Male C57BL/6 mice (8–10 weeks old, 22–25 g) were purchased from the Experimental Animals Center of Southern Medical University. The mice were kept in a temperature-controlled animal facility under normal light/dark cycle with free access to food and water. All animals adapted to the environment for 7 days before experiments. All animal experiments were approved by the Southern Medical University Administrative Panel on Laboratory Animal Care and conducted in accordance with the guidelines of Animal Use and Care of Southern Medical University.

To induce cerebral I/R injury, a transient middle cerebral artery occlusion (tMCAO) model was performed on the mice as previously described (Luo et al., 2017).Briefly, mice were anesthetized with sevoflurane (5% for induction and 2–3% for maintenance). Following a midline cervical incision, the right common carotid artery (CCA), external carotid artery (ECA), and internal carotid artery (ICA) were carefully exposed under an operating microscope. Thereafter, a silicone rubber-coated nylon monofilament (Yushun Biological Technology Co. Ltd., Pingdingshan, China) was inserted into the ECA, and advanced to occlude the middle cerebral artery for 90 min. After 90 min occlusion, the monofilament was gently pulled out for reperfusion and the incision was sutured. Mice in sham group adopted a same surgery except the middle cerebral artery occlusion.

Adult mice were randomized into three groups (sham group, tMCAO group and tMCAO + celastrol group; n = 18 in each group). Celastrol (Selleckchem, Houston, TX, United States) was dissolved in 1% dimethylsulfoxide (DMSO) (Sigma-Aldrich, St. Louis, MO, United States) at the concentration of 4.5 mg/kg and injected intraperitoneally at the onset of reperfusion. The mice in sham and tMCAO groups without drug treatment were injected with the same volume of DMSO. Mice were re-anesthetized and sacrificed 24 h after tMCAO. The concentration of celastrol used in the experiment was based on the concentration reported in previous study (Chen et al., 2022).

The 2,3,5-triphenyltetrazolium chloride (TTC) (Sigma-Aldrich, St. Louis, MO, United States) staining was used to determine cerebral infarction volume. After 90 min of MCAO and 24 h of reperfusion, the mice were anesthetized with 5% sevoflurane, and their brains (n = 5/group) were rapidly removed and coronally cut into six slices at a thickness of 2 mm using a rodent brain matrix. The brain slices were stained with 2% TTC at 37°C for 15 min and subsequently fixed in 4% paraformaldehyde at 4°C overnight. After TTC staining, the red area indicated no infarction while the white area indicated infarction. The brain slices were scanned and the infarct size was analyzed using Image J software (National Institutes of Health, Bethesda, MD, United States) by researchers who were blinded to the study group. In order to exclude the effect of cerebral edema, the following calculation formula was used: (contralateral hemisphere area–ipsilateral non-ischemic hemisphere area)/contralateral hemisphere area × 100% (Liu M. et al., 2021). Five male mice were used in each group.

The neurological deficit scores of the mice were evaluated at 24 h after tMCAO by researchers who were blinded to the experimental groups. According to the modified Bederson score, the neurological grading scores range from 0 to 5 (0, no deficit; 1, forelimb flexion; 2, as for 1, plus decreased resistance to lateral push; 3, unidirectional circling; 4, longitudinal spinning or seizure activity; and 5, no movement) (Jin et al., 2015). Seven male mice were used in each group.

Motor performance was accessed by accelerating rotarod test after evaluating the neurological deficit scores. The mice were trained to remain on the rotarod at a starting rotation of 5 r/min which was accelerated to 40 r/min over 60 s before the model establishment (Du et al., 2020). The mice were tested under the same accelerated conditions after 24 h of reperfusion. The entire test lasted 300 s and was performed three times for each mouse at 10-min intervals. The latency of falling off of the rod were recorded and averaged.

Primary cortical neurons were obtained from embryonic day C57BL/6 mice and cultured as previously described (Luo et al., 2017). Briefly, pregnant mice were euthanized and the cerebral cortices of the embryos were dissected and dissociated by mild trypsinization, followed by trituration in DNAase I (Sigma-Aldrich, St. Louis, MO, United States). The cells were suspended in neurobasal medium supplemented with 2% B-27 (Gibco, Grand Island, NY, United States) and 0.5 mM Glutamax (Gibco, Grand Island, NY, United States). The single cell suspension was plated in 6-well plates precoated with poly-L-lysine, and the cell culture was kept in a humidified atmosphere of 5% CO₂ at 37°C. Half of the culture medium was replaced every 3 days and neurons were cultured for 9 days for use in subsequent experiments.

Primary cultures of astrocytes were prepared from cortices of C57BL/6 newborn mice (P1–P3). Briefly, the bilateral cortices were dissected in a sterile environment and digested with 0.25% trypsin and DNAase I for 10 min at 37°C. Subsequently, cortical fragments were suspended in Dulbecco’s Modified Eagle’s medium (DMEM)/F12 (Gibco, Grand Island, NY, United States) with 10% fetal bovine serum (Gibco, Grand Island, NY, United States). Single cell suspensions were made by repeated pipetting and the cells were incubated at 37°C in a humidified 5% CO₂ chamber for 7 days. The culture medium was replaced twice a day.

BV2 microglial cells, which were bought from Shanghai Gaining Biological Technology Co. Ltd., were cultured in DMEM (Gibco, Grand Island, NY, United States) with 10% fetal bovine serum at 37°C in CO₂/air (5/95%) mixture.

To simulate ischemia-reperfusion injury in vitro, primary cortical neurons, primary astrocytes and BV2 microglial cells were subjected to oxygen and glucose deprivation (OGD) followed by reoxygenation. Primary astrocytes and BV2 microglial cells were incubated with glucose-free DMEM and placed within a hypoxic chamber which was continuously maintained with 95% N₂, 5% CO₂, 1% O₂ at 37°C for 5 h, while primary cortical neurons for 4 h. OGD was terminated by replacing the glucose-free DMEM to their normal culture medium in the normoxic incubator with 95% air and 5% CO₂ for 24 h. Cells incubated in normal culture medium under a normoxic incubator were used as the normoxic control. At the same time, celastrol was applied in the culture medium with the final concentration of 0.5 μM for 24 h. In contrast, the same volume of DMSO was applied in the culture medium in the control group cells. All experiments were at least duplicated three times biologically.

The total RNA from the ischemic hemisphere or cells was isolated with the TRIzol Reagent (Invitrogen, Carlsbad, CA, United States) according to the manufacturer’s instructions. RNA purity and concentration were evaluated by using the Nanodrop ND-2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, United States). RNA Integrity Number (RIN) was analyzed by Bioanalyzer 2100 (Agilent, Palo Alto, CA, United States). If the RIN number is >7, it can be used for high-throughput transcriptome sequencing.

A total of 5 μg RNA from ischemic hemisphere was utilized for each RNA sample. Firstly, ribosomal RNA (rRNA) was depleted by Ribo-Zero Gold rRNA Removal Kit (Illumina, San Diego, CA, United States). Secondly, the left RNAs were fragmented into short fragments using divalent cations (NEBNext^® Magnesium RNA Fragmentation Module, NEB, Ipswich, MA, United States) under high temperature. The complementary DNA (cDNA) was synthesized and purified. Finally, the average insert size for the final cDNA library was 300 ± 50 bp. 2 × 150 bp paired-end sequencing was performed on Illumina Novaseq™6000 (LC-Bio Technology Co. Ltd., Hangzhou, China) according to the recommended protocol.

Clean reads were obtained by removing reads containing adapter, reads containing ploy-N and low-quality reads from raw data by Cutadapt (Love et al., 2014). Then sequence quality was verified using FastQC (Babraham Bioinformatics, Babraham Institute, Cambridge, United Kingdom), including the Q20, Q30, and GC-content of the clean data. The downstream analysis was done on high-quality clean data.

Cuffdiff (v2.1.1) was used to calculate fragments per kilobase million (FPKMs) of both lncRNAs and mRNAs. The differentially expressed lncRNAs and mRNAs were selected with fold change > 2 or fold change < 0.5 and P-value < 0.05 by DESeq2. To outline the characteristics of gene expression profiles, heatmaps and volcano plots were generated by using the R package.

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of differentially expressed genes (DEGs) was implemented with DAVID.¹ GO analysis includes the categories biological processes (BP), cellular components (CC), and molecular functions (MF). GO terms with P < 0.05 were defined as significantly enriched GO terms in DEGs. Pathways with P < 0.05 were considered as significantly enriched pathways in DEGs.

The soft clustering Mfuzz function is based on the fuzzy c-means algorithm of the e1071 package. The R/Bioconductor package was used for soft clustering of genes, and Mfuzz-specific clusters were selected based on gene expression trends (Yang et al., 2020). The genes were determined by default parameters and the number of clusters were 12.

According to the competitive endogenous RNA (ceRNA) mechanism, miRNA can lead to gene silencing by binding mRNA, while ceRNA can regulate mRNA expression by competitively binding miRNA. In this study, DE lncRNAs and DE mRNAs were constructed for ceRNA network. DE mRNA was put into Starbase database (Starbase V3.0) to predict upstream miRNAs, and DE lncRNA was put into miRcode to determine the targeted miRNAs. Finally, the lncRNA-miRNA-mRNA ceRNA network formed by the intersection of the two groups of predicted miRNAs. The ceRNA network was visualized by Cytoscape (Li et al., 2014).

