Acute ischemic stroke (IS) is a leading cause of disability and death in adults worldwide, especially in China, where there are >2 million new cases per year (1,2). Neuroinflammation, which occurs minutes to hours after stroke, is a major pathological process and is significantly associated with brain damage (3). Numerous animal and clinical studies have suggested that the inflammatory modulation of the central nervous system might be a viable alternative therapeutic strategy for acute IS (3,4).
Microglia are resident innate immune cells in the brain and play crucial roles in both pathological and physiological conditions, such as response to brain injury, microenvironment maintenance, and pathogen defense (5,6). After the occurrence of cerebral ischemia, microglia are mobilized and transferred to the damaged areas, where they rapidly release cytotoxic mediators and contribute to the occurrence and spread of neuroinflammation (7). Cerebral reperfusion further activates microglia and leads to an enhanced cascade of inflammatory responses that ultimately lead to further neuronal damage, which is referred to as ischemia/reperfusion injury (I/R) (8). Currently, few ideal drugs are available to suppress microglia-mediated inflammation (9).
Podoplanin (PDPN), which is a small transmembrane mucin-like glycoprotein, is widely expressed in microglia, dendritic cells, type I alveolar cells, macrophages, and different types of fibroblasts (10). Previous studies have shown that PDPN promotes the inflammatory response in different diseases, including rheumatoid arthritis, lung injury, and traumatic brain injury (10,11). Additionally, the neutralization of PDPN via an anti-PDPN antibody (α-PDPN) has been shown to suppress inflammatory responses and improve heart function after myocardial infarction (12). Recently, the role of PDPN in neuroinflammation has been examined. PDPN expression can be induced by hydrogen peroxide, lipopolysaccharide, hemoglobin, and I/R injury on microglia, and play a pro-inflammatory role in cerebral injury (13,14). Notably, α-PDPN pretreatment has been shown to attenuate neurological deficits and reduce cerebral infarct volume in a mouse model of IS by reducing the inflammatory response (14). However, the molecular mechanism by which α-PDPN inhibits I/R-induced microglial activation is not fully understood.
Thus, this study sought to explore the molecular mechanism by which α-PDPN inhibits the activation of microglia subjected to I/R injury. In this study, microglia BV2 cells were pre-treated with α-PDPN and then subjected to oxygen-glucose deprivation and reoxygenation (OGD-R). The differentially expressed genes (DEGs) underwent a transcriptome sequencing technology analysis, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The RNA-seq data are available in GenBank Sequence Read Archive (SRA) database with accession number PRJNA825112. We present the following article in accordance with the MDAR reporting checklist (available at https://atm.amegroups.com/article/view/10.21037/atm-22-1952/rc).
α-PDPN treatment and OGD-R insult
The murine microglial BV2 cell line (5×104 cells/mL, Procell Life Science and Technology, Co. Ltd., Wuhan, China) was cultured in complete Dulbecco’s Modified Eagle’s medium (DMEM, Procell Life Science and Technology, Co. Ltd.) containing 10% fetal bovine serum, streptomycin (100 U/mL), and penicillin (100 U/mL) at 37 ℃ with 5% carbon dioxide (CO2). Next, the cells were treated with α-PDPN (10 µg/mL, Bio X Cell, clone 8.1.1, West Lebanon, NH, USA) in complete DMEM medium for 6 h, and then subjected to OGD-R, which is an accepted in-vitro model of I/R (15). In brief, the cells were grown in a serum and glucose-free medium in a hypoxia chamber (H35; Don Whitley Scientific Ltd., Shipley, UK) containing 5% CO2 and 95% nitrogen at 37 ℃ for 6 hours. The cells were then grown in complete DMEM medium for 20 h in an incubator with 5% CO2 and 95% air at 37 ℃. The cells in the α-PDPN group were treated with α-PDPN (10 µg/mL) