Network-based global analysis of the cellular senescence process and senescence phenotype in the peripheral sensory neurons of the dorsal root ganglia

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Senescent neurons are neurons with arrested cell cycle that have undergone cellular senescence but remain in the tissue and play various biological roles. To understand the accumulation of senescent neurons in the DRG during aging, we aimed to elucidate the mechanism that induces cellular senescence in DRG neurons and the role of senescent DRG neurons. We integrated multiple public transcriptome datasets for DRGs, which represent cell bodies in neurons, and sciatic nerve, which represents axon in neurons, using network medicine-based bioinformatics analysis to account for axon-cell body interaction involved in cellular senescenc. Network medicine-based bioinformatics analysis revealed that age-related Mapk3 decline leads to impaired cholesterol metabolism and biosynthetic function in axons, resulting in compensatory upregulation of Srebf1 , a transcription factor involved in lipid and cholesterol metabolism, which in turn leads to CDKN2A-mediated cellular senescence. Furthermore, this analysis revealed that senescent DRG neurons develop a senescence phenotype characterized by activation of antigen-presenting cells via upregulation of Ctss as a hub gene. B cells inferred as antigen-presenting cells activated by Ctss , and CD8-positive T cells inferred as cells that receive antigen presentation from the B cells. dorsal root ganglion peripheral nerve cellular senescence aging transcriptome bioinformatics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Peripheral sensory nerves consist of the dorsal root ganglia (DRGs), axons (e.g., sciatic nerves (SNs)), and axon terminals associated with sensory receptors. They sense stimuli from the body’s external environment (touch, pain, temperature, pressure, etc.) and internal conditions (muscle tone, tendon and ligament elongation, etc.) and transmit them to the central nervous system [ 1 , 2 ]. The prevalence of peripheral sensory dysfunction increases with age, resulting in restrictions to healthy and fulfilling living [ 1 – 3 ]. Although the reasons for age-related decline in sensory function have been interpreted, the essential mechanisms of age-related degeneration of peripheral sensory nerves resulting in sensory dysfunction remain elusive. Cellular senescence is a crucial biological process that explains age-related degeneration of peripheral sensory nerves. Cells undergoing cellular senescence exhibit telomere shortening and cell cycle arrest mediated by factors that control cancer, such as p16, p53, and p21 [ 6 – 8 ]. Furthermore, they trigger senescence-associated secretory phenotype, which secretes various factors, including inflammatory cytokines, chemokines, and extracellular matrix-degrading enzymes [ 9 – 11 ]. In aged DRGs of the peripheral nervous system, the number of senescent neurons increases, and sensory function decreases [ 12 ]. In recent years, senescent cell-depleting drugs (senolytic drugs) have been developed to address the various adverse effects of senescent cell accumulation and have significantly improved aging-associated decline in tissue function [ 13 – 16 ]. Senolytic drugs also improve sensory function by removing senescent neurons from aged DRGs [ 12 ]. However, the use of senolytic drugs has the disadvantage that, in tissues where senescent cells accumulate, the removal of a large number of senescent cells reduces the number of cells that build the tissue thus affecting tissue structure integrity [ 17 , 18 ]. Therefore, inhibiting cellular senescence of normal DRG neurons, suppressing the accumulation of aged DRG neurons, and suppressing the inflammatory response caused by aged DRG neurons is important in preventing age-related degeneration of sensory nerves. With the development of science and technology in recent years, much transcriptomic analysis data has been published, and many transcriptome datasets target peripheral sensory nerves. However, no datasets target the entire neuronal structure such as the cell body, axons, and axon terminal, and only datasets that individually target the neuron cell body (DRG) and axons (SNs) have been compiled. Therefore, we aimed to elucidate the molecular mechanisms underlying the cellular senescence process and senescence phenotype of peripheral sensory neurons by conducting bioinformatics analysis that integrates transcriptomic data for DRG neurons and peripheral nerve axons from which DRG neurons extend. Specifically, recognizing that genes and proteins do not function independently but rather play biological roles through interactions with other genes and proteins, we employed gene set enrichment analysis (GSEA) and network analysis based on the principles of network medicine theory [ 19 ]. Based on the functional correlation with cellular senescence factors, we identified the network that determines the cellular senescence process and senescence phenotype of sensory neurons as well as the core genes of the network. 2. Material and Methods 2.1. Extraction of transcriptomic datasets Transcriptomic datasets were searched using the following keywords in the Gene Expression Omnibus ( https://www.ncbi.nlm.nih.gov/geo/ ), a public repository provided by the National Center for Biotechnology Information. #1 “ganglia, spinal”[MeSH Terms] OR dorsal root ganglion[All Fields] #2 (“peripheral nerves”[MeSH Terms] OR peripheral nerve[All Fields]) OR (“sciatic nerve”[MeSH Terms] OR sciatic nerve[All Fields]) #3 (“aging”[MeSH Terms] OR aging[All Fields]) OR (“aging”[MeSH Terms] OR senescence[All Fields]) #4 “#1” OR “#2” #5 “#3” AND “#4” Transcriptomic datasets were included in this study based on the following criteria: 1) comparison between young and aged groups and 2) analysis targeting the DRG or SN. For each extracted dataset, the raw count data of each sample were normalized to transcripts per kilobase million (TPM). Differential expression analysis between two groups was conducted using the R/Bioconductor package DESeq2 [ 20 ]. In DESeq2, statistical significance was assessed using the Wald test, with a p-value cutoff of 0.05. Common differentially expressed genes (DEGs) detected in each dataset for the DRG and SN were extracted. 2.2. Evaluation of cellular senescence using transcriptomic datasets Cellular senescence is induced by increased expression of tumor suppressor genes. The major tumor suppressor signaling pathways include the p16/Rb and p53/p21 signaling pathways, with p16, p53, and p21 being the key cellular senescence factors [ 21 ]. Targeting the CDKN2A , Trp53 , and CDKN1A genes, which encode p16, p53, and p21, respectively, the fold change was calculated by comparing the extracted dataset between the young and aged groups. Datasets where the log2 fold change (log2FC) of the target genes was greater than zero were defined as aged datasets and utilized for bioinformatics analysis. 2.3. Weighted gene co-expression network analysis (WGCNA) and characterization of gene modules by protein–protein interaction (PPI) network From biological and statistical perspectives, genes with very low counts in RNA sequencing (RNA-seq) or low intensity in microarray data were removed prior to downstream analysis. The aged dataset comprised one dataset targeting the DRG (GSE149770) and two datasets targeting the SN (GSE137504 and GSE198669). To conduct the WGCNA, a dataset targeting SN with a larger sample size was utilized to ensure the statistical reliability and stability of the co-expression modules. Thirty-six samples (aged group: 24, young group: 12) from the two SN datasets were merged, and their raw data were normalized to TPM. These samples were filtered using hierarchical clustering as employed in the WGCNA tutorial (36 × 35 samples). The analyzed genes were filtered using the top 3000 genes with mean absolute deviation. The answer to “Can both logarithms of the order distribution be approximated by a straight line?” was used to determine the optimal soft threshold (= 28), considering the scale-free nature of the network. A topological overlap matrix (TOM) was formulated based on adjacency values to calculate corresponding dissimilarity (1-TOM) values. Module identification was accomplished using the dynamic tree cut method by hierarchically clustering genes using 1-TOM as the distance measure with a minimum size cutoff of 30 and a deep split value of four for the resulting dendrogram. Modules that are strongly correlated with each other in the eigengene module, which is the first principal component of the principal component analysis of the gene expression matrix of the modules, were merged. A module preservation function was used to verify the stability of the identified modules by calculating module preservation and quality statistics using the WGCNA package [ 22 ]. We evaluated the concordance between the genes in the module constructed using WGCNA and the DEGs obtained from the DRG dataset. The module constructed from the genes upregulated in the aged group in the dataset was defined as the aged module. Likewise, the young module was constructed from the genes in the young group. Among the DEGs common to the DRG and SN datasets, those with elevated expression in the aged group were targeted to construct protein–protein interaction (PPI) networks using the STRING database ( https://string-db.org/ ) [ 23 ]. In the STRING database, the “Protein with value/Rank” option was used, with gene symbols as identifiers and Log2FC as values. The genes constituting the networks were considered representative of their modules, and the characteristics of each module with respect to cellular senescence were predicted based on the direction of the interactions. 