Identification of Ferroptosis-related Regulatory Network and Validation of the Expression of miRNA-326–IL-1β in Spinal Cord Injury

doi:10.21203/rs.3.rs-3867139/v1

Download PDF

Research Article

Identification of Ferroptosis-related Regulatory Network and Validation of the Expression of miRNA-326–IL-1β in Spinal Cord Injury

https://doi.org/10.21203/rs.3.rs-3867139/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background

Spinal cord injury (SCI) is a severe central nervous system injury. Emerging research suggests a connection between SCI and ferroptosis. However, its underlying mechanism remains incompletely understood. This study aims to identify key genes associated with ferroptosis after SCI and explore their potential molecular mechanisms.

Methods

Ferroptosis-related genes (FRGs) were identified by intersecting GSE151371 and the FerrDb database. Enrichment analysis was performed with Gene Ontology (GO) / KEGG. And the top five hub FRGs were from protein-protein interaction network analysis. Subsequently, the competing endogenous RNA (ceRNA) network was constructed based on ENCORI dataset. Furthermore, rat SCI Model was constructed and Basso-Beattie-Bresnahan Locomotor Scale Assessment was used to evaluate hind limb motor function in sham group and SCI group. Ferroptosis marker genes Gpx4, Acsl4 and predicted genes miR-326 – IL-1β were validated through RT-qPCR.

Results

We screened out 38 FRGs. GO and KEGG analyses revealed that lipid response was significantly associated with ferroptosis after SCI, while IL-17 signaling pathway was predominantly involved in the regulation of ferroptosis. Moreover, we identified five hub FRGs - PPARG, IL-1β, PTGS2, IFNG, and MAPK3 - which played crucial roles in the ceRNA network. Furthermore, the RNA expression level of Acsl4 was upregulated in the SCI group than in the sham group, while the Gpx4 was reversed. Similarly, in comparison to the sham group, the expression level of IL-1β was increased in the SCI group, while miR-326 exhibited a decrease expression.

Conclusions

miR-326–IL-1β may play pivotal roles in the molecular mechanisms underlying ferroptosis after SCI. Further experimental validation is warranted.

spinal cord injury

ferroptosis

miRNA-326

IL-1β

bioinformatics analysis

Spinal cord injury (SCI) is a severe central nervous system injury that leads to a high disability rate and high mortality rate [1]. The annual global incidence of SCI ranges from 250,000 to 500,000 new cases [2]. Acute spinal cord injury (ASCI) patients have a death rate ranging from 4.4–16.7% [3, 4]. Since the primary injury cannot be reversed, effective intervention for secondary injury after SCI, including neuronal necrosis and apoptosis, ischemia–reperfusion injury, demyelination and degeneration of spinal cord axons [5], is considered to be the key to treatment [6, 7]. However, the mechanisms of secondary injury are complex and poorly understood, hence further exploration of these mechanisms after SCI is essential [4, 8, 9].

Ferroptosis, an emerging form of regulated cell death distinct from traditional apoptosis and necrosis, involves excessive accumulation of iron-dependent lipid peroxides leading to fatal cellular damage [10, 11]. Increasing evidence suggests that ferroptosis plays a role in the pathological process of secondary injuries following SCI. For instance, the rupture of red blood cells leads to a large amount of free Fe³⁺ leakage [12], and activated microglia can cause intracellular iron overload, leading to ferroptosis [13]. Therefore, targeting the inhibition of ferroptosis after SCI is considered to be an effective treatment strategy [14–16], with screening and comprehensive analysis of ferroptosis-related genes (FRGs) from the SCI dataset in the Gene Expression Omnibus (GEO) one of the leading strategies to explore the mechanism of secondary injury and find novel therapeutic targets.

In this study, we identified the FRGs using the GSE151371 sequencing data from the GEO database. Subsequently, we performed Gene Ontology (GO) functional annotation analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Additionally, a protein-protein interaction (PPI) network was constructed, and based on their degree of connectivity, PPARG, IL-1β, PTGS2, IFNG, and MAPK3 were selected as the top five hub FRGs. We predicted miRNAs for these hub FRGs via the intersection of mirDIP and GSE151371. Then we constructed a competing endogenous RNA (ceRNA) network based on ENCORI dataset and a TF-miRNA-mRNA network using TransmiR v2.0 database. Moreover, we also predicted 102 potential drugs that could target the hub FRGs using DGIdb. Finally, we confirmed the expression levels of Gpx4, Acsl4 and miRNA-326–IL-1β via in vivo experiments with Real-time quantitative polymerase chain reaction (RT-qPCR) testing. Our findings may provide valuable insights into understanding the mechanisms underlying ferroptosis after SCI and offer novel diagnostic and therapeutic targets for SCI.

Data Collection

The normalized expression matrix of the GSE151371 dataset[17] based on the GPL20301 platform was retrieved from the GEO database (http://www.ncbi.nlm.nih.gov/gds/). The miRNA and mRNA expression profiles were obtained from this dataset, which include 10 samples of peripheral blood extracted from healthy control (HC) individuals, 10 samples from trauma control (TC) patients with non-central nervous system injuries, and 38 samples from traumatic spinal cord injury (TSCI) patients. The workflow of this study is shown in Fig. 1.

