Competitive Endogenous RNA Network Associated with Pyroptosis in the Acute Phase of Post Traumatic Brain Injury

doi:10.21203/rs.3.rs-5259688/v1

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Competitive Endogenous RNA Network Associated with Pyroptosis in the Acute Phase of Post Traumatic Brain Injury

https://doi.org/10.21203/rs.3.rs-5259688/v1

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Traumatic brain injury (TBI) is a prevalent neurosurgical condition that can lead to significant disability and mortality. This study investigates the role of long non-coding RNAs (lncRNAs) and pyroptosis in neuroinflammation during the acute phase post-TBI. We analyzed 58 pyroptosis-related genes through mRNA-seq in the injured brain of 33 mice subjected to controlled cortical impact (CCI), organized into 11 groups with different time points (0, 1, 2, 3, 4, 6, 12, 24, 72, 148 hours), including a sham control. Notably, due to the significance of 12-hour time point in the acute inflammatory response, it was selected for whole RNA-seq to profile lncRNA expression, which revealed 540 differentially expressed mRNAs (419 upregulated, 121 downregulated) and 95 lncRNAs (42 upregulated, 53 downregulated). Four key pyroptosis genes (Casp4, Il1a, Il1b, and Il6) were significantly overexpressed. Utilizing the R package “multiMiR” and various databases (“miRDB”, “Starbase” and “LncBase v3.0”), we identified miRNA-mRNA and lncRNA-miRNA interactions, culminating in a pyroptosis-associated competitive endogenous RNA (ceRNA) network comprising 4 lncRNAs, 16 miRNAs, and 4 mRNAs. The 4 lncRNAs and 4 mRNAs showed concordance between the targeted gene expression verified by reverse transcription quantitative polymerase chain reaction (RT-qPCR) and the whole RNA-seq results. Our findings indicate that Casp4-mediated non-canonical pyroptosis may play a critical role during the acute phase following TBI, offering insights into potential therapeutic targets and mechanisms for TBI management.

Traumatic brain injury (TBI)

acute phase

pyroptosis

long non-coding RNAs (lncRNAs)

ceRNA

Traumatic brain injury (TBI) is a serious public health problem caused by external physical forces, resulting in brain tissue damage and neurological dysfunction, which is also a leading cause of death and disability^[1]. TBI causes primary mechanical injuries and subsequent delayed secondary injuries. Increasing evidence indicates that TBI is not only an acute disease, but can also trigger significant systemic changes^{[2, 3]}. The pathophysiological processes following TBI involve a complex cascade of events, including neuroinflammation, neuronal injury, and potential long-term neurological impairments, characterized by significant heterogeneity in injury patterns^[4]. Unfortunately, there has been little progress in the treatment of TBI, and few randomized controlled trials have been conducted to inform treatment guidelines. Uniform intervention faces challenges due to heterogeneity, as demonstrated by the repeated failures of neuroprotective clinical trials^{[5, 6]}.

Emerging studies have illuminated pyroptosis as a novel cell death mechanism associated with neuroinflammation in recent years^{[7, 8]}, and it may also exacerbate the consequences of TBI^[9]. Due to its inherent pro-inflammatory nature, there is growing research on the role of pyroptosis in TBI^[10]. During acute TBI, particularly within the first 12 hours (h), cellular stress initiates complex molecular cascades that can lead to pyroptosis of neurons, microglia and astrocytes. The biological effects of TBI-induced inflammation may act as a double-edged sword at different phases^[11]. Thus, by comprehending the roles played by inflammation following TBI, more effective strategies for TBI treatment approaches may be developed, potentially reducing the risk of neurological and psychiatric consequences for patients.