2.4. Network and hub gene analysis Among the modules constructed from the WGCNA, a PPI network and weighted gene co-expression network were constructed for the modules related to cellular senescence, and the networks were analyzed using Cytoscape software (version 3.10.1). The topology parameters (i.e., degree centrality and betweenness centrality) of a particular network were determined by the “analyze network” feature in Cytoscape software. In the constructed network, genes present at the center of the network and functional hubs had high degree and betweenness centralities. Therefore, the product of the degree centrality and betweenness centrality was used to rank the genes in the module, and genes with high ranks in both networks were designated as hub genes. 2.5. Gene set enrichment analysis GSEA was performed on all genes detected in each dataset as described using the GSEA web tool provided by Broad Institute Website ( https://www.gsea-msigdb.org/gsea/index.jsp ) [ 24 ]. The database of gene sets used in GSEA was WikiPathways (wikipathways.org), and the degree of enrichment to annotations for biological processes was quantified as the enrichment score (ES). To facilitate comparison and interpretation between datasets with different tissues (DRG and SN) and sample sizes, the normalized enrichment score (NES) was calculated by normalizing the ES according to the gene set size. The datasets were compared using NES. The minimum and maximum criteria for selecting gene sets from the collection were 10 and 500 genes, respectively. Leading-edge analysis was performed on annotations with high NES commonly found in the aged group of each dataset after GSEA to determine the core genes that defined the subset of genes with positive NES. These leading-edge genes were used as seed genes for network propagation using random walk with restart (RWR). 2.6. Network propagation using random walk with restart algorithm RWR was performed using the R/Bioconductor package RandomWalkRestartMH 1.22.0 [ 24 ]. RWR simulated a walker that started from one node or a set of nodes (seed nodes) in a network and moved randomly to deliver probabilities on the seed node to other nodes. In this study, walkers were simulated in an aged module with the seed gene as the starting node. For each node (gene in the aged module), a gene expression value was assigned as the initial score and a rank score was calculated as the establishment distribution. The higher the rank score, the more important each node (gene) within the module was. 2.7. Prediction of the abundance of infiltrated immune cells using CIBERSORT analysis We used the analytical tool CIBERSORTx ( https://cibersortx.stanford.edu/ ) provided by Alizadeh Lab and Newman Lab to estimate the abundance of each immune cell type in the aged and young groups using the CIBERSORT algorithm [ 25 ]. We used LM22, which has gene expression patterns for 22 immune cell types provided by the developer, as a reference set. The expression values of the DEGs for each sample were used as expression data for estimation. The analytical parameter permutation for significance analysis was set to 1000, as recommended by CIBERSORTx. 2.8. Statistical analysis All statistical analyses were performed using SPSS version 25 for Windows (Japan IBM, Japan). Data were presented with means and 95% confidence intervals (mean ± 95%CI). Two-tailed Student’s t-test and two-tailed Wilcoxon rank-sum test were performed to compare the number of infiltrated immune cells in the young and aged group. Complete-case analysis was performed for missing data, such as genes that were not adequately detected by RNA-seq. For all statistical tests, p < 0.05 was considered statistically significant. 3. Results 3.1. Cellular senescence of peripheral sensory neurons is induced by CDKN2A We obtained comprehensive gene expression datasets by comparing the young and aged groups for both the DRG and SN. There were four datasets, with two datasets each for the DRG (GSE63651, 149770) and SN (GSE137504, 198669) (Fig. 1 a). Three and one dataset contained tissue samples from C57BL/6J mice and F344 rats, respectively. The log2FC for the cellular senescence markers in the extracted dataset were CDKN2A (DRG: 0.50, SN: 1.39 ± 1.05), Trp53 (DRG: 0.42, SN: 0.18 ± 0.36), and CDKN1A (DRG: 0.26, SN: 0.13 ± 1.05) (Fig. 1 b). Considering these results, we generated an aged dataset based on CDKN2A (GSE149770, 137504, and 198669) (Fig. 1 c) which contained 1188 and 240 DEGs in the DRG (aged-up: 518, aged-down: 670) and SN (aged-up: 108, aged-down: 132), respectively. Among these, 45 DEGs were common to both DRG and SN. Among these 45 genes, 15 were upregulated in the aged group, including CDKN2A (Fig. 1 d). 3.2. Cathepsin S is a hub gene that characterizes the phenotype of aged sensory neurons WGCNA was performed on 35 samples (aged group: 23, young group: 12) from two datasets targeting the SN (Fig. 2 a). The WGCNA module was divided into six modules. Modules 2 to 5 were aged modules. Modules 1 and 6, which had the highest and lowest number of dependent genes, respectively, were young modules (Fig. 2 b). Module 4 had the highest percentage of genes common to the DEGs extracted from the DRG dataset. Among the DEGs common to the DRG and SN datasets, a PPI network was constructed for 15 genes that were upregulated in the aged group. The constructed PPI network identified module 1 as the module that induces the cellular senescence factor CDKN2A, which causes cellular senescence, and module 4 as the module that receives action from CDKN2A and thus, the network expresses the characteristics of aged sensory neurons with cellular senescence phenotypically (Fig. 2 c). A PPI network was constructed for module 4, and genes were ranked by “degree.” Cathepsin S (Ctss), a cysteine protease involved in protein differentiation and processing, was identified as a candidate hub gene (Fig. 2 d). Furthermore, Ctss was inferred to be a hub gene in the weighted co-expression network constructed from 26 genes that were common to the DEGs in the DRG dataset of module 4 (Fig. 2 e). 3.3. Cathepsin S induces activity of antigen-presenting cells represented by microglia GSEA was performed using the DRG dataset to investigate the top 10 WikiPathways annotations enriched in the aged group in the order of increasing NES. An NES of 2.0 or higher and a smaller false discovery rate (FDR) generally indicate a significant and strong enrichment. In this analysis, “Microglia pathogen phagocytosis pathway” and “Tyrobp causal network in microglia” showed high NES and minimal FDR (Fig. 3 a). We identified five leading-edge genes common to these two annotations related to microglial activity, namely Ncf2 , Itgam , Nckap1l , Itgb2 , and Tyrobp (Fig. 3 b). Furthermore, these two annotations showed significant and higher enrichment in the aged group, not only in the DRG but also in the SN (Fig. 3 c). The expression of Ctss, which was identified as a hub gene in association with cellular senescence in module 4, increased with increasing CDKN2A expression (Fig. 3 d). The five genes identified as leading-edge genes that characterize aged sensory nerves were incorporated into module 4 as seed genes, and network propagation was performed starting from the seed genes. We found that many genes in module 4 were propagated to other genes when they started from the seed genes (Fig. 3 e). Ctss had a high rank score (rank score: 0.06). 3.4. Decreased cholesterol metabolism in the axon of aged sensory nerves activated the transcription factor Srebf1 in the cell body, thereby causing cellular senescence PPI network and weighted gene co-expression networks were constructed and analyzed for module 1, the cellular senescence-induction module that includes the transcription factor Srebf1, which induces CDKN2A. Specifically, 104 genes in module 1 common to DEGs in the DRG dataset were targeted. Mapk3 was identified as a hub gene from the PPI network analysis and was also highly ranked in the weighted gene co-expression network (Fig. 4 a). It was upregulated in the DRG but downregulated in the SN during senescence. In contrast, Srebf1 was consistently upregulated during senescence in the DRG and SN (Fig. 4 b). Srebf1 is an important transcription factor involved in lipid and cholesterol metabolism. Therefore, we investigated the WikiPathways annotations related to lipid and cholesterol metabolism. Two annotations related to cholesterol metabolism, namely “Cholesterol metabolism with Bloch and Kandutsch Russell pathway” and “Cholesterol biosynthesis,” were commonly included in the top 10 annotations that were significantly enriched in the SN of the young group. Therefore, these annotations were significantly less enriched in the SN of the aged group (NES: -1.95 ± 0.93, FDR: 0.009 ± 0.012 vs. NES: -2.11 ± 0.53, FDR: 0.00 ± 0.00) (Fig. 4 c). However, these annotations were not significantly enriched in the young group. We identified Fdps and Pmvk as the leading-edge genes associated with these two annotations. 3.5. Aged sensory nerves are infiltrated by B and CD8 + T cells through the axon Considering that aged sensory neurons are characterized by the activation of antigen-presenting cells, including microglia, these neurons may be infiltrated by antigen-presenting cells, such as B cells, macrophages, and dendritic cells, as well as by T cells, which interact with the presented antigens. Therefore, we used CIBERSORT analysis to estimate the percentage of immune cells in the DRG and SN of sensory nerves. The results showed vastly different percentages of immune cell infiltration in the DRG and SN in the young and aged groups (Fig. 5 a). In particular, among the antigen-presenting cells, macrophages and dendritic cells exhibited decreased infiltration rate, while B cells exhibited increased infiltration rate with aging. The seven T cell phenotypes inferred were CD8 + T cells, naïve CD4 + T cells, memory resting CD4 + T cells, memory activated CD4 + T cells, follicular helper T cells, regulatory T cells (Treg), and gamma delta T cells (Tγδ) (Fig. 5 b). T cells were mainly infiltrated in the SN and were less infiltrated in the DRG. In the SN, the proportion of CD8 + T cells and memory resting CD4 + T cells infiltrated was significantly increased in the aged group compared to the young group. 4. Discussion In this study, we identified the molecular mechanisms that induce cellular senescence in DRG neurons and the inflammation-induced phenotype of aged DRG neurons that have undergone cellular senescence (Fig. 6 ). We found that cellular senescence in DRG neurons was induced by increased expression of CDKN2A, which encodes the cellular senescence-inducing factor p16, which in turn was caused by increased expression of Srebf1 , a transcription factor involved in lipid and cholesterol metabolism. Although Srebf1 expression increases with aging, the senescence-induced gene network to which Srebf1 belongs primarily includes genes whose expression decreases with aging. This gene network includes Fdps and Pmvk , the leading-edge genes of the pathways involved in cholesterol metabolism and biosynthesis, which are significantly suppressed by aging. Furthermore, Mapk3 , a hub gene that plays a central role in this network, was suppressed by aging, suggesting that cholesterol metabolism and biosynthesis are regulated by Mapk3 . The conflicting results between Srebf1 and Mapk3 expression may be attributed to the possibility that Srebf1 expression may have increased to compensate for the Mapk3 -mediated decline in cholesterol metabolism and biosynthetic functions associated with aging. Mapk3 is a target gene for cancer therapy, and its increased expression activates cell proliferation and differentiation, whereas its suppression induces the expression of the cellular senescence marker p16 and accumulation of aged cells [ 27 , 28 ]. Srebf1 has been identified as a marker of cellular senescence in patients with bladder [ 29 ] and esophageal cancers [ 30 ]. Although decreased Mapk3 expression and increased Srebf1 expression are involved in cellular senescence, the pathways involved are unclear. However, in this study, two independent changes in gene expression thought to induce cellular senescence can be viewed as a series of molecular mechanisms associated with aging. Interestingly, reduced cholesterol metabolism and biosynthetic function, which is key to the molecular response that induces cellular senescence in DRG neurons, occurs only in the axons of DRG neurons and not the cell body. In DRG neurons, axons are subject to various stresses, which may induce cellular senescence of DRG neurons. In summary, the p16-mediated cellular senescence process in DRG neurons may be explained by the compensatory upregulation of Srebf1 for cholesterol metabolism and biosynthetic dysfunction via Mapk3 downregulation in axons. We also identified a senescence phenotype that characterizes aged DRG neurons. In this phenotype, Ctss functions as a hub gene and promotes the immune response by increasing the expression of antigen-presenting cell activators (Ncf2, Itgam, nckap1l, Itgb2, and Tyrobp). The molecular response that results in chronic inflammation in aged DRG neurons also occurs specifically in the axons. This is because T cells that recognizes the antigens presented by activated antigen-presenting cells infiltrate the SN but rarely infiltrate the DRG. Zhou et al. showed that CD8 + T-cell infiltration of sensory nerve axons occurs when aging and releases inflammatory cytokines, leading to age-related axonal degeneration and prolonged injury healing. Our study also identified CD8 + T cells as an infiltrating T-cell type that significantly increases with age in SNs [ 31 ]. In addition, Ctss , the hub gene identified in this study, is a plasma protein marker that predicts migratory deficits in elderly individuals, suggesting a link with aging [ 32 ]. In summary, aged DRG neurons may contribute to chronic inflammation in sensory nerve axons by activating antigen-presenting cells through Ctss upregulation, which in turn causes activation of CD8 + T cells. Chronic inflammation of nerve tissue accelerates tissue aging [ 33 – 35 ]. Our findings may provide the basis for research on the molecular mechanism underlying the “Inflammaging” of peripheral sensory nerves. A limitation of this study is that the inferred molecular mechanisms underlying the cellular senescence process and senescence phenotype have not been validated through in vitro and in vivo experiments. In particular, it remains to be verified whether the upregulation of Srebf1 in the cell body during the cellular senescence process is a compensatory response to reduced cholesterol metabolism in axons, specifically due to the inactivation of the mevalonate pathway. However, there is some evidence that a reduction in the mevalonate pathway causes the cellular aging process. This is evidenced by the use of statins, which inhibit the mevalonate pathway, leading to age-like declines in peripheral nerve function [ 36 , 37 ] and the involvement of cell membrane fluidity, which is regulated by the mevalonate pathway, in the cellular senescence process [ 38 , 39 ]. 5. Conclusion In peripheral sensory neurons, the suppression of Mapk3 with aging leads to impaired axonal cholesterol metabolism and biosynthesis, resulting in transcription factor Srebf1 -mediated CDKN2A activation and cellular senescence. Furthermore, aged peripheral sensory neurons exhibit a senescence phenotype that activates antigen-presenting cells via Ctss . These molecular mechanisms have been rigorously inferred from network analyses using transcriptomic data, and their validation by in vivo and in vitro experiments in future research will provide a foundation for understanding the aging of peripheral sensory neurons. Abbreviations 95%CI, 95% confidence interval;DEG, differentially expressed gene;DRG, dorsal root ganglion; ES, enrichment score; FDR, false discovery rate; GSEA, gene set enrichment analysis; log2FC, log2 fold change; NES, normalized enrichment score; PPI, protein–protein interaction; RNA-seq, RNA sequencing; RWR, random walk with restart; SN, sciatic nerve; TOM, topological overlap matrix; 1-TOM, dissimilarity in TOM; TPM, transcripts per kilobase million; WGCNA, weighted gene co-expression network analysis Declarations Ethics approval This is an in silico studies with publicly available transcriptome datasets. The Saitama Prefectural University Experimental Animal Ethics Committee has confirmed that no ethical approval is required. Consent to participate Not applicable. Consent to publication Not applicable. Availability of data and materials The datasets analysed during the current study are available in the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/), provided by National Center for Biotechnology Information. Competing Interests None Funding This project has received funding from the Grant-in-Aid for JSPS Fellows JP23KJ1789 and Saitama Prefectural University Research (SPUR) Grant. Author Contributions All authors contributed to the study conception and design. Material preparation and data collection and analysis were performed by Sora Kawabata. Hirotaka Iijima provided advice on data analysis, and Kenji Murata oversaw the entire study. The first draft of the manuscript was written by Sora Kawabata and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgements This work was supported by Grant-in-Aid for JSPS Fellows. We would like to thank Editage ( www.editage.jp ) for English language editing. 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Front Oncol 12:937951. https://doi.org/10.3389/fonc.2022.937951 Wang Y, Dai L, Huang R et al (2023) Prognosis signature for predicting the survival and immunotherapy response in esophageal carcinoma based on cellular senescence-related genes. Front Oncol 13:1203351. https://doi.org/10.3389/fonc.2023.1203351 Zhou L, Kong G, Palmisano I et al (2022) Reversible CD8 T cell-neuron cross-talk causes aging-dependent neuronal regenerative decline. Science 376:eabd5926. https://doi.org/10.1126/science.abd5926 Osawa Y, Semba RD, Fantoni G et al (2020) Plasma proteomic signature of the risk of developing mobility disability: A 9-year follow-up. Aging Cell 19:e13132. https://doi.org/10.1111/acel.13132 Büttner R, Schulz A, Reuter M et al (2018) Inflammaging impairs peripheral nerve maintenance and regeneration. Aging Cell 17:e12833. https://doi.org/10.1111/acel.12833 Gąsiorowski K, Brokos B, Echeverria V et al (2018) RAGE-TLR Crosstalk Sustains Chronic Inflammation in Neurodegeneration. Mol Neurobiol 55:1463–1476. https://doi.org/10.1007/s12035-017-0419-4 Pongratz G, Straub RH (2013) Role of peripheral nerve fibres in acute and chronic inflammation in arthritis. Nat Rev Rheumatol 9:117–126. https://doi.org/10.1038/nrrheum.2012.181 Baker SK, Tarnopolsky MA (2005) Statin-associated neuromyotoxicity. Drugs Today 41:267–293. https://doi.org/10.1358/dot.2005.41.4.908565 Svendsen T, de Krøigård K, Wirenfeldt T M, et al (2020) Statin use and peripheral nerve function-A prospective follow-up study. Basic Clin Pharmacol Toxicol 126:203–211. https://doi.org/10.1111/bcpt.13320 Lee S-M, Lee SH, Jung Y et al (2020) FABP3-mediated membrane lipid saturation alters fluidity and induces ER stress in skeletal muscle with aging. Nat Commun 11:5661. https://doi.org/10.1038/s41467-020-19501-6 Jeong J-H, Han J-S, Jung Y et al (2023) A new AMPK isoform mediates glucose-restriction induced longevity non-cell autonomously by promoting membrane fluidity. Nat Commun 14:288. https://doi.org/10.1038/s41467-023-35952-z Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Dec, 2024 Read the published version in Molecular Neurobiology → Version 1 posted Editorial decision: Revision requested 09 Oct, 2024 Reviews received at journal 09 Oct, 2024 Reviewers agreed at journal 29 Sep, 2024 Reviewers agreed at journal 28 Aug, 2024 Reviews received at journal 07 Aug, 2024 Reviewers agreed at journal 29 Jul, 2024 Reviewers invited by journal 13 Jun, 2024 Submission checks completed at journal 29 May, 2024 Editor assigned by journal 29 May, 2024 First submitted to journal 16 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4431608","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":312080943,"identity":"c6034d59-7dba-4dcc-997e-3293e5757f06","order_by":0,"name":"Sora Kawabata","email":"","orcid":"","institution":"Graduate School of Saitama Prefectural University","correspondingAuthor":false,"prefix":"","firstName":"Sora","middleName":"","lastName":"Kawabata","suffix":""},{"id":312080944,"identity":"21121700-a3a1-43a3-bd2d-82b225b7c765","order_by":1,"name":"Hirotaka Iijima","email":"","orcid":"","institution":"Schoen Adams Research Institute at Spaulding","correspondingAuthor":false,"prefix":"","firstName":"Hirotaka","middleName":"","lastName":"Iijima","suffix":""},{"id":312080945,"identity":"f06b0eb1-58b9-4b4a-a9ab-676d4ae4ec64","order_by":2,"name":"Naohiko Kanemura","email":"","orcid":"","institution":"Saitama Prefectural University","correspondingAuthor":false,"prefix":"","firstName":"Naohiko","middleName":"","lastName":"Kanemura","suffix":""},{"id":312080946,"identity":"2524b08f-acd4-4cad-ba23-1211d547eb0b","order_by":3,"name":"Kenji Murata","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYBADAyBOOPABxpUgpP4AVMvBGaRqYWDmIcZBujNyH37+UMNgzC/d8PCwzZ87iQ3shx8wWO7ArcXsRrqxxIFjDGaScw4kHM5te5bYwJNmwCB5Bp+WNAaJA2z/bQxuJAC1NBxObGDIYWCQbMOrhfnHgX8MNvYgLRZ/gFr43xDUwiZxsI3BzEACqIWBDahFgpAtZ56xWZztYzCWANpysLftmXGbxDODA3j9cjyN+UbFNwbD/hk5yR9+/Lkj28+f/PCxJJ4QQwI8CQygKGIDkoclG4jSwn4ArAUEGD8Sp2UUjIJRMApGBgAA2tBYOCBG/qoAAAAASUVORK5CYII=","orcid":"","institution":"Saitama Prefectural University","correspondingAuthor":true,"prefix":"","firstName":"Kenji","middleName":"","lastName":"Murata","suffix":""}],"badges":[],"createdAt":"2024-05-16 14:06:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4431608/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4431608/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12035-024-04666-8","type":"published","date":"2024-12-23T15:56:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":58102362,"identity":"2ded59b0-b3c0-4d12-a83c-7750fc05179c","added_by":"auto","created_at":"2024-06-11 06:54:04","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":742697,"visible":true,"origin":"","legend":"\u003cp\u003eCellular senescence of DRG neurons was induced by CDKN2A. (a) Schema for systematic extraction of transcriptome data. (b) Expression of cellular senescence factors CDKN2A, Trp53, and CDKN1A in the sciatic nerve (SN) and dorsal root ganglion (DRG). In the boxplots, horizontal lines indicated the mean and 95% CI, and white plots indicated the value of Log2FC for each group comparison. (c) Extraction of aged datasets. (d) Extraction of DEGs common in the datasets for DRG and SN\u003c/p\u003e","description":"","filename":"Fig.1..jpg","url":"https://assets-eu.researchsquare.com/files/rs-4431608/v1/3c9fef18a5f3f99b351932b0.jpg"},{"id":58103323,"identity":"8fc91524-63c6-4568-923b-ba0a972bcc97","added_by":"auto","created_at":"2024-06-11 07:02:04","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":863942,"visible":true,"origin":"","legend":"\u003cp\u003eWGCNA was conducted to identify gene modules that induce cellular senescence and senescence phenotypes in DRG neurons.\u003cstrong\u003e \u003c/strong\u003e(a) Analysis protocol of WGCNA for sciatic nerve. (b) WGCNA gene module and module characteristics. (c) Relationship between the cellular senescence factor CDKN2A and each gene module inferred from the PPI network. (d) Construction of module 4 PPI network and network analysis. The right image depicts the PPI network. (e) Construction of module 4 weighted co-expression network and network analysis\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4431608/v1/f8f1fce94173c28a0a45579b.jpg"},{"id":58103324,"identity":"549f326e-7f77-4be2-a967-18993760eeb7","added_by":"auto","created_at":"2024-06-11 07:02:04","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1090864,"visible":true,"origin":"","legend":"\u003cp\u003eCathepsin S-mediated activation of antigen-presenting cells exhibited a senescent phenotype in DRG neurons. (a) Enrichment analysis using GSEA. (b) Leading-edge analysis in high NES annotations. The red bars in the X-axis indicate the leading-edge genes in “Microglia-related annotations.” (c) Enrichment levels for microglia-related annotations. (d) Relationship between CDKN2A and Ctss. The age of the aged group was indicated by the size of the plot (the older the age, the larger the plot). (e) Network propagation using RWR. The figure on the right is a schematic of network propagation. The size of the rank score indicated its importance in the network\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4431608/v1/b7d035716f1bc92f01b13e54.jpg"},{"id":58102367,"identity":"aab60238-559b-4618-bfc1-6a77248f2cdf","added_by":"auto","created_at":"2024-06-11 06:54:05","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":845306,"visible":true,"origin":"","legend":"\u003cp\u003eMapk3-mediated impairment of cholesterol metabolism and biosynthesis in axons induced cellular senescence.\u003cstrong\u003e \u003c/strong\u003e(a) Network analysis and inference of cellular senescence-inducing hub genes in the cellular senescence induction module. (b) Relationship between CDKN2A and Mapk3 and between CDKN2A and Srebf1. (c) Enrichment levels in the WikiPathway annotation that were significantly enriched in the young group commonly in the dataset for SN and the leading-edge genes in that annotation\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4431608/v1/1c044689f282a75be781e5d4.jpg"},{"id":58103810,"identity":"88f1cfd3-8133-433a-a15b-e6d9d0233c95","added_by":"auto","created_at":"2024-06-11 07:10:05","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":959105,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction of immune cells with aged DRG neurons. (a) Percentage of infiltrated immune cells estimated at each sensory nerve site (DRG or SN). In the left figure, the x-axis shows each sample for each group, and y-axis shows the percentage infiltration of 22 immune cells, including phenotypes. The right figure shows the estimated infiltration percentage of each antigen-presenting cell. The color shade indicates the phenotype of each antigen-presenting cell. Green indicates naïve B cells, memory B cells, and plasma cells in order, from darkest to lightest; yellow indicates M0, M1, and M2 macrophages in order, from darkest to lightest; and red indicates resting dendritic cells and activated dendritic cells in order, from darkest to lightest. (b) Group comparison of the infiltration proportion in each phenotype of T cells. Data are shown as boxplots. In these boxplots, boxes indicate the interquartile range and median (upper and lower bars indicate maximum and minimum values), and dot plots indicate sample value. Two-group comparisons were performed using a two-tailed Wilcoxon rank sum test, with p\u0026lt;0.05 considered statistically significant\u003c/p\u003e","description":"","filename":"Fig.5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4431608/v1/32420277a15355f8f5c975b9.jpg"},{"id":58102364,"identity":"339d1d5b-871c-4d26-9ff7-96dcf71c3635","added_by":"auto","created_at":"2024-06-11 06:54:04","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":502512,"visible":true,"origin":"","legend":"\u003cp\u003eThe cellular senescence process and senescence phenotype in peripheral sensory neurons\u003c/p\u003e","description":"","filename":"Fig.6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4431608/v1/bcbdee4620dc56349076fb1c.jpg"},{"id":72640249,"identity":"87b069cb-5cc7-4ab6-9483-624e38f0c0fa","added_by":"auto","created_at":"2024-12-30 16:03:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5586514,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4431608/v1/6317c824-1b5c-43fe-87ca-de51bb73b7d0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Network-based global analysis of the cellular senescence process and senescence phenotype in the peripheral sensory neurons of the dorsal root ganglia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePeripheral sensory nerves consist of the dorsal root ganglia (DRGs), axons (e.g., sciatic nerves (SNs)), and axon terminals associated with sensory receptors. They sense stimuli from the body\u0026rsquo;s external environment (touch, pain, temperature, pressure, etc.) and internal conditions (muscle tone, tendon and ligament elongation, etc.) and transmit them to the central nervous system [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The prevalence of peripheral sensory dysfunction increases with age, resulting in restrictions to healthy and fulfilling living [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although the reasons for age-related decline in sensory function have been interpreted, the essential mechanisms of age-related degeneration of peripheral sensory nerves resulting in sensory dysfunction remain elusive.\u003c/p\u003e \u003cp\u003eCellular senescence is a crucial biological process that explains age-related degeneration of peripheral sensory nerves. Cells undergoing cellular senescence exhibit telomere shortening and cell cycle arrest mediated by factors that control cancer, such as p16, p53, and p21 [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Furthermore, they trigger senescence-associated secretory phenotype, which secretes various factors, including inflammatory cytokines, chemokines, and extracellular matrix-degrading enzymes [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In aged DRGs of the peripheral nervous system, the number of senescent neurons increases, and sensory function decreases [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, senescent cell-depleting drugs (senolytic drugs) have been developed to address the various adverse effects of senescent cell accumulation and have significantly improved aging-associated decline in tissue function [\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Senolytic drugs also improve sensory function by removing senescent neurons from aged DRGs [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, the use of senolytic drugs has the disadvantage that, in tissues where senescent cells accumulate, the removal of a large number of senescent cells reduces the number of cells that build the tissue thus affecting tissue structure integrity [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Therefore, inhibiting cellular senescence of normal DRG neurons, suppressing the accumulation of aged DRG neurons, and suppressing the inflammatory response caused by aged DRG neurons is important in preventing age-related degeneration of sensory nerves.\u003c/p\u003e \u003cp\u003eWith the development of science and technology in recent years, much transcriptomic analysis data has been published, and many transcriptome datasets target peripheral sensory nerves. However, no datasets target the entire neuronal structure such as the cell body, axons, and axon terminal, and only datasets that individually target the neuron cell body (DRG) and axons (SNs) have been compiled. Therefore, we aimed to elucidate the molecular mechanisms underlying the cellular senescence process and senescence phenotype of peripheral sensory neurons by conducting bioinformatics analysis that integrates transcriptomic data for DRG neurons and peripheral nerve axons from which DRG neurons extend. Specifically, recognizing that genes and proteins do not function independently but rather play biological roles through interactions with other genes and proteins, we employed gene set enrichment analysis (GSEA) and network analysis based on the principles of network medicine theory [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Based on the functional correlation with cellular senescence factors, we identified the network that determines the cellular senescence process and senescence phenotype of sensory neurons as well as the core genes of the network.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Extraction of transcriptomic datasets\u003c/h2\u003e \u003cp\u003eTranscriptomic datasets were searched using the following keywords in the Gene Expression Omnibus (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a public repository provided by the National Center for Biotechnology Information.\u003c/p\u003e \u003cp\u003e#1 \u0026ldquo;ganglia, spinal\u0026rdquo;[MeSH Terms] OR dorsal root ganglion[All Fields]\u003c/p\u003e \u003cp\u003e#2 (\u0026ldquo;peripheral nerves\u0026rdquo;[MeSH Terms] OR peripheral nerve[All Fields]) OR (\u0026ldquo;sciatic nerve\u0026rdquo;[MeSH Terms] OR sciatic nerve[All Fields])\u003c/p\u003e \u003cp\u003e#3 (\u0026ldquo;aging\u0026rdquo;[MeSH Terms] OR aging[All Fields]) OR (\u0026ldquo;aging\u0026rdquo;[MeSH Terms] OR senescence[All Fields])\u003c/p\u003e \u003cp\u003e#4 \u0026ldquo;#1\u0026rdquo; OR \u0026ldquo;#2\u0026rdquo;\u003c/p\u003e \u003cp\u003e#5 \u0026ldquo;#3\u0026rdquo; AND \u0026ldquo;#4\u0026rdquo;\u003c/p\u003e \u003cp\u003eTranscriptomic datasets were included in this study based on the following criteria: 1) comparison between young and aged groups and 2) analysis targeting the DRG or SN. For each extracted dataset, the raw count data of each sample were normalized to transcripts per kilobase million (TPM). Differential expression analysis between two groups was conducted using the R/Bioconductor package DESeq2 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In DESeq2, statistical significance was assessed using the Wald test, with a p-value cutoff of 0.05. Common differentially expressed genes (DEGs) detected in each dataset for the DRG and SN were extracted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Evaluation of cellular senescence using transcriptomic datasets\u003c/h2\u003e \u003cp\u003eCellular senescence is induced by increased expression of tumor suppressor genes. The major tumor suppressor signaling pathways include the p16/Rb and p53/p21 signaling pathways, with p16, p53, and p21 being the key cellular senescence factors [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Targeting the \u003cem\u003eCDKN2A\u003c/em\u003e, \u003cem\u003eTrp53\u003c/em\u003e, and \u003cem\u003eCDKN1A\u003c/em\u003e genes, which encode p16, p53, and p21, respectively, the fold change was calculated by comparing the extracted dataset between the young and aged groups. Datasets where the log2 fold change (log2FC) of the target genes was greater than zero were defined as aged datasets and utilized for bioinformatics analysis.\u003c/p\u003e \u003cp\u003e2.3. Weighted gene co-expression network analysis (WGCNA) and characterization of gene modules by protein\u0026ndash;protein interaction (PPI) network\u003c/p\u003e \u003cp\u003eFrom biological and statistical perspectives, genes with very low counts in RNA sequencing (RNA-seq) or low intensity in microarray data were removed prior to downstream analysis. The aged dataset comprised one dataset targeting the DRG (GSE149770) and two datasets targeting the SN (GSE137504 and GSE198669). To conduct the WGCNA, a dataset targeting SN with a larger sample size was utilized to ensure the statistical reliability and stability of the co-expression modules. Thirty-six samples (aged group: 24, young group: 12) from the two SN datasets were merged, and their raw data were normalized to TPM. These samples were filtered using hierarchical clustering as employed in the WGCNA tutorial (36 \u0026times; 35 samples). The analyzed genes were filtered using the top 3000 genes with mean absolute deviation. The answer to \u0026ldquo;Can both logarithms of the order distribution be approximated by a straight line?