Identification of DEmiRNAs, DEmRNAs and FRGs

All genes were annotated using the Xiantao Academic website (http://www.xiantao.love/). Then, differentially expressed miRNAs (DEmiRNAs) and mRNAs (DEmRNAs) after SCI were screened out by pairwise comparisons among the three groups (HC, TC, TSCI). The Sangerbox 3.0 website (http://sangerbox.com/) [18] was used to complete the task, with the thresholds of |log2(FC)| >1.0 and adjusted P-value < 0.05. Three datasets of genes related to ferroptosis were downloaded from the FerrDb database (http://www.zhounan.org/ferrdb/) [19], which led us to obtain 264 drivers, 238 suppressors, and 11 markers. A total of 484 genes were screened after removing duplicate genes. The FRGs after SCI were screened out by studying the intersection of the DEmRNAs and the 484 genes. The FRGs and DEmiRNAs in SCI were visualized with volcano and Venn diagrams, produced using the Sangerbox 3.0 online website.

GO and KEGG Pathway Enrichment Analysis of FRGs

GO and KEGG pathway enrichment analysis, performed via the Sangerbox 3.0 website, was used to explore the roles of all DEmRNAs, with the thresholds set as P < 0.05 and false discovery rate(FDR) < 0.1. The analysis results were downloaded and visualized via the Bioinformatics enrichment dot bubble (www.bioinformatics.com.cn).

Construction of PPI Network and Identification of Hub FRGs after SCI

The PPI network of the DEmRNAs, which was used to analyze protein interactions, was established via the STRING (v11.5) database (http://string-db.org/). The minimum required interaction score was set as 0.400, and the result was downloaded from the STRING database with nodes hidden if they were disconnected from the network. Then, the result was visualized using Cytoscape 3.10.0 software. Subsequently, the CytoHubba(v0.1)plug-in of Cytoscape was used to determine the top five genes with the highest connectivity, which were selected as the hub genes.

Prediction of Target Relationship Between DEmiRNAs and FRGs

The target miRNAs for the five hub genes were predicted through the online databases mirDIP (http://ophid.utoronto.ca/mirDIP/) [20, 21]. Furthermore, all DEmiRNAs were identified using the miRDB database (https://mirdb.org/)[22, 23] to obtain mature miRNAs. Subsequently, all the predicted miRNAs’ intersections with the mature miRNAs were studied. The miRNA–mRNA target relationship was established according to the opposite expression level. The interactive network was visualized using Cytoscape 3.10.0 software.

Construction of the ceRNA Network

Based on the competitive endogenous RNA (ceRNA) hypothesis, potentially interacting lncRNAs were predicted through the ENCORI dataset (http://starbase.sysu.edu.cn/index.php.)[24] with the miRNAs included in miRNA-mRNA relationship pairs. Finally, the lncRNA-miRNA-mRNA network consisting of hub FRGs was constructed. The task of interactive network visualization was performed by Cytoscape 3.10.0 software.

Establishment of the TF–miRNA–mRNA Network

Based on miRNAs included in miRNA-mRNA relationship pairs, potentially interacting Transcription factors(TFs) were predicted through the TransmiR v2.0 database (http://www.cuilab.cn/transmir)[25, 26]. Finally, a TF-miRNA-mRNA network composed of hub FRGs was established and the network was visualized using Cytoscape 3.10.0 software.

Prediction of hub FRG-targeted drugs

Using DGIdb(https://dgidb.org/), potential drugs that could target the five key FRGs included PPARG, PTGS2, IL-1β, MAPK3 and IFNG were predicted when using default interaction parameters. Cytoscape 3.10.0 was used to visualize the resulting data.

Rat SCI Model

Adult female Sprague–Dawley rats (200 ~ 250 g) were provided by the Animal Center of Guangxi Medical University (Nanning, Guangxi, China) and were randomly divided into sham and SCI groups. All rats were bred under controlled environmental conditions. The care and use of all animals were in accordance with the guidelines established by the National Institutes of Health of China. The experiment was approved by the Ethics Committee of Guangxi Medical University. After being weighed and marked, all rats were anesthetized by intraperitoneal injection with 1% (w/v) pentobarbital sodium (65 mg/kg) [27] and then underwent a laminectomy to expose the T10 spinal cord. The model of TSCI was established via the modified version of Allen’s method [28]. At the end of the surgery, the incision was sutured layer by layer. Rats in the SCI group were manually urinated twice a day until the bladder function recovered.

Basso, Beattie, and Bresnahan Locomotor Scale Assessment

The motor function of rat hind limbs was evaluated at different time points (0, 1, 3, and 7 days) after the operation using the Basso, Beattie, and Bresnahan Locomotor Scale (BBB) score [29]. BBB scores range from 0 to 21, where a higher score indicates better motor function. Three uninformed researchers performed the task of observing and evaluating hind limb motor function in rats in open areas.

Real-time quantitative polymerase chain reaction (RT-qPCR)

Total RNA was extracted from the rat spinal cord using NucleoZOL (Machery-Nagel, Germany), and RNA purity was detected by a spectrophotometer at 260/280 and 260/230 nm. cDNA was prepared using a reverse transcription kit (TaKaRa, Dalian, China) with the total RNA as the template, and then a TB Green® Premix Ex Taq™ II Kit (TaKaRa, Dalian, China) and ABI 7500 System were used for PCR amplification experiments. The thermal cycles were set as follows: 95°C for 30 s, 40 cycles of 95°C for 20 s, and 60°C for 34s. Each quantitative PCR reaction was performed in triplicate wells. The results for miRNA and mRNA were normalized based on the expression of U6 and Gapdh, respectively. The primers used in this study are listed in Table 1. The relative expression levels were calculated via the 2^−ΔΔCT method.

Table 1

Primer sequences used for RT-qPCR.
Gene	Sequence
miR-326-3p	F: 5'-TCTGGGCCCTTCCTCCAGT-3'
miR-326-3p	R: universal reverse primer
Il-1β	F: 5'-TCACAGCAGCATCTCGACAA-3'
Il-1β	R: 5'-ACGGGCAAGACATAGGTAGC-3'
Gpx4	F: AGGCAGGAGCCAGGAAGTAA
Gpx4	R: TCCATTTCCACAGTGGGTGG
Acsl4	F: TGGGCTGACAGAATCATGCG
Acsl4	R: GCAAATAAGAGGAGCGCCAA
U6	F: 5'-GGAACGATACAGAGAAGATTAGC-3'
U6	R: 5'-TGGAACGCTTCACGAATTTGCG-3'
Gapdh	F: 5'-GGCACAGTCAAGGCTGAGAATG-3'
Gapdh	R: 5'-ATGGTGGTGAAGACGCCAGTA-3'

Statistical Analysis

The data were analyzed using Statistical Product and Service Solutions (SPSS) software version 20.0. The results of RT-qPCR are presented as means ± standard deviation (SD). Comparison between the two groups was performed via an independent t-test, and P < 0.05 was considered statistically significant.

Identification of DEmRNAs, DEmiRNAs, and FRGs

The gene expression profile GSE151371 was used to identify DEmRNAs and DEmiRNAs among the HC, TC, and SCI groups, with the predetermined threshold set as |log2FC| >1.0 and adjusted P < 0.05. The results are shown in volcano plots (Fig. 2a–c). A total of 1645 (1219 + 270 + 156) mRNAs and 47 (24 + 14 + 9) miRNAs, which were significantly differentially expressed only in the SCI patients, were screened out for further analysis (Fig. 2d–e). Next, we identified 38 FRGs in SCI by studying the 1645 DEmRNAs’ intersections with 484 ferroptosis-related genes (Fig. 2f). The list of those 38 FRGs in SCI is shown in Supplementary Table 1.

GO and KEGG Pathway Enrichment Analysis of FRGs

We performed GO and KEGG pathway enrichment analysis for 38 FRGs. The most important biological process (BP) terms were “response to lipid,” “unsaturated fatty acid metabolic process,” “inflammatory response,” and “cell death” (Fig. 3a). The top cellular component (CC) terms included “secretory granule lumen,” “cytoplasmic vesicle lumen,” and “vesicle lumen” (Fig. 3b). The meaningful terms for molecular functions (MF) were “oxidoreductase activity,” “dioxygenase activity,” and “iron ion binding” (Fig. 3c). KEGG pathway analysis revealed that FRGs were significantly enriched for the IL-17, HIF-1, TNF, and NOD-like receptor signaling pathways (Fig. 3d).

Construction of PPI Network and Identification of Hub FRGs after SCI

To explore the interactions among proteins, a PPI network was constructed for the 38 ferroptosis-related DEmRNAs (Fig. 4a). With the highest degrees of connectivity, PPARG, IL-1β, PTGS2, IFNG, and MAPK3 were selected as the hub genes (Fig. 4b). The expression levels of these five hub genes are shown in Fig. 4c–g.

Prediction of Target Relationships Between DEmiRNAs and Hub FRGs

Using the top five hub FRGs, 2180 target miRNAs were predicted based on on the mirDIP(Fig. 5a). Meanwhile, using the 47 DEmiRNAs, 58 mature miRNAs were identified based on the miRDB database (Supplementary Table 2). All the target miRNAs’ intersections with the 58 mature miRNAs were studied, and obtained 52 miRNAs (Fig. 5b). Then, a miRNA–mRNA interactive network was established (Fig. 5c-d). The expression levels of the six miRNAs that were down-regulated are shown in Fig. 5e. According to their converse expression levels, the target relationship of miRNA-326–IL-1β was used for the next validation step.

Construction of the lncRNA–miRNA–mRNA ceRNA Network

A lncRNA-miRNA-mRNA ceRNA network was constructed with the aim of exploring the function of lncRNAs as miRNA sponges in SCI. The network contains 211 lncRNA nodes, 11 miRNA nodes, 3 Hub FRGs(PTGS2,IL-1β and IFNG) nodes and 346 edges (Fig. 6).

Establishment of the TF–miRNA–mRNA Network

According to the miRNAs contained in miRNA-mRNA relationship pairs, a TF-miRNA-mRNA network consisted of hub FRGs was established based on the TransmiR v2.0 database. The network contains 135 TF nodes, 37 miRNA nodes, 5 Hub FRG nodes and 1392 edges (Fig. 7).

Prediction of hub FRG-targeted drugs

Drugs that may target FRGs and their associations were predicted through the DGIdb database (https://dgidb.org/) using default interaction parameters (Fig. 8a). And then potential drugs that may target five key FRGs including PPARG, PTGS2, IL-1β, MAPK3 and IFNG were collated. (Fig. 8b).

Validation of Ferroptosis-related Genes Expression in SCI Rat Model

Compared with the sham group, we found that the BBB score of the SCI group was significantly lower at all time points (Fig. 9a). The RT-qPCR results showed that the expression of ferroptosis marker gene Gpx4 was down-regulated, while Acsl4 was up-regulated (Fig. 9b-c). Compared with the sham group, the expression level of IL-1β was increased in the SCI group, while miR-326 exhibited a decrease expression, and the results were consistent with the results of bioinformatics analysis (Fig. 9d–e).

In the present study, 47 DEmiRNAs and 38 FRGs were identified in SCI. These FRGs were found to be related to the response to lipids, inflammation, and some signal transduction pathways. Subsequently, the top five hub FRGs—PPARG, IL-1β, PTGS2, IFNG, and MAPK3—were screened out. Target relationships between DEmiRNAs and the five hub FRGs were predicted. And then ceRNA network and TF-miRNA-mRNA network were constructed through ENCORI dataset and TransmiR v2.0 database, respectively. Moreover, potential drugs that could target the top five hub FRGs were predicted using DGIdb. Finally,combined with the prediction results, miR-326 and its target gene IL-1β were selected and validated in rat SCI model via RT-qPCR testing. Our verification demonstrated that the expression levels of miR-326 and IL-1β were consistent with the results of our bioinformatics analysis. Our findings may contribute to further studies exploring the mechanisms of ferroptosis after SCI, as well as provide novel diagnostic and therapeutic targets for SCI.

Ferroptosis is a form of nonapoptotic cell death, which was first reported by Dixon et al. in 2012 [10]. Subsequent studies have reported that this mode of cell death occurs in SCI [13, 16, 30]. The latest view of the mechanism of ferroptosis in SCI proposed that the accumulation of iron occurs as the first step [12]. Excessive iron in cells will induce mitochondrial damage and reactive oxygen species (ROS) production via the Fenton reaction [31, 32]. In addition, ferrous iron has high reactivity and increases the cytotoxicity of ROS. The primary role of ROS is to cause oxidative damage to biofilms by targeting polyunsaturated fatty acids [12]. Large amounts of ROS will trigger lipid peroxidation that damages the plasma membrane and leads to ferroptosis [33]. Moreover, the critical role of the inflammatory response in a secondary injury caused by SCI has been widely considered and demonstrated [34, 35]. Interestingly, ferroptosis has been linked to inflammation and immunity [36]. It was reported that inhibiting ferroptosis helps reduce the toxicity of inflammation and enhances neuronal survival after SCI [37]. The results of GO enrichment analysis in the present study were consistent with the current understanding of the biological processes of ferroptosis. However, there have been few reports of the pathways associated with ferroptosis in SCI. A recent study showed that the HIF-1 signaling pathway might play a central role in ferroptosis after SCI [38]. In our study, along with the HIF-1 signaling pathway, we also found other important pathways to be enriched, including the IL-7, TNF, and NOD-like receptor signaling pathways. This suggests that these pathways may be involved in ferroptosis after SCI, though the reports to date are rare. Further studies are needed to improve our understanding of the roles of these pathways in the pathophysiological process of SCI.

Among the five hub genes, IL-1β is an important inflammatory cytokine, and it is also a critical driver of ferroptosis [19]. It has been reported that IL-1β was significantly up-regulated [39] and had a deleterious effect on the plasticity of CNS axons after SCI [40]. Another study demonstrated that IL-1β inhibits the recovery of SCI by inducing NF-κB and up-regulating microRNA-372 expression [41]. On the contrary, down-regulating IL-1β will reduce secondary injury after SCI, protect neurons, and improve the prognosis [39, 42]. Research has proven that the molecular mechanism of down-regulating IL-1β can significantly reduce the expression of TNF-α, Bax, and caspase-3, and increase the expression of Bcl-2 [39]. Furthermore, IL-1β was reported to be involved in the pathogenesis of neuropathic pain [43], and it can be used as a molecular biomarker for the degree of such pain [44]. Given the strong link between ferroptosis and inflammation, scholars are interested in determining whether abnormal expression of IL-1β is associated with ferroptosis. An in vitro experiment demonstrated that up-regulating IL-1β expression induced ferroptosis in an osteoarthritis model [45]. However, there are not yet reports on whether this positive relationship between IL-1β and ferroptosis also exists in SCI. The results of our bioinformatics analysis showed that IL-1β was up-regulated in ferroptosis induced by SCI. We then verified the expression levels of IL-1β in vitro, and the results were consistent with the bioinformatics analysis. This indicates that IL-1β may be a core gene related to ferroptosis after SCI. What’s more, we predicted the target relationship between DEmiRNAs and ferroptosis-related DEmRNAs, thereby making a preliminary identification of miRNA-326 as the sponge gene of IL-1β.

MicroRNAs (miRNAs) are small non-coding RNAs widely involved in and influencing cellular and physiological functions in all multicellular organisms [46]. It has been revealed that miRNAs may perform their biological functions by directly interacting with target mRNAs [47]. It was reported that miR-326 was down-regulated and targeted TNFSF14 to promote pulmonary inflammation in the fibrotic lung tissues of silica-treated mice [48]. miR-326 was also discovered to inhibit the neddylation process and alleviate inflammatory bowel disease by targeting NEDD8 [49]. Recently, various reports have shown that the down-regulation of miR-326 promotes lung cellular inflammatory damage induced by lipopolysaccharide [50, 51]. The latest study reported that miR-326 expression was down-regulated in sevofluraneinduced human hippocampal neuron injury and promoted neuronal inflammation and apoptosis [52]. Furthermore, some scholars have found miRNA-326 to be highly expressed in multiple sclerosis patients[2, 53]. However, the expression of miR-326 in ferroptosis induced by SCI, and its specific functions in that scenario, remain unclear. In the present study, we verified the miRNA-326 expression level in vitro using an RT-qPCR assay and found the result was consistent with our prediction and most previous findings. Additionally, our study revealed that the target regulatory relationship of miR-326–IL-1β may be among the molecular mechanisms of ferroptosis induced by SCI.

However, this study had certain limitations that must be addressed in future research. First, only one human dataset was found to be available for biological analysis, whereas future research should seek to use multiple sequencing data. Second, certain clinical data were lacking due to the difficulty of obtaining blood samples from patients with acute SCI. Furthermore, it has been reported that gene expression varies at different time points after SCI [54], a variable that was not accounted for in this research design. Therefore, clinical studies with large samples and multiple time points are needed in the future. Third, only the miR-326–IL-1β expression levels were detected by RT-qPCR in our study, meaning further research is required to verify the specific mechanism of ferroptosis induced by SCI.

In sum, we investigated the expression of ferroptosis-related genes after SCI, analyzed the biological processes and signaling pathways of ferroptosis-related genes, and predicted the regulatory relationship between DEmiRNA and key genes. Our study revealed that the target regulatory relationship of miR-326–IL-1β may be among the molecular mechanisms of ferroptosis after SCI. Our results provide a basis for further investigation of those molecular mechanisms. In addition, our research also provides novel diagnostic and therapeutic targets for SCI, though a range of molecular experiments is needed to validate our findings in the future.

In conclusion, the current study identified 38 FRGs for SCI patients and constructed Ferroptosis-related regulatory networks based on the five hub genes including PPARG, IL-1β, PTGS2, IFNG and MAPK3. Interestingly, the miR-326 – IL-1β regulatory relationship has not been reported in SCI. Notably, the expression level of miR-326 – IL-1β were confirmed by RT-qPCR, which were consistent with the prediction, suggesting that miR-326 – IL-1β may play an essential role in molecular mechanisms of ferroptosis after SCI. The findings of our study may contribute to further explore mechanisms of ferroptosis after SCI, as well as provide some novel diagnostic and therapeutic targets for SCI. However, further verification experiments are necessary to validate our findings in the future.

SCI

Spinal cord injury

FRGs

Ferroptosis-related genes

Gene Ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

ceRNA

Competing endogenous RNA

ASCI

Acute spinal cord injury

GEO

Gene Expression Omnibus

DEmiRNAs

Differentially expressed miRNAs

DEmRNAs

Differentially expressed mRNAs

PPI

Protein-protein interaction

RT-Qpcr

Real-time quantitative polymerase chain reaction

Healthy control

Trauma control

TSCI

traumatic spinal cord injury

TFs

Transcription factors

BBB

Basso, Beattie, and Bresnahan Locomotor Scale

Standard deviation

Biological process

Cellular component

molecular functions

ROS

Reactive oxygen species

Availability of data and materials

The data presented in this study are openly available in GEO datasets (http://www.ncbi.nlm.nih.gov/gds/).

Funding

This research was supported by the Doctoral Innovation Project of the Guangxi Education Department (project no.: YCBZ2022083).

Author Contributions

S.X. conceived and designed the project and completed the writing—original draft preparation; X.L. and YY, completed the data analysis; Y.G. devised the methodology; J.C. revised the manuscript; Y.J. and Q.P. performed the experiments; J.X. reviewed and revised the manuscript before submission for publication. All authors have read and agreed to the published version of the manuscript.

