow. An elevated level of CD45 has been confirmed in Alzheimer's disease and multiple sclerosis (Johnston et al. 2001; Licastro et al. 1998). Meanwhile, lowering the CD45 level inhibits microglial activity (Tan et al. 2000a; Tan et al. 2000b). ITGAM, a gene encoding CD11b, is often used as a marker for microglia (Ochocka et al. 2021). CD53 is an adhesion molecule expressed on the membrane of immune-only cells and it is involved in the process of adhesion/migration of immune cells to the CNS (Dunlock 2020). CD68 is often used as a lysosomal marker and deemed closely related to phagocytosis of microglia (Walker and Lue 2015). The expression of CD68 is up-regulated in phagocytosis-activated microglia (Zotova et al. 2013). CD74 is a classical marker of microglial activation and it is released from neurons during senescence.(Walker and Lue 2015). The macrophage migration inhibitory factor (MIF) binds to CD74 on microglia, thereby inducing microglia polarization toward M1(Jin et al. 2021). Studies show that TGFB1 plays a protective role against NP, and high TGFB1 levels inhibit neuroinflammatory responses and alleviate pain in NP rats (Chen et al. 2013). In aged mice, the up-regulated expression of TGFB1 suppresses the production of new neurons in the hippocampus (Buckwalter et al. 2006). However, the role of TGFB1 in the spinal cord of aged animals remains unknown. The marked upregulation of these hub genes in the spinal cord of aging and SNI rats suggests that microglial activation may play a key role in NP and aging.

In recent years, an increasing number of studies have confirmed that microglia play an irreplaceable role in aging and NP (Grassivaro et al. 2021; Hao et al. 2007; Ho et al. 2020). Microglia are a kind of macrophage widely distributed in the CNS. In the CNS of healthy animals, most microglia are in a quiescent state and involved in maintaining homeostasis and immune surveillance. When the homeostasis of the CNS is disrupted, microglia are activated and converted into two types, namely M1 (classical activation) and M2 (neuroprotective activation)(Atta et al. 2023). If primary afferent neurons are injured, microglia polarize to M1 phase, migrating to and accumulating at the injury neurons. These cells release multiple pro-inflammatory factors, which increase neuronal sensitivity and affect the synaptic transmission process of neurons, leading to the central sensitization of NP patients(Domoto et al. 2021). In the CNS of aged animals, microglia are activated, and the cytoplasmic structure of these cells shows the characteristics of senescence and dystrophy (Streit et al. 2008). In age-related neurodegenerative lesions such as AD and PD, activated microglia are destructive, according to previous reports (Flanary et al. 2007; Kim and Joh 2006). Senescent microglia migrate at a reduced rate when nerve injury occurs, and the inflammatory response caused by microglia lasts for a longer time (Damani et al. 2011). These studies show that the long-term pro-inflammatory effects of activated senescent microglia in the CNS of senescent animals after nerve injury may play a critical role in aging and NP considering their malnutrition and decreased motor function.

In this study, we downloaded the NP dataset GSE18803 and the aging dataset GSE3305 from the GEO database to identify the aging- and NP-related hub genes, which were PTPRC, CD53, CD74, CD68, ITGAM, and TGFB1. The hub genes were validated using NP and aging animals. Our findings may provide potential evidence for the association of aging with NP and new ideas for the treatment of NP in the elderly.

There are still some shortcomings in this study. Firstly, we only used one aging dataset and one NP dataset, so the number of samples in the datasets may be insufficient. More microarray datasets are still needed for subsequent validation. Secondly, this study only focused on the spinal cord levels in the CNS of aging and NP animal, but ignored the brain.

Funding

This work was supported by the National Natural Science Foundation of China (No. 8272526); Guangdong Basic and Applied Basic Research Foundation, China (No.2021A1515011042).

Author information

Author and Affiliations

Department of Rhabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.

Jiahui Pang, Yubai Zhao, Ziwei Hu, Yin Xu, Xinli Liu, Yingxuan Hu, Wu Wen

Contributions

All authors participated to the study conceptualization and design. Jiahui Pang and Yubai Zhao written the first draft; Jiahui Pang, Ziwei Hu managed the animal experiments; All authors read and approved the first manuscript. Wen Wu provide financial support for this study.

Corresponding author

Correspondence to Wen Wu.

Ethics declarations

Ethics Approval

The animal experiments was approved by the Experimental Animal Ethics Committee of Zhujiang Hospital Southern Medical University (No.LAEC-2022-111).

Competing Interests

The authors declare no competing interests.

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No competing interests reported.

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Identification and experimental validation of the spinal cord hub genes of aging and neuropathic pain

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Version 1

Abstract

Figures

1. Introduction

2. Materials and methods

2.1 Microarray datasets

2.2 Identification of ANPDEGs

2.3 GO functional annotation analysis and KEGG pathway enrichment analysis

2.4 Construction of the PPI network and identification of hub genes

2.5 Related miRNA prediction

2.6 Animals

2.7 RT-qPCR analysis

2.8 Statistical analysis

3. Result

3.1 Normalization of the expression of genes

3.2 Identification of ANPDEGs

3.3 GO functional annotation analysis and KEGG pathway enrichment analysis

3.4 Construction of the PPI network and identification of hub genes

3.5 miRNA-mRNA network

3.6 Confirmation of ANPDEG expression in NP and aging rats

4. Discussion

Declarations

References

Additional Declarations

Supplementary Files

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