The competitive endogenous RNA (ceRNA) hypothesis, proposed in 2011 by Pandolfi et al.^[12], reveals the intricate interactions of pools of different forms of RNA and microRNA (miRNA) pools^[13], including messenger RNA (mRNA) and non-coding RNAs such as long non-coding RNA (lncRNA), circular RNA (circRNA) and pseudogenes. This hypothesis posits that these RNA transcripts can compete for binding to shared miRNAs, thereby typically leading to the degradation of the target RNA or inhibition of its translation. Understanding ceRNA interactions is crucial in TBI research, as they may modulate cellular responses in disease onset and progression, extending beyond cancer to non-neoplastic conditions^{[14, 15]}. Currently, only a few studies have addressed complex ceRNA networks after TBI^[16–19]. A study identified a network of 23 mRNAs, 5 miRNAs, and 2 lncRNAs using C57BL/6 mice 1 day (d) after TBI and revealed a potential Neat1/miR-31-5p/Myd88 inflammation-related axis^[17]. Yang et al. investigated lncRNAs and mRNAs during the subacute phase in adult mice, finding 12 lncRNAs, 59 miRNAs, and 25 mRNAs^[19]. These dysregulated molecules may serve as therapeutic targets to address long-term recovery issues after TBI. Regarding the early stages post-TBI, Jiang et al. established a circular RNA (circRNA) expression profile 6 h after TBI, providing promising potential therapeutic circRNA targets^[16]. However, research on ceRNA in relation to lncRNA during the acute phase of TBI is still lacking. Thus, studying the molecular mechanisms during the acute phase after TBI will therefore be of considerable significance.

This study aims to fill this gap by constructing a ceRNA network focused on pyroptosis during the early acute stage in TBI. By using the controlled cortical impact (CCI) model to simulate TBI, we discovered critical ceRNA partners regulating pyroptosis through deep transcriptomic sequencing combined with bioinformatics, which include 4 lncRNAs, 16 miRNAs, and 4 mRNAs, uncovering new regulatory mechanism and potential therapeutic targets after TBI. Graphical abstract image displays the study's flow chart.

Animals and Cells

The male C57BL/6N mice (10 weeks old, 23–25 g) were purchased from SPF Biotechnology Co. Ltd. (Beijing, China) and housed in a barrier-free and pathogen-free environment with unrestricted diet and water and 12 h of light and darkness per day. The experimental procedures were performed in accordance with the Guide for the Care and Use of Laboratory Animals published by the National Institutes of Health and the ARRIVE Guidelines 2.0 (Animal Research: Reporting of In Vivo Experiments)^[20] and were approved by the Institutional Animal Experimental Committee. The immortalized BV2 cell line was purchased from the Cell Bank of the Chinese Academy of Sciences and cultured in Dulbecco’s modified Eagle’s medium (Gibco, USA) with 10% fetal bovine serum (Gibco, USA) and 1% penicillin-streptomycin solution (Wuhan Pricella Biotechnology Co., Ltd., China) according to the product instructions. BV2 were cultured in a 5% CO2 incubator at 37°C.

Establishment of traumatic brain injury model

Based on the previous literature^{[21, 22]}, TBI was induced using the CCI model. Briefly, after anesthesia, the mice were placed in a prone position on a stereotaxic apparatus, ensuring the head remained level, followed by hair removal and disinfection of the surgical area with iodophor. The skull was fully exposed, and a portable cranial drill was used to create a 4 mm-diameter bone window in the left parietal lobe, positioned 2 mm lateral to the midline, between the bregma and lambda. The parameters of the CCI device were set to an impact speed of 3.5 m/s, dwell time of 500 msec, and tissue depth of 2 mm, delivering a precise cortical impact using a 3 mm diameter impactor tip perpendicular to the brain surface. The scalp was sutured immediately after TBI. Throughout the surgery, a thermostatic heating pad was utilized to maintain the animals' core body temperature at 37°C. The animals were randomly divided into 11 groups, a sham group and ten TBI groups across different time points, each consisting of 3 mice per group. The sham operation group underwent identical procedures except for the CCI. At 0 h, 1 h, 2 h, 3 h, 4 h, 6 h, 12 h, 1 d, 3 d and 7 d after injury, we performed perfusion with normal saline in all animals and collected the injured brain tissues for bulk RNA-seq, and 12 h for whole transcriptome sequencing.

Neuroinflammation Model Induced by Lipopolysaccharide (LPS)

Lay BV2 cells in 12-well plates a day in advance, with about 2×10⁵ cells per well. When the cell confluence was about 80%, 1 µg/ml LPS diluted in medium was added for continuing culture, and total RNA was then extracted after 6 h of stimulation for subsequent experiments.

RNA Isolation and RNA sequencing

Total RNA from injury and surrounding tissue was isolated utilizing the Trizol reagent (Invitrogen, USA) following the manufacturer’s instructions. The purity and concentration of the RNA were assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, USA). Transcriptome sequencing was performed by OE Biotech Co., Ltd. (Shanghai, China). In addition, total RNA from cells was extracted using RNAeasy Animal RNA Isolation Kit with Spin Column (Beyotime, Shanghai, China).

Gene expression time series analysis

Following the methodology for pseudotime analysis in RNA bulk-seq^[23–25], we utilized the R package "bulkPseudotime" to conduct time series analysis on our sequence data. PCA analysis was performed on sample points, and Euclidean distances between them were calculated using PC1 and PC2 to derive a timeline, which was subsequently scaled to a range of 0–10. Using this new timeline, we generated expression profiles of the key pyroptosis-related genes across different time points.

Difference Analysis and Enrichment Analysis

We performed the relevant bioinformatics analysis on R software. The ‘DESeq2’ package was employed to identify the differential expression of mRNAs and lncRNAs between TBI and sham-operation groups, in which met the criteria of a adjust p value (FDR) < 0.05 and the absolute value of log₂(fold change) > 1 were considered as differentially expressed genes (DEGs) and lncRNAs (DELs). Subsequently, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted based on hypergeometric distribution algorithm to screen for significantly enriched functions and signaling pathways through R package “clusterProfiler”^[26]. In addition, we used “c2.cp.kegg.v7.4.symbols.gmt” and “c5.go.v7.4.symbols.gmt” for further gene set enrichment analysis (GSEA)^[27].

CeRNA Network

We defined a pyroptosis-related genes set containing 58 genes(Supplementary Table) from the MSigDB database^[28] and previous literature^{[29, 30]}. Key molecules were identified by the gene set and DEGs. The ceRNA network was constructed using these key genes. Pearson correlation coefficients were utilized to calculate the expression correlation between up-regulated DELs and aforementioned core genes. A criterion of absolute value of Pearson correlation coefficients > 0.8 was applied to obtain significantly correlated lncRNA-mRNA co-expression relationships. Finally, the R package “multiMIR”^[31] and miRDB database^[32] were used to predict the miRNA-mRNA targets, while the lncRNA-miRNA interactions were predicted by Starbase^[33] and LncBase v3.0 database^[33]. The intersection of the miRNAs identified from these two analyses was considered for inclusion in the ceRNA network, as it represented the shared miRNAs interacting with both mRNAs and lncRNAs. Accordingly, a ceRNA molecular network was established and visualized by R software.

Weighted Gene Co-Expression Network Analysis (WGCNA)

We constructed a correlation matrix using the weight method to calculate pairwise correlations between all mRNAs and lncRNAs using the R package “WGCNA”^[34]. Firstly, we draw a systematic clustering tree of samples to check the quality of the data after filtering out lowly expressed genes. The appropriate soft thresholding performance was selected based on the scale-free topology criterion. Then the one-step method was selected to build a weighted lncRNA-mRNA co-expression network. Gene modules were generated according to a soft thresholding power of 12, a minimum module gene count of 50, and a minimum module merge cut height of 0.25. Traits are classified by binary factor. Pearson correlation was used to calculate the correlation between each module and the traits. Finally, by analyzing the module genes that correlated most strongly with the traits and components of the ceRNA network, we were able to further pinpoint key molecules.

Reverse transcription quantitative polymerase chain reaction (RT-qPCR)

NovoScript®Plus All-in-one 1st Strand cDNA Synthesis SuperMix (Novoprotein Scientific Inc., Suzhou, China) was used to efficiently synthesize first strand cDNA using total RNA as template. Then NovoStart® Fast SYBR qPCR SuperMix (Novoprotein Scientific Inc., Suzhou, China) was prepared for RT-qPCR using QuantStudioTM 5 Real-Time PCR System (Thermo Fisher Scientific, USA). The experiment was conducted strictly according to the manufacturer’s instructions. Primers in the study were designed independently and synthesized by the TsingKe biological technology (Beijing, China), which are displayed in Table 1. All experiments had 3 biological replicates, with each biological replicate having 3 technical replicates. The lncRNAs and mRNAs relative expression levels were normalized using GAPDH as reference gene, which shown as 2^−ΔΔCt methods.