Acknowledgements

Not applicable

Ethical Approval: The animal experiments in this paper have been approved by the Ethics Committee of Guangxi Medical University.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Rong Y, Ji C, Wang Z, Ge X, Wang J, Ye W, Tang P, Jiang D, Fan J, Yin G, et al. Small extracellular vesicles encapsulating CCL2 from activated astrocytes induce microglial activation and neuronal apoptosis after traumatic spinal cord injury. J Neuroinflammation. 2021;18(1):196.
Du C, Liu C, Kang J, Zhao G, Ye Z, Huang S, Li Z, Wu Z, Pei G. MicroRNA miR-326 regulates TH-17 differentiation and is associated with the pathogenesis of multiple sclerosis. Nat Immunol. 2009;10(12):1252–9.
Liu W, Rong Y, Wang J, Zhou Z, Ge X, Ji C, Jiang D, Gong F, Li L, Chen J, et al. Exosome-shuttled miR-216a-5p from hypoxic preconditioned mesenchymal stem cells repair traumatic spinal cord injury by shifting microglial M1/M2 polarization. J Neuroinflammation. 2020;17(1):47.
Liu C, Hu F, Jiao G, Guo Y, Zhou P, Zhang Y, Zhang Z, Yi J, You Y, Li Z, et al. Dental pulp stem cell-derived exosomes suppress M1 macrophage polarization through the ROS-MAPK-NFκB P65 signaling pathway after spinal cord injury. J Nanobiotechnol. 2022;20(1):65.
Shang Z, Wang M, Zhang B, Wang X, Wanyan P. Clinical translation of stem cell therapy for spinal cord injury still premature: results from a single-arm meta-analysis based on 62 clinical trials. BMC Med. 2022;20(1):284.
Zhang C, Yan Z, Maknojia A, Riquelme MA, Gu S, Booher G, Wallace DJ, Bartanusz V, Goswami A, Xiong W et al. Inhibition of astrocyte hemichannel improves recovery from spinal cord injury. JCI Insight 2021, 6(5).
Yu Q, Jiang X, Liu X, Shen W, Mei X, Tian H, Wu C. Glutathione-modified macrophage-derived cell membranes encapsulated metformin nanogels for the treatment of spinal cord injury. Biomater Adv. 2022;133:112668.
Wang C, Xu T, Lachance BB, Zhong X, Shen G, Xu T, Tang C, Jia X. Critical roles of sphingosine kinase 1 in the regulation of neuroinflammation and neuronal injury after spinal cord injury. J Neuroinflammation. 2021;18(1):50.
Xu P, Zhang F, Chang M-M, Zhong C, Sun C-H, Zhu H-R, Yao J-C, Li Z-Z, Li S-T, Zhang W-C, et al. Recruitment of γδ T cells to the lesion via the CCL2/CCR2 signaling after spinal cord injury. J Neuroinflammation. 2021;18(1):64.
Dixon SJ, Lemberg KM, Lamprecht MR, Skouta R, Zaitsev EM, Gleason CE, Patel DN, Bauer AJ, Cantley AM, Yang WS, et al. Ferroptosis: an iron-dependent form of nonapoptotic cell death. Cell. 2012;149(5):1060–72.
Stockwell BR, Friedmann Angeli JP, Bayir H, Bush AI, Conrad M, Dixon SJ, Fulda S, Gascón S, Hatzios SK, Kagan VE, et al. Ferroptosis: A Regulated Cell Death Nexus Linking Metabolism, Redox Biology, and Disease. Cell. 2017;171(2):273–85.
Chen Y, Liu S, Li J, Li Z, Quan J, Liu X, Tang Y, Liu B. The Latest View on the Mechanism of Ferroptosis and Its Research Progress in Spinal Cord Injury. Oxid Med Cell Longev 2020, 2020:6375938.
Feng Z, Min L, Chen H, Deng W, Tan M, Liu H, Hou J. Iron overload in the motor cortex induces neuronal ferroptosis following spinal cord injury. Redox Biol. 2021;43:101984.
Ge H, Xue X, Xian J, Yuan L, Wang L, Zou Y, Zhong J, Jiang Z, Shi J, Chen T, et al. Ferrostatin-1 Alleviates White Matter Injury Via Decreasing Ferroptosis Following Spinal Cord Injury. Mol Neurobiol. 2022;59(1):161–76.
Chen Y-X, Zuliyaer T, Liu B, Guo S, Yang D-G, Gao F, Yu Y, Yang M-L, Du L-J, Li J-J. Sodium selenite promotes neurological function recovery after spinal cord injury by inhibiting ferroptosis. Neural Regen Res. 2022;17(12):2702–9.
Ge M-H, Tian H, Mao L, Li D-Y, Lin J-Q, Hu H-S, Huang S-C, Zhang C-J, Mei X-F. Zinc attenuates ferroptosis and promotes functional recovery in contusion spinal cord injury by activating Nrf2/GPX4 defense pathway. CNS Neurosci Ther. 2021;27(9):1023–40.
Kyritsis N, Torres-Espín A, Schupp PG, Huie JR, Chou A, Duong-Fernandez X, Thomas LH, Tsolinas RE, Hemmerle DD, Pascual LU et al. Diagnostic blood RNA profiles for human acute spinal cord injury. J Exp Med 2021, 218(3).
Jia Y, Liu W, Zhan H-E, Yi X-P, Liang H, Zheng Q-L, Jiang X-Y, Zhou H-Y, Zhao L, Zhao X-L, et al. Roles of hsa-miR-12462 and SLC9A1 in acute myeloid leukemia. J Hematol Oncol. 2020;13(1):101.
Zhou N, Bao J. FerrDb: a manually curated resource for regulators and markers of ferroptosis and ferroptosis-disease associations. Database (Oxford) 2020, 2020.
Shirdel EA, Xie W, Mak TW, Jurisica I. NAViGaTing the micronome–using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs. PLoS ONE. 2011;6(2):e17429.
Tokar T, Pastrello C, Rossos AEM, Abovsky M, Hauschild A-C, Tsay M, Lu R, Jurisica I. mirDIP 4.1-integrative database of human microRNA target predictions. Nucleic Acids Res. 2018;46(D1):D360–70.
Liu W, Wang X. Prediction of functional microRNA targets by integrative modeling of microRNA binding and target expression data. Genome Biol. 2019;20(1):18.
Chen Y, Wang X. miRDB: an online database for prediction of functional microRNA targets. Nucleic Acids Res. 2020;48(D1):D127–31.
Li J-H, Liu S, Zhou H, Qu L-H, Yang J-H. starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res. 2014;42(Database issue):D92–7.
Wang J, Lu M, Qiu C, Cui Q. TransmiR: a transcription factor-microRNA regulation database. Nucleic Acids Res. 2010;38(Database issue):D119–22.
Tong Z, Cui Q, Wang J, Zhou Y. TransmiR v2.0: an updated transcription factor-microRNA regulation database. Nucleic Acids Res. 2019;47(D1):D253–8.
Zhang T, Li K, Zhang Z-L, Gao K, Lv C-L. LncRNA Airsci increases the inflammatory response after spinal cord injury in rats through the nuclear factor kappa B signaling pathway. Neural Regen Res. 2021;16(4):772–7.
Liu H, Zhang J, Xu X, Lu S, Yang D, Xie C, Jia M, Zhang W, Jin L, Wang X, et al. SARM1 promotes neuroinflammation and inhibits neural regeneration after spinal cord injury through NF-κB signaling. Theranostics. 2021;11(9):4187–206.
Li D, Yang T, Shao C, Cao Z, Zhang H. LncRNA MIAT activates vascular endothelial growth factor A through RAD21 to promote nerve injury repair in acute spinal cord injury. Mol Cell Endocrinol. 2021;528:111244.
Yao X, Zhang Y, Hao J, Duan H-Q, Zhao C-X, Sun C, Li B, Fan B-Y, Wang X, Li W-X, et al. Deferoxamine promotes recovery of traumatic spinal cord injury by inhibiting ferroptosis. Neural Regen Res. 2019;14(3):532–41.
Sumneang N, Siri-Angkul N, Kumfu S, Chattipakorn SC, Chattipakorn N. The effects of iron overload on mitochondrial function, mitochondrial dynamics, and ferroptosis in cardiomyocytes. Arch Biochem Biophys. 2020;680:108241.
Cao JY, Dixon SJ. Mechanisms of ferroptosis. Cell Mol Life Sci. 2016;73(11–12):2195–209.
Hassannia B, Vandenabeele P, Vanden Berghe T. Targeting Ferroptosis to Iron Out Cancer. Cancer Cell. 2019;35(6):830–49.
Hou Y, Luan J, Huang T, Deng T, Li X, Xiao Z, Zhan J, Luo D, Hou Y, Xu L, et al. Tauroursodeoxycholic acid alleviates secondary injury in spinal cord injury mice by reducing oxidative stress, apoptosis, and inflammatory response. J Neuroinflammation. 2021;18(1):216.
Yates AG, Jogia T, Gillespie ER, Couch Y, Ruitenberg MJ, Anthony DC. Acute IL-1RA treatment suppresses the peripheral and central inflammatory response to spinal cord injury. J Neuroinflammation. 2021;18(1):15.
Chen X, Kang R, Kroemer G, Tang D. Ferroptosis in infection, inflammation, and immunity. J Exp Med 2021, 218(6).
Zhang Y, Sun C, Zhao C, Hao J, Zhang Y, Fan B, Li B, Duan H, Liu C, Kong X, et al. Ferroptosis inhibitor SRS 16–86 attenuates ferroptosis and promotes functional recovery in contusion spinal cord injury. Brain Res. 2019;1706:48–57.
Dong H, Zhang C, Shi D, Xiao X, Chen X, Zeng Y, Li X, Xie R. Ferroptosis related genes participate in the pathogenesis of spinal cord injury via HIF-1 signaling pathway. Brain Res Bull. 2023;192:192–202.
Lin WP, Lin JH, Cai B, Shi JX, Li WJ, Choudhury GR, Wu SQ, Wu JZ, Wu HP, Ke QF. Effect of adenovirus-mediated RNA interference of IL-1β expression on spinal cord injury in rats. Spinal Cord. 2016;54(10):778–84.
Boato F, Rosenberger K, Nelissen S, Geboes L, Peters EM, Nitsch R, Hendrix S. Absence of IL-1beta positively affects neurological outcome, lesion development and axonal plasticity after spinal cord injury. J Neuroinflammation. 2013;10:6.
Zhou W, Yuan T, Gao Y, Yin P, Liu W, Pan C, Liu Y, Yu X. IL-1β-induces NF-κB and upregulates microRNA-372 to inhibit spinal cord injury recovery. J Neurophysiol. 2017;117(6):2282–91.
Li T, Li Y-T, Song D-Y. The expression of IL-1β can deteriorate the prognosis of nervous system after spinal cord injury. Int J Neurosci. 2018;128(8):778–82.
Yamashita T, Kamikaseda S, Tanaka A, Tozaki-Saitoh H, Caaveiro JMM, Inoue K, Tsuda M. New Inhibitory Effects of Cilnidipine on Microglial P2X7 Receptors and IL-1β Release: An Involvement in its Alleviating Effect on Neuropathic Pain. Cells 2021, 10(2).
Arman A, Deng F, Goldys EM, Liu G, Hutchinson MR. In vivo intrathecal IL-1β quantification in rats: Monitoring the molecular signals of neuropathic pain. Brain Behav Immun. 2020;88:442–50.
Xu W, Zhang B, Xi C, Qin Y, Lin X, Wang B, Kong P, Yan J. Ferroptosis Plays a Role in Human Chondrocyte of Osteoarthritis Induced by IL-1β In Vitro. Cartilage 2023:19476035221142011.
Gaudet AD, Fonken LK, Watkins LR, Nelson RJ, Popovich PG. MicroRNAs: Roles in Regulating Neuroinflammation. Neuroscientist. 2018;24(3):221–45.
Bartel DP. Metazoan MicroRNAs. Cell. 2018;173(1):20–51.
Xu T, Yan W, Wu Q, Xu Q, Yuan J, Li Y, Li P, Pan H, Ni C. MiR-326 Inhibits Inflammation and Promotes Autophagy in Silica-Induced Pulmonary Fibrosis through Targeting TNFSF14 and PTBP1. Chem Res Toxicol. 2019;32(11):2192–203.
Wang G, Yuan J, Cai X, Xu Z, Wang J, Ocansey DKW, Yan Y, Qian H, Zhang X, Xu W, et al. HucMSC-exosomes carrying miR-326 inhibit neddylation to relieve inflammatory bowel disease in mice. Clin Transl Med. 2020;10(2):e113.
Zhou G, Duan Y, Lu C, Wang W. Knockdown of circ-UQCRC2 ameliorated lipopolysaccharide-induced injury in MRC-5 cells by the miR-326/PDCD4/NF-κB pathway. Int Immunopharmacol. 2021;97:107633.
Liang Y, Miao Y, Xiang J. Circular RNA circESPL1 knockdown alleviates lipopolysaccharide (LPS)-induced lung cell injury via sponging miR-326 to regulate MAPK14. Int Immunopharmacol. 2022;112:109146.
Qian X, Zheng S, Yu Y. CircUBE3B High Expression Participates in Sevoflurane-Induced Human Hippocampal Neuron Injury via Targeting miR-326 and Regulating MYD88 Expression. Neurotox Res. 2023;41(1):16–28.
Honardoost MA, Kiani-Esfahani A, Ghaedi K, Etemadifar M, Salehi M. miR-326 and miR-26a, two potential markers for diagnosis of relapse and remission phases in patient with relapsing-remitting multiple sclerosis. Gene. 2014;544(2):128–33.
Li J-Z, Fan B-Y, Sun T, Wang X-X, Li J-J, Zhang J-P, Gu G-J, Shen W-Y, Liu D-R, Wei Z-J, et al. Bioinformatics analysis of ferroptosis in spinal cord injury. Neural Regen Res. 2023;18(3):626–33.

No competing interests reported.

supplementarymaterial.docx

Download PDF

Editorial decision: Revision requested
27 Mar, 2024
Reviewers agreed at journal
17 Mar, 2024
Reviews received at journal
16 Mar, 2024
Reviewers agreed at journal
15 Mar, 2024
Reviewers invited by journal
27 Feb, 2024
Editor invited by journal
22 Jan, 2024
Editor assigned by journal
18 Jan, 2024
Submission checks completed at journal
18 Jan, 2024
First submitted to journal
15 Jan, 2024

You are reading this latest preprint version

Identification of Ferroptosis-related Regulatory Network and Validation of the Expression of miRNA-326–IL-1β in Spinal Cord Injury

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Materials and Methods

Data Collection

Identification of DEmiRNAs, DEmRNAs and FRGs

GO and KEGG Pathway Enrichment Analysis of FRGs

Construction of PPI Network and Identification of Hub FRGs after SCI

Prediction of Target Relationship Between DEmiRNAs and FRGs

Construction of the ceRNA Network

Establishment of the TF–miRNA–mRNA Network

Prediction of hub FRG-targeted drugs

Rat SCI Model

Basso, Beattie, and Bresnahan Locomotor Scale Assessment

Real-time quantitative polymerase chain reaction (RT-qPCR)

Statistical Analysis

Results

Identification of DEmRNAs, DEmiRNAs, and FRGs

GO and KEGG Pathway Enrichment Analysis of FRGs

Construction of PPI Network and Identification of Hub FRGs after SCI

Prediction of Target Relationships Between DEmiRNAs and Hub FRGs

Construction of the lncRNA–miRNA–mRNA ceRNA Network

Establishment of the TF–miRNA–mRNA Network

Prediction of hub FRG-targeted drugs

Validation of Ferroptosis-related Genes Expression in SCI Rat Model

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1