\u0026rdquo; was used to determine the optimal soft threshold (=\u0026thinsp;28), considering the scale-free nature of the network. A topological overlap matrix (TOM) was formulated based on adjacency values to calculate corresponding dissimilarity (1-TOM) values. Module identification was accomplished using the dynamic tree cut method by hierarchically clustering genes using 1-TOM as the distance measure with a minimum size cutoff of 30 and a deep split value of four for the resulting dendrogram. Modules that are strongly correlated with each other in the eigengene module, which is the first principal component of the principal component analysis of the gene expression matrix of the modules, were merged. A module preservation function was used to verify the stability of the identified modules by calculating module preservation and quality statistics using the WGCNA package [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe evaluated the concordance between the genes in the module constructed using WGCNA and the DEGs obtained from the DRG dataset. The module constructed from the genes upregulated in the aged group in the dataset was defined as the aged module. Likewise, the young module was constructed from the genes in the young group. Among the DEGs common to the DRG and SN datasets, those with elevated expression in the aged group were targeted to construct protein\u0026ndash;protein interaction (PPI) networks using the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In the STRING database, the \u0026ldquo;Protein with value/Rank\u0026rdquo; option was used, with gene symbols as identifiers and Log2FC as values. The genes constituting the networks were considered representative of their modules, and the characteristics of each module with respect to cellular senescence were predicted based on the direction of the interactions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Network and hub gene analysis\u003c/h2\u003e \u003cp\u003eAmong the modules constructed from the WGCNA, a PPI network and weighted gene co-expression network were constructed for the modules related to cellular senescence, and the networks were analyzed using Cytoscape software (version 3.10.1). The topology parameters (i.e., degree centrality and betweenness centrality) of a particular network were determined by the \u0026ldquo;analyze network\u0026rdquo; feature in Cytoscape software. In the constructed network, genes present at the center of the network and functional hubs had high degree and betweenness centralities. Therefore, the product of the degree centrality and betweenness centrality was used to rank the genes in the module, and genes with high ranks in both networks were designated as hub genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Gene set enrichment analysis\u003c/h2\u003e \u003cp\u003eGSEA was performed on all genes detected in each dataset as described using the GSEA web tool provided by Broad Institute Website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/index.jsp\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/index.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The database of gene sets used in GSEA was WikiPathways (wikipathways.org), and the degree of enrichment to annotations for biological processes was quantified as the enrichment score (ES). To facilitate comparison and interpretation between datasets with different tissues (DRG and SN) and sample sizes, the normalized enrichment score (NES) was calculated by normalizing the ES according to the gene set size. The datasets were compared using NES. The minimum and maximum criteria for selecting gene sets from the collection were 10 and 500 genes, respectively.\u003c/p\u003e \u003cp\u003eLeading-edge analysis was performed on annotations with high NES commonly found in the aged group of each dataset after GSEA to determine the core genes that defined the subset of genes with positive NES. These leading-edge genes were used as seed genes for network propagation using random walk with restart (RWR).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Network propagation using random walk with restart algorithm\u003c/h2\u003e \u003cp\u003eRWR was performed using the R/Bioconductor package RandomWalkRestartMH 1.22.0 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. RWR simulated a walker that started from one node or a set of nodes (seed nodes) in a network and moved randomly to deliver probabilities on the seed node to other nodes. In this study, walkers were simulated in an aged module with the seed gene as the starting node. For each node (gene in the aged module), a gene expression value was assigned as the initial score and a rank score was calculated as the establishment distribution. The higher the rank score, the more important each node (gene) within the module was.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Prediction of the abundance of infiltrated immune cells using CIBERSORT analysis\u003c/h2\u003e \u003cp\u003eWe used the analytical tool CIBERSORTx (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cibersortx.stanford.edu/\u003c/span\u003e\u003cspan address=\"https://cibersortx.stanford.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) provided by Alizadeh Lab and Newman Lab to estimate the abundance of each immune cell type in the aged and young groups using the CIBERSORT algorithm [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. We used LM22, which has gene expression patterns for 22 immune cell types provided by the developer, as a reference set. The expression values of the DEGs for each sample were used as expression data for estimation. The analytical parameter permutation for significance analysis was set to 1000, as recommended by CIBERSORTx.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Statistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using SPSS version 25 for Windows (Japan IBM, Japan). Data were presented with means and 95% confidence intervals (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;95%CI). Two-tailed Student\u0026rsquo;s t-test and two-tailed Wilcoxon rank-sum test were performed to compare the number of infiltrated immune cells in the young and aged group. Complete-case analysis was performed for missing data, such as genes that were not adequately detected by RNA-seq.\u0026nbsp;For all statistical tests, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Cellular senescence of peripheral sensory neurons is induced by CDKN2A\u003c/h2\u003e \u003cp\u003eWe obtained comprehensive gene expression datasets by comparing the young and aged groups for both the DRG and SN. There were four datasets, with two datasets each for the DRG (GSE63651, 149770) and SN (GSE137504, 198669) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Three and one dataset contained tissue samples from C57BL/6J mice and F344 rats, respectively.\u003c/p\u003e \u003cp\u003eThe log2FC for the cellular senescence markers in the extracted dataset were CDKN2A (DRG: 0.50, SN: 1.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05), Trp53 (DRG: 0.42, SN: 0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36), and CDKN1A (DRG: 0.26, SN: 0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Considering these results, we generated an aged dataset based on CDKN2A (GSE149770, 137504, and 198669) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec) which contained 1188 and 240 DEGs in the DRG (aged-up: 518, aged-down: 670) and SN (aged-up: 108, aged-down: 132), respectively. Among these, 45 DEGs were common to both DRG and SN. Among these 45 genes, 15 were upregulated in the aged group, including CDKN2A (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Cathepsin S is a hub gene that characterizes the phenotype of aged sensory neurons\u003c/h2\u003e \u003cp\u003eWGCNA was performed on 35 samples (aged group: 23, young group: 12) from two datasets targeting the SN (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The WGCNA module was divided into six modules. Modules 2 to 5 were aged modules. Modules 1 and 6, which had the highest and lowest number of dependent genes, respectively, were young modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Module 4 had the highest percentage of genes common to the DEGs extracted from the DRG dataset. Among the DEGs common to the DRG and SN datasets, a PPI network was constructed for 15 genes that were upregulated in the aged group. The constructed PPI network identified module 1 as the module that induces the cellular senescence factor CDKN2A, which causes cellular senescence, and module 4 as the module that receives action from CDKN2A and thus, the network expresses the characteristics of aged sensory neurons with cellular senescence phenotypically (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eA PPI network was constructed for module 4, and genes were ranked by \u0026ldquo;degree.