Table 1

The primers sequence for RT-qPCR.
Name	Primers sequence
Casp4	F: 5’ GCTGATGCTGTCAAGCTGAG 3’ R: 5’ GAGCCTCCTGTTTTGTCTCG 3’
Il1a	F: 5’ CGTCAAGCAACGGGAAGATT 3’ R: 5’ ATCTGGGTTGGATGGTCTCTTC 3’
Il1b	F: 5’ TGTAATGAAAGACGGCACACC 3’ R: 5’ TCTTCTTTGGGTATTGCTTGG 3’
Il6	F: 5’ GCTACCAAACTGGATATAATCAGGA 3’ R: 5’ CCAGGTAGCTATGGTACTCCAGAA 3’
F630028O10Rik	F: 5’ AGCTGAGTTGGACACTCTGTG 3’ R: 5’ AGGCTATGTGCTCTGTCCTG 3’
F730311O21Rik	F: 5’ CGCACGGTAACGTTTGGATG 3’ R: 5’ GGCCAGATGAGTCCGACATT 3’
Mir17hg	F: 5’ CACAGGTTGGGATTTGTCGC 3’ R: 5’ GTGGAAATCGGCATCTTCAGC 3’
BE69007	F: 5’ TGGGCACCGATCCTGACTAT 3’ R: 5’ TTCTGTTGCTGTAACTGTAGCTT 5’

Statistical Analysis

All data were analyzed with the GraphPad Prism (version 9.5.0) software and R software (version 4.3.1). The differences in expression levels between two groups were assessed using a two-tailed Student’s t-test. A p-value of less than 0.05 was considered statistically significant.

Temporal Expression Patterns of Pyroptosis-Related Genes Following TBI

We obtained bulk RNA-Seq data at various time points following TBI (Fig. 1A.B). After filtering out low-expression genes, we extracted the time-series expression profiles of pyroptosis-related genes, identifying a total of 48 pyroptosis-associated genes. Our analysis revealed that more than half of these genes exhibited varying degrees of activation post-TBI. Notably, genes such as Casp4, Nlrp3, Nod2, Il1a, Il1b, Il6, and Tnf-α showed significant activation as early as 2 h post-TBI (Fig. 1C). Transcriptional profiling indicated that Casp4 peaked around 12 h and 1 day, whereas Casp1 displayed activation trends only after 12 h (Fig. 1D). The majority of the identified genes were highly expressed at 1 day (62.5%), 3 days (72.9%), and 7 days (72.9%) post-TBI. These findings suggest that pyroptosis plays a critical and non-negligible role in the pathophysiological response to TBI.

DEGs and DELs in the Acute Phase Following TBI

To elucidate the transcriptional landscape during the acute phase following TBI, we conducted whole transcriptomic sequencing of tissues at 12 h post-injury (Fig. 2). Bioinformatics analysis revealed 540 DEGs between the TBI and sham-operated groups, including 419 upregulated mRNAs and 121 downregulated mRNAs, as well as with 95 DELs, comprising 42 upregulated lncRNAs and 53 downregulated lncRNAs. Volcano plot and heatmap for DEGs and DELs are shown in Fig. 2. Notably, nearly 80% of the DEGs exhibited upregulation state post-TBI. The top five upregulated genes were Cxcr2, Il36g, Mmp8, Hcar2, and Mmp3. These findings underscore the heightened transcriptional activity and the potential roles of these genes in the early response to TBI.

Gene Set Enrichment Analysis

The enrichment analysis revealed significant enrichment of biological processes (BP), cellular components (CC), and molecular functions (MF) through GO analysis (Fig. 3A). A total of 1710 GO terms were found to be significantly enriched (adjusted p-value < 0.05), shedding light on the functional roles of the identified genes. Top 10 of BP were “cytokine-mediated signaling pathway”, “leukocyte migration”, “positive regulation of response to external stimulus”, “leukocyte cell-cell adhesion”, “cell chemotaxis”, “positive regulation of defense response”, “leukocyte chemotaxis”, “myeloid leukocyte migration”, “regulation of tumor necrosis factor superfamily cytokine production” and “neutrophil migration”. MF enrichment included terms related to “cytokine receptor binding”, “cytokine activity”, “G protein-coupled receptor binding”, “cell adhesion molecule binding”, “immune receptor activity”, “cytokine binding” and so on. Concurrently, KEGG pathway analysis identified 37 significantly enriched pathways (adjusted p-value < 0.05), highlighting the involvement of the gene set in key inflammatory signaling pathways such as “Cytokine-cytokine receptor interaction”, “TNF signaling pathway”, “IL-17 signaling pathway”, “NF-kappa B signaling pathway”, and “Chemokine signaling pathway” (Fig. 3B, Supplementary Fig. 2A). Additionally, GSEA unveiled significant activation of gene sets associated with “Cytokine-cytokine receptor interaction”, “Nod like receptor signaling pathway”, “Chemokine signaling pathway”, “Hematopoietic cell lineage”, and “Toll like receptor signaling pathway”, emphasizing the relevance of the gene set to the underlying biological context (Fig. 3C). A significant finding is that we observed the marked inhibition of the "Long term depression" pathway at 12 h post-TBI, suggesting early indications of cognitive impairment following TBI (Supplementary Fig. 2B).

Identification of Key Molecules and Construction of CeRNA Network

By intersecting the pyroptosis-related gene list with the DEGs, we identified four core genes during the 12-hour post-TBI acute phase: Casp4, Il1a, Il1b, and Il6 (Fig. 5A), all of which were upregulated. Correlation analysis between the expression levels of these four genes and 42 upregulated DELs revealed a correlation heatmap depicted in Fig. 5B, where correlations > 0.8 highlighted 25 lncRNAs. Subsequently, using the 'multiMiR' R package and the 'miRDB' database, we predicted miRNAs potentially binding to these key genes, resulting in 166 miRNAs. Additionally, leveraging the “Starbase” and “LncBase v3.0” databases, we identified 25 lncRNAs with correlations > 0.8 that could interact with miRNAs, yielding only 4 specific lncRNAs (F630028O10Rik, F730311O21Rik, BE692007, Mir17hg) and their corresponding 90 miRNAs from these sources. The intersection of these two miRNA lists identified 16 common miRNAs, serving as intermediary bridge molecules in the ceRNA network. Finally, we constructed a TBI acute phase pyroptosis-related ceRNA network comprising 4 lncRNAs, associated 16 miRNAs, and 4 mRNAs (Fig. 5C). Subsequently, we demonstrated the lncRNA-mRNA correlation chord diagram, highlighting the significance and representativeness of Casp4 and Il1b in the acute phase 12 h post-TBI (Fig. 5D). Further, the ceRNA network was narrowed by WGCNA analysis (Fig. 4), and then the lncRNA-miRNA-mRNA axis with Il1b as the core was obtained (Fig. 5E).

The Expression of lncRNA and mRNA Validated by RT-qPCR

At both animals (Fig. 6A) and cellular levels (Fig. 6B), validation of lncRNA and mRNA expression post-TBI reveals significant upregulation. The results indicate marked increases in these lncRNAs and mRNAs following TBI.

TBI represents a multifaceted challenge in clinical neuroscience, characterized by immediate mechanical damage followed by complex secondary injury cascades that exacerbate neuronal loss and functional deficits. The acute phase post-TBI, particularly within the first 12 hours, is critical for understanding the molecular events that determine the subsequent pathological consequences. Our study leveraged whole RNA-seq coupled with advanced bioinformatics analysis to identify key inflammatory molecules and construct a ceRNA molecular network associated with pyroptosis during this acute phase. Ultimately, we identified 4 lncRNAs (F630028O10Rik, F730311O21Rik, BE692007, Mir17hg), 16 miRNAs and 4 mRNAs (Casp4, Il1a, Il1b, Il6) as critical targets in the acute phase after TBI.

The molecular mechanisms of pyroptosis can be broadly categorized into four distinct signaling pathways: the canonical inflammasome pathway, the non-canonical inflammasome pathway, the apoptotic caspases-associated pathway (caspase-3/8) and the granzymes-mediated pathway^{[8, 35]}. The canonical pathway involves the formation of inflammasomes, leading to the activation of inflammatory caspase-1. This activation promotes the maturation and release of IL-18 and IL-1β and triggers the cleavage of the gasdermin D (GSDMD) protein, releasing its N-terminal fragment, which then forms pores in the cell membrane, resulting in the release of cellular contents and cell death^[36]. Non-canonical pyroptosis, on the other hand, does not rely on caspase-1 but is mediated primarily by caspase-4/5/11, which can be directly activated by lipopolysaccharide (LPS) and subsequently cleave GSDMD to induce pyroptosis. The N-terminal fragment generated by caspase-4/5/11 cleavage of GSDMD theoretically promotes inflammasome activation and caspase-1-dependent pyroptosis, potentially through potassium efflux^[37]. Research suggests that IL-1β plays a role in early inflammation, while IL-18 contributes to sustained inflammation activation in TBI^{[38, 39]}. Interestingly, our findings support this idea, as we observed that the peak expression of caspase-11 occurs at 12 hours post-TBI, preceding that of caspase-1. IL-1β shows an earlier increase at 6 hours post-TBI, while IL-18 remains elevated seven days post-TBI, indicating a potential role for pyroptosis in both the acute and chronic phases of TBI. Moderate pyroptosis contributes to organismal homeostasis, whereas excessive pyroptosis can sustain inflammation. Prolonged inflammation is a major cause of secondary damage, and excessive activation of neuroinflammation is considered critical in the development of post-TBI complications such as motor and cognitive impairments^[40]. Thus, targeting pyroptosis could be a key strategy for interventions during the acute phase of TBI.

Through enrichment analysis, we found that cytokines and related inflammatory pathways, such as TNF and IL-17, are activated 12 hours after TBI. This indicates a complex intercellular communication during the acute phase of TBI, where the cross-talk among different cytokines may contribute to diverse and heterogeneous outcomes. Notably, we found a significant downregulation of “long term depression” signaling pathway at 12 hours post-TBI, suggesting an early impairment of synaptic plasticity following TBI. Recent studies have shown that TBI is a significant risk factor for age-related neurodegenerative diseases^[41]. The risk of all-cause dementia increases by 63–96% following TBI^{[42, 43]}. Researchers have detected increased expression of proteins associated with neurodegenerative diseases as early as 7 days post-injury^[44], indicating that neurodegenerative processes may begin soon after TBI. Thus, for early intervention strategies targeted at improving outcomes from TBI, it is important to look into molecular targets during the acute phase of TBI.

LncRNAs are typically defined as non-coding transcripts longer than 200 nucleotides, and currently, over 100,000 human lncRNAs have been recorded^[45]. Aberrant expression of lncRNAs has been implicated in various diseases such as cancer, cardiovascular diseases, endocrine disorders, and neurological diseases^{[46, 47]}. They often participate in inflammation and immune regulation under pathological conditions through ceRNA mechanisms^[48–50]. Using bioinformatic analysis, we identified a ceRNA network related to pyroptosis during the acute phase of TBI, comprising 4 lncRNAs, 16 miRNAs, and 4 mRNAs, suggesting these molecules hold significant potential for further exploration. F630028O10Rik predominantly localizes in the cytoplasm and contributes to formation of ceRNA net, with limited prior research reported^{[51, 52]}. Encouragingly, Xu et al. reported on F630028O10Rik in spinal cord injury (SCI), finding its activation post-SCI and its involvement in promoting microglial pyroptosis via the miR-1231-5p/Col1a1 ceRNA axis^[51]. We believe that it has an inestimable prospect in future, and further experiments are needed to explore its potential molecular mechanism. Research indicates that overexpression of Mir17hg following intracerebral hemorrhage (ICH) induces pro-inflammatory activation in microglial cells in mice and suppressing its expression aids in reducing inflammation and resolving hematoma^[53]. This corresponds with our finding that elevated Mir17hg post-TBI may impact TBI outcomes by regulating pyroptosis. We report the potential biological significance of two lncRNAs in disease for the first time, BE692007 and F730311O21Rik, which demonstrate promising performance in animal models and cell inflammation models for TBI. Although BE692007 did not show significant difference in animal tissue, its substantial increase following LPS stimulation suggests its role in inflammation post-TBI via the BE692007/miR-6240/Il1b axis. Researchers have identified miR-6240 as a potential biomarker and therapeutic target in an Alzheimer's disease^[54], providing a new direction for our future research.

MiR-105 is recognized as a protective factor in myocardial infarction, inhibiting ischemic cell death through the suppression of necrotic and apoptotic pathways^[55]. We propose that after TBI, miR-105 binds to lncRNAs, potentially leading to upregulation of Il-6 expression and exacerbating the inflammatory response. Further investigation is needed to determine if miR-105 plays a protective role post-TBI akin to its function in ischemia-hypoxia situations. Through literature review, we found that miR-212-5p within the ceRNA network may be a key miRNA, with several studies indicating its neuroprotective role ^{[56, 57]}. For instance, it is regulated by the lncfos/miR-212-5p/Casp7 axis in ischemic stroke, protecting the brain from injury^[56]. Additionally, after TBI, miR-212-5p inhibits neuronal death by targeting Ptgs2 and mitigating ferroptosis^[57]. We hypothesize that downregulation of miR-212-5p post-TBI promotes cell pyroptosis through the F630028O10Rik/miR-212-5p/Casp4 axis, thereby aggravating secondary damage. In conclusion, the intertwined roles of lncRNAs and miRNAs in TBI necessitate further extensive research to validate their functions in the post-TBI context.

Among the key cellular participants in the response to TBI, microglia, the resident immune cells of the central nervous system (CNS), have emerged as pivotal players due to their versatile roles across different stages of injury^{[58, 59]}. Microglia are crucial for coordinating immune responses, clearing cellular debris, and engaging in synaptic pruning following TBI. Acute activation of microglia can potentially mitigate the immediate effects of injury, while prolonged activation may contribute to neurodegeneration. To explore the role of microglia in the acute phase of TBI, we established a neuroinflammation model by stimulating BV2 cells with LPS. The results confirmed that key pyroptosis molecules during the acute phase of TBI showed similar outcomes after 6 h of LPS stimulation, underscoring the significant presence of microglia post-TBI. Although F630028O10Rik did not show difference after LPS stimulation, this may be attributed to limitations of cell model and the timing of LPS stimulation.

However, the study still has the following limitations. First, variability in sampling injured tissue may lead to differences in specific cellular composition, which could potentially affect the interpretation of molecular responses. Second, although bulk RNA-seq provides a comprehensive view of gene expression, it lacks the resolution to distinguish cell-specific changes, potentially masking crucial insights into cellular responses and interactions after TBI. Furthermore, the use of animal models and cellular inflammation models to simulate TBI, while valuable for studying certain aspects of the disease, cannot fully recapitulate the complex pathophysiological mechanisms observed in human TBI. In conclusion, while the study provides valuable insights into the ceRNA network of acute phase in TBI, these limitations underscore the need for further research utilizing advanced single-cell sequence method, refined injury models, and integrative approaches to further understand how to improve ceRNA-mediated regulatory networks in TBI pathology.

Overall, our study contributes to understanding the key molecules during the acute phase of TBI by constructing a ceRNA network. This provides potential and promising targets for therapy during this critical phase. Future research should focus on these molecules and their functional mechanisms across different stages of TBI.

Disclosure

There are no competing financial interests.

Funding

This study was supported by National Natural Science Foundation of China (NSFC) (NO.32300816), Elite Medical Professionals Project of China-Japan Friendship Hospital (ZRJY2024-QM07), National High Level Hospital Clinical Research Funding of China (2022-NHLHCRF-YS-05), Grants from National Key R&D Program of China (2022YFC2402500) and Beijing Municipal Natural Science Foundation (L222034).

Author Contribution

All authors had full access to the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Conceptualization, Z.Y.L and Y.Y.B; Methodology, Z.Y.L, D.H.H, Z.C.P, W.Z; Investigation, H.K, C.P.Y, Z.Y.S; Formal Analysis, D.H.H and Z.C.P; Writing - Original Draft, D.H.H and Z.Y.L; Writing - Review & Editing, Z.Y.L, T.S.Z and D.H.H; Visualization, Z.C.P and D.H.H; Funding Acquisition, Z.Y.L and Y.Y.B; Supervision and project Administration, Z.L and Y.Y.B. All authors read and approved the final manuscript.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Competitive Endogenous RNA Network Associated with Pyroptosis in the Acute Phase of Post Traumatic Brain Injury

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Abstract

Figures

Introduction

Materials and Methods

Animals and Cells

Establishment of traumatic brain injury model

Neuroinflammation Model Induced by Lipopolysaccharide (LPS)

RNA Isolation and RNA sequencing

Gene expression time series analysis

Difference Analysis and Enrichment Analysis

CeRNA Network

Weighted Gene Co-Expression Network Analysis (WGCNA)

Reverse transcription quantitative polymerase chain reaction (RT-qPCR)

Statistical Analysis

Results

Temporal Expression Patterns of Pyroptosis-Related Genes Following TBI

DEGs and DELs in the Acute Phase Following TBI

Gene Set Enrichment Analysis

Identification of Key Molecules and Construction of CeRNA Network

The Expression of lncRNA and mRNA Validated by RT-qPCR

Discussion

Declarations

Disclosure

Funding

Author Contribution

Data Availability Statement

References

Additional Declarations

Supplementary Files

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