Total RNA (1 μg) was used to synthesize cDNA using a ReverTra Ace qPCR RT Master Mix with gDNA Remover (TOYOBO, Tokyo, Japan). Quantitative real-time PCR was performed on the ABI QuantStudio 6 flex (Applied Biosystems, Carlsbad, CA, United States) using SYBR Green Realtime PCR Master Mix (TOYOBO, Tokyo, Japan). GAPDH was used as a reference gene for quantification. Each experimental group was performed in triplicate to obtain the cycle time (CT) mean and the results of the analyses were calculated using the 2-ΔΔCT equation. The primer sequences were shown in the Supplementary Table 1.

Data are expressed as mean ± SEM. Differences were evaluated by one-way analysis of variance (ANOVA; three or more groups). P < 0.05 was considered statistical significance. Statistical analyses were performed using SPSS 20.0 Statistics (IBM SPSS Statistics for Version 20.0, IBM Corp, Armonk, NY United States).

We examined whether celastrol improve infarct volume and neurological behavior after tMCAO. After 90 min occlusion, celastrol was immediately injected at a concentration of 4.5 mg/kg at the beginning of reperfusion. Infarct volume was measured with TTC staining after 24 h of reperfusion. The results show that the infarct volume was apparently larger in the tMCAO group (48.87 ± 2.86%) compared with sham group (0%), whereas celastrol treatment significantly reduced I/R-induced infarct volume to 35.89 ± 2.10% (Figures 1A,B). Similarly, the neurological deficit scores of mice in the tMCAO group were significantly increased to 2.85 ± 0.34, but celastrol treatment decreased neurological outcomes to 1.71 ± 0.28 (Figure 1C). Subsequently, the mice were subjected to the Rotarod fatigue test. The results showed that the time spent on the rotarod of mice in tMCAO group was 52.21 ± 5.60 s, while celastrol treatment significantly increased the time spent on the rotarod to 113.40 ± 6.40 s (Figure 1D).

To explore the mechanism underlying the neuroprotective function of celastrol in tMCAO, RNA-sequencing analysis was performed. Gene expression profiles for sham, tMCAO and tMCAO + celastrol groups were visualized as heatmap (Figure 2A). The fold change (FC) > 2 or FC < 0.5 and P < 0.05 were used as the threshold to identify the DEGs between each two groups. A total of 50 DE lncRNAs (Supplementary Table 2) and 696 DE mRNAs (Supplementary Table 3) were identified between the sham and tMCAO group. And a total of 544 DE lncRNAs (Supplementary Table 4) and 324 DE mRNAs (Supplementary Table 5) were identified between tMCAO and tMCAO + celastrol group. The mRNA profiles were further analyzed, and the distribution of mRNA was displayed by volcano plots. 612 upregulated DE mRNAs and 84 downregulated DE mRNAs were found between the sham and tMCAO group (Figure 2B). However, 168 upregulated DE mRNAs and 156 downregulated DE mRNAs were found between the tMCAO and tMCAO + celastrol group (Figure 2C). The distribution of mRNA in sham and tMCAO + celastrol group was also shown in Figure 2D.

Gene Ontology and KEGG enrichment analysis were performed with DE mRNAs between sham and tMCAO groups. The top 10 results of GO analysis in the biological process on mRNA are shown in Figure 2E, including neutrophil migration, granulocyte migration, myeloid leukocyte migration, leukocyte chemotaxis, leukocyte migration, cell chemotaxis, cytokine-mediated signaling pathway, positive regulation of response to external stimulus, adaptive immune response, and regulation of immune effect or process. The top 10 results of the KEGG analysis of DE mRNAs also appeared in Figure 2F, including viral protein interaction with cytokine and cytokine receptor, malaria, complement, and coagulation cascades, rheumatoid arthritis, IL-17 signaling, cytokine-cytokine receptor interaction, TNF signaling pathway, lipid and atherosclerosis, NOD-like receptor signaling and chemokine signaling pathway. The results above indicated the potential connection between these pathways and the effects of celastrol. The results above indicated that the neuroprotective effects of celastrol are potentially related to these biological processes and metabolic pathways.