\u0026rdquo; Cathepsin S (Ctss), a cysteine protease involved in protein differentiation and processing, was identified as a candidate hub gene (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). Furthermore, Ctss was inferred to be a hub gene in the weighted co-expression network constructed from 26 genes that were common to the DEGs in the DRG dataset of module 4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Cathepsin S induces activity of antigen-presenting cells represented by microglia\u003c/h2\u003e \u003cp\u003eGSEA was performed using the DRG dataset to investigate the top 10 WikiPathways annotations enriched in the aged group in the order of increasing NES. An NES of 2.0 or higher and a smaller false discovery rate (FDR) generally indicate a significant and strong enrichment. In this analysis, \u0026ldquo;Microglia pathogen phagocytosis pathway\u0026rdquo; and \u0026ldquo;Tyrobp causal network in microglia\u0026rdquo; showed high NES and minimal FDR (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). We identified five leading-edge genes common to these two annotations related to microglial activity, namely \u003cem\u003eNcf2\u003c/em\u003e, \u003cem\u003eItgam\u003c/em\u003e, \u003cem\u003eNckap1l\u003c/em\u003e, \u003cem\u003eItgb2\u003c/em\u003e, and \u003cem\u003eTyrobp\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Furthermore, these two annotations showed significant and higher enrichment in the aged group, not only in the DRG but also in the SN (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eThe expression of Ctss, which was identified as a hub gene in association with cellular senescence in module 4, increased with increasing CDKN2A expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). The five genes identified as leading-edge genes that characterize aged sensory nerves were incorporated into module 4 as seed genes, and network propagation was performed starting from the seed genes. We found that many genes in module 4 were propagated to other genes when they started from the seed genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). Ctss had a high rank score (rank score: 0.06).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e3.4. Decreased cholesterol metabolism in the axon of aged sensory nerves activated the transcription factor Srebf1 in the cell body, thereby causing cellular senescence\u003c/p\u003e \u003cp\u003ePPI network and weighted gene co-expression networks were constructed and analyzed for module 1, the cellular senescence-induction module that includes the transcription factor Srebf1, which induces CDKN2A. Specifically, 104 genes in module 1 common to DEGs in the DRG dataset were targeted. \u003cem\u003eMapk3\u003c/em\u003e was identified as a hub gene from the PPI network analysis and was also highly ranked in the weighted gene co-expression network (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). It was upregulated in the DRG but downregulated in the SN during senescence. In contrast, \u003cem\u003eSrebf1\u003c/em\u003e was consistently upregulated during senescence in the DRG and SN (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSrebf1 is an important transcription factor involved in lipid and cholesterol metabolism. Therefore, we investigated the WikiPathways annotations related to lipid and cholesterol metabolism. Two annotations related to cholesterol metabolism, namely \u0026ldquo;Cholesterol metabolism with Bloch and Kandutsch Russell pathway\u0026rdquo; and \u0026ldquo;Cholesterol biosynthesis,\u0026rdquo; were commonly included in the top 10 annotations that were significantly enriched in the SN of the young group. Therefore, these annotations were significantly less enriched in the SN of the aged group (NES: -1.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93, FDR: 0.009\u0026thinsp;\u0026plusmn;\u0026thinsp;0.012 vs. NES: -2.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53, FDR: 0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). However, these annotations were not significantly enriched in the young group. We identified \u003cem\u003eFdps\u003c/em\u003e and \u003cem\u003ePmvk\u003c/em\u003e as the leading-edge genes associated with these two annotations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Aged sensory nerves are infiltrated by B and CD8\u003csup\u003e+\u003c/sup\u003e T cells through the axon\u003c/h2\u003e \u003cp\u003eConsidering that aged sensory neurons are characterized by the activation of antigen-presenting cells, including microglia, these neurons may be infiltrated by antigen-presenting cells, such as B cells, macrophages, and dendritic cells, as well as by T cells, which interact with the presented antigens. Therefore, we used CIBERSORT analysis to estimate the percentage of immune cells in the DRG and SN of sensory nerves. The results showed vastly different percentages of immune cell infiltration in the DRG and SN in the young and aged groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). In particular, among the antigen-presenting cells, macrophages and dendritic cells exhibited decreased infiltration rate, while B cells exhibited increased infiltration rate with aging.\u003c/p\u003e \u003cp\u003eThe seven T cell phenotypes inferred were CD8\u003csup\u003e+\u003c/sup\u003e T cells, na\u0026iuml;ve CD4\u003csup\u003e+\u003c/sup\u003e T cells, memory resting CD4\u003csup\u003e+\u003c/sup\u003e T cells, memory activated CD4\u003csup\u003e+\u003c/sup\u003e T cells, follicular helper T cells, regulatory T cells (Treg), and gamma delta T cells (Tγδ) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). T cells were mainly infiltrated in the SN and were less infiltrated in the DRG. In the SN, the proportion of CD8\u003csup\u003e+\u003c/sup\u003e T cells and memory resting CD4\u003csup\u003e+\u003c/sup\u003e T cells infiltrated was significantly increased in the aged group compared to the young group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we identified the molecular mechanisms that induce cellular senescence in DRG neurons and the inflammation-induced phenotype of aged DRG neurons that have undergone cellular senescence (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe found that cellular senescence in DRG neurons was induced by increased expression of CDKN2A, which encodes the cellular senescence-inducing factor p16, which in turn was caused by increased expression of \u003cem\u003eSrebf1\u003c/em\u003e, a transcription factor involved in lipid and cholesterol metabolism. Although \u003cem\u003eSrebf1\u003c/em\u003e expression increases with aging, the senescence-induced gene network to which \u003cem\u003eSrebf1\u003c/em\u003e belongs primarily includes genes whose expression decreases with aging. This gene network includes \u003cem\u003eFdps\u003c/em\u003e and \u003cem\u003ePmvk\u003c/em\u003e, the leading-edge genes of the pathways involved in cholesterol metabolism and biosynthesis, which are significantly suppressed by aging. Furthermore, \u003cem\u003eMapk3\u003c/em\u003e, a hub gene that plays a central role in this network, was suppressed by aging, suggesting that cholesterol metabolism and biosynthesis are regulated by \u003cem\u003eMapk3\u003c/em\u003e. The conflicting results between \u003cem\u003eSrebf1\u003c/em\u003e and \u003cem\u003eMapk3\u003c/em\u003e expression may be attributed to the possibility that \u003cem\u003eSrebf1\u003c/em\u003e expression may have increased to compensate for the \u003cem\u003eMapk3\u003c/em\u003e-mediated decline in cholesterol metabolism and biosynthetic functions associated with aging. \u003cem\u003eMapk3\u003c/em\u003e is a target gene for cancer therapy, and its increased expression activates cell proliferation and differentiation, whereas its suppression induces the expression of the cellular senescence marker p16 and accumulation of aged cells [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. \u003cem\u003eSrebf1\u003c/em\u003e has been identified as a marker of cellular senescence in patients with bladder [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and esophageal cancers [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Although decreased \u003cem\u003eMapk3\u003c/em\u003e expression and increased \u003cem\u003eSrebf1\u003c/em\u003e expression are involved in cellular senescence, the pathways involved are unclear. However, in this study, two independent changes in gene expression thought to induce cellular senescence can be viewed as a series of molecular mechanisms associated with aging. Interestingly, reduced cholesterol metabolism and biosynthetic function, which is key to the molecular response that induces cellular senescence in DRG neurons, occurs only in the axons of DRG neurons and not the cell body. In DRG neurons, axons are subject to various stresses, which may induce cellular senescence of DRG neurons. In summary, the p16-mediated cellular senescence process in DRG neurons may be explained by the compensatory upregulation of \u003cem\u003eSrebf1\u003c/em\u003e for cholesterol metabolism and biosynthetic dysfunction via \u003cem\u003eMapk3\u003c/em\u003e downregulation in axons.\u003c/p\u003e \u003cp\u003eWe also identified a senescence phenotype that characterizes aged DRG neurons. In this phenotype, \u003cem\u003eCtss\u003c/em\u003e functions as a hub gene and promotes the immune response by increasing the expression of antigen-presenting cell activators (Ncf2, Itgam, nckap1l, Itgb2, and Tyrobp). The molecular response that results in chronic inflammation in aged DRG neurons also occurs specifically in the axons. This is because T cells that recognizes the antigens presented by activated antigen-presenting cells infiltrate the SN but rarely infiltrate the DRG. Zhou et al. showed that CD8\u003csup\u003e+\u003c/sup\u003e T-cell infiltration of sensory nerve axons occurs when aging and releases inflammatory cytokines, leading to age-related axonal degeneration and prolonged injury healing. Our study also identified CD8\u003csup\u003e+\u003c/sup\u003e T cells as an infiltrating T-cell type that significantly increases with age in SNs [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In addition, \u003cem\u003eCtss\u003c/em\u003e, the hub gene identified in this study, is a plasma protein marker that predicts migratory deficits in elderly individuals, suggesting a link with aging [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In summary, aged DRG neurons may contribute to chronic inflammation in sensory nerve axons by activating antigen-presenting cells through \u003cem\u003eCtss\u003c/em\u003e upregulation, which in turn causes activation of CD8\u003csup\u003e+\u003c/sup\u003e T cells. Chronic inflammation of nerve tissue accelerates tissue aging [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Our findings may provide the basis for research on the molecular mechanism underlying the \u0026ldquo;Inflammaging\u0026rdquo; of peripheral sensory nerves.\u003c/p\u003e \u003cp\u003eA limitation of this study is that the inferred molecular mechanisms underlying the cellular senescence process and senescence phenotype have not been validated through in vitro and in vivo experiments. In particular, it remains to be verified whether the upregulation of \u003cem\u003eSrebf1\u003c/em\u003e in the cell body during the cellular senescence process is a compensatory response to reduced cholesterol metabolism in axons, specifically due to the inactivation of the mevalonate pathway. However, there is some evidence that a reduction in the mevalonate pathway causes the cellular aging process. This is evidenced by the use of statins, which inhibit the mevalonate pathway, leading to age-like declines in peripheral nerve function [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and the involvement of cell membrane fluidity, which is regulated by the mevalonate pathway, in the cellular senescence process [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn peripheral sensory neurons, the suppression of \u003cem\u003eMapk3\u003c/em\u003e with aging leads to impaired axonal cholesterol metabolism and biosynthesis, resulting in transcription factor \u003cem\u003eSrebf1\u003c/em\u003e-mediated CDKN2A activation and cellular senescence. Furthermore, aged peripheral sensory neurons exhibit a senescence phenotype that activates antigen-presenting cells via \u003cem\u003eCtss\u003c/em\u003e. These molecular mechanisms have been rigorously inferred from network analyses using transcriptomic data, and their validation by in vivo and in vitro experiments in future research will provide a foundation for understanding the aging of peripheral sensory neurons.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003e95%CI,\u0026nbsp;95% confidence interval;DEG, differentially expressed gene;DRG, dorsal root ganglion; ES, enrichment score; FDR, false discovery rate; GSEA, gene set enrichment analysis; log2FC, log2 fold change; NES, normalized enrichment score; PPI, protein\u0026ndash;protein interaction; RNA-seq, RNA sequencing; RWR, random walk with restart; SN, sciatic nerve; TOM, topological overlap matrix; 1-TOM, dissimilarity in TOM; TPM, transcripts per kilobase million; WGCNA, weighted gene co-expression network analysis\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is an in silico studies with publicly available transcriptome datasets. The Saitama Prefectural University Experimental Animal Ethics Committee has confirmed that no ethical approval is required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the current study are available in the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/), provided by National Center for Biotechnology Information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project has received funding from the\u0026nbsp;Grant-in-Aid for JSPS Fellows JP23KJ1789 and Saitama Prefectural University Research\u0026nbsp;(SPUR) Grant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation and data collection and analysis were performed by Sora Kawabata. Hirotaka Iijima provided advice on data analysis, and Kenji Murata oversaw the entire study. The first draft of the manuscript was written by Sora Kawabata and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Grant-in-Aid for JSPS Fellows. We would like to thank Editage (\u003ca href=\"https://ind01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.editage.jp%2F\u0026data=05|01|[email protected]|d19236e0dfe043ead77308db5a614048|762d8873d7774e7fbb6be4d2cccca312|0|0|638203145963925545|Unknown|TWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D|3000|||\u0026sdata=3Ft%2BFi9diYBeORMgJke%2Bb2yLcVUtqgNyMTRE2M073GY%3D\u0026reserved=0\"\u003ewww.editage.jp\u003c/a\u003e) for English language editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBouche P (2020) Neuropathy of the elderly. 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Nat Commun 14:288. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-023-35952-z\u003c/span\u003e\u003cspan address=\"10.1038/s41467-023-35952-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-neurobiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"moln","sideBox":"Learn more about [Molecular Neurobiology](https://www.springer.com/journal/12035)","snPcode":"12035","submissionUrl":"https://submission.nature.com/new-submission/12035/3","title":"Molecular Neurobiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"dorsal root ganglion, peripheral nerve, cellular senescence, aging, transcriptome, bioinformatics","lastPublishedDoi":"10.21203/rs.3.rs-4431608/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4431608/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccumulation of senescent neurons in the dorsal root ganglion (DRG) is an important tissue phenotype that causes age-related degeneration of peripheral sensory nerves. Senescent neurons are neurons with arrested cell cycle that have undergone cellular senescence but remain in the tissue and play various biological roles. To understand the accumulation of senescent neurons in the DRG during aging, we aimed to elucidate the mechanism that induces cellular senescence in DRG neurons and the role of senescent DRG neurons. We integrated multiple public transcriptome datasets for DRGs, which represent cell bodies in neurons, and sciatic nerve, which represents axon in neurons, using network medicine-based bioinformatics analysis to account for axon-cell body interaction involved in cellular senescenc. Network medicine-based bioinformatics analysis revealed that age-related \u003cem\u003eMapk3\u003c/em\u003e decline leads to impaired cholesterol metabolism and biosynthetic function in axons, resulting in compensatory upregulation of \u003cem\u003eSrebf1\u003c/em\u003e, a transcription factor involved in lipid and cholesterol metabolism, which in turn leads to CDKN2A-mediated cellular senescence. Furthermore, this analysis revealed that senescent DRG neurons develop a senescence phenotype characterized by activation of antigen-presenting cells via upregulation of \u003cem\u003eCtss\u003c/em\u003e as a hub gene. B cells inferred as antigen-presenting cells activated by \u003cem\u003eCtss\u003c/em\u003e, and CD8-positive T cells inferred as cells that receive antigen presentation from the B cells.\u003c/p\u003e","manuscriptTitle":"Network-based global analysis of the cellular senescence process and senescence phenotype in the peripheral sensory neurons of the dorsal root ganglia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-11 06:54:00","doi":"10.21203/rs.3.rs-4431608/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-09T13:08:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-09T11:55:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"297641204643754190454395869041989535381","date":"2024-09-29T16:31:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"237173847064223489126224555970807184597","date":"2024-08-28T17:19:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-07T18:22:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"142275086528404288797878716613102842441","date":"2024-07-29T15:02:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-13T17:18:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-29T13:38:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-29T13:38:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Neurobiology","date":"2024-05-16T14:03:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-neurobiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"moln","sideBox":"Learn more about [Molecular Neurobiology](https://www.springer.com/journal/12035)","snPcode":"12035","submissionUrl":"https://submission.nature.com/new-submission/12035/3","title":"Molecular Neurobiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"18846800-19fa-497f-9251-447fd6853468","owner":[],"postedDate":"June 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-30T15:58:11+00:00","versionOfRecord":{"articleIdentity":"rs-4431608","link":"https://doi.org/10.1007/s12035-024-04666-8","journal":{"identity":"molecular-neurobiology","isVorOnly":false,"title":"Molecular Neurobiology"},"publishedOn":"2024-12-23 15:56:53","publishedOnDateReadable":"December 23rd, 2024"},"versionCreatedAt":"2024-06-11 06:54:00","video":"","vorDoi":"10.1007/s12035-024-04666-8","vorDoiUrl":"https://doi.org/10.1007/s12035-024-04666-8","workflowStages":[]},"version":"v1","identity":"rs-4431608","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4431608","identity":"rs-4431608","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}