The soft clustering method was used to assign genes to several clusters based on expression patterns. For mRNA, a total of six clusters were obtained by Mfuzz analysis in the three groups (Figure 3A). These six clusters could be classified into two large classes. One type of clusters showed upregulation of gene expression between the tMCAO and tMCAO + celastrol group (including cluster 1, 2, and 3), while another showed the downregulation. According to the experimental design, cluster 2 and cluster 4 were selected for further analysis. In order to gain insight into the biological function, GO and KEGG enrichment analysis were performed in these two clusters. The top 10 results of GO analysis in the biological process on cluster 2 and cluster 4 are shown in Figures 3B,E respectively. The results showed that genes in these two clusters were associated with different biological processes. Genes in Cluster 2 were mainly associated with neuro-related processes, such as synapse organization, axonogenesis and neuron differentiation. Genes in Cluster 4 were mainly associated with immune and inflammation. The top 5 results of the KEGG analysis also appeared in Figures 3C,F respectively. In addition, the expression of mRNA in cluster 2 and cluster 4 were shown in the heatmap respectively (Figures 3D,G). The DE mRNAs from mRNA cluster 2 and cluster 4 were respectively listed in Supplementary Tables 6, 7.

For lncRNA, a total of six clusters were obtained by Mfuzz analysis in the three groups (Figure 4A). These six clusters also could be classified into two large classes. One type of clusters showed downregulation of gene expression between the tMCAO and tMCAO + celastrol group (including cluster 1, 2, and 3), while another showed the upregulation. The expression of lncRNA in cluster 3 and cluster 5&6 were shown in the heatmap respectively (Figures 4B,C). The DE lncRNAs from lncRNA cluster 3 and cluster 5&6 were listed in Supplementary Table 8.

Based on differential analyses and interaction prediction, the lncRNA-miRNA-mRNA ceRNA network were established. The ceRNA networks included both positive and negative regulation (Figure 5). DE mRNAs in Mfuzz Cluster 2 (up-regulated) and DE lncRNAs in lncRNA Mfuzz Cluster 5&6 (up-regulated) were both used to predict the bound miRNAs, then the intersection of these two groups of miRNAs were formed into ceRNA network. Finally, six up-regulated lncRNAs, nine down-regulated miRNAs, and 16 up-regulated mRNAs were constructed into lncRNA-miRNA-mRNA ceRNA networks (Figure 5A). Similarly, DE mRNAs in Mfuzz Cluster 4 (down-regulated) and DE lncRNAs in lncRNA Mfuzz Cluster 3 (down-regulated) were used to predict the bound miRNAs, then the ceRNA network formed by intersection of these two groups of mirnas. Finally, ceRNA networks were constructed by 4 up-regulated lncRNAs, 16 down-regulated miRNAs and 36 up-regulated mRNAs (Figure 5B). The network consists of lncRNAs (rectangles), miRNAs (triangles), and mRNAs (circles). The red pots represent up-regulated RNAs and the green pots represent down-regulated RNAs. Above of all indicated potential critical RNA interactions involved in celastrol treatment.

The relationship between lncRNAs and mRNAs was based on the Cis and Trans function. Figure 6A shows the interaction network of differentially expressed up-regulated lncRNAs with DE mRNAs which is also the target genes of lncRNAs. Similarly, Figure 6B shows the interaction network of differentially expressed down-regulated lncRNAs with their differentially expressed target mRNAs. The Triangle node and the round node respectively represent lncRNAs and mRNAs. The red color reveals that the lncRNAs or mRNAs are significantly up-regulated, otherwise indicated with a green color.

Based on the above bioinformatic analysis, we selected several DE lncRNAs and DE mRNAs for qRT-PCR validation. 5830444B04Rik-Elavl4, C030005K06Rik-Sh3bp4, and 4930413F20Rik- HIF-1α were selected from the ceRNA network, and Gm15444-Ascl2/Acod1 was selected from the DE lncRNA-DE mRNA interaction network. They were selected for further validation in the brain tissue, primary neurons, primary astrocytes, and BV2 cells by qRT-PCR. In the brain tissue, 5830444B04Rik, C030005K06Rik, Elavl4, Sh3bp4, HIF-1α, Ascl2, and Acod1 showed the same fold change patterns as those in the RNA-Seq results (Figure 7A). The expression of 4930413F20Rik and Gm15444 were consistent with lncRNA cluster 3, and Ascl2, and Acod1 were also consistent with mRNA cluster 4 in three types of cells. The expression of HIF-1α was consistent with mRNA cluster 4 both in the primary astrocyte and BV2 cells and the expression of Sh3bp4 was consistent with mRNA cluster 2 in the primary astrocyte (Figures 7B–D). But some of them did not reach statistical significance. The validation of the other DEGs were shown in the Supplementary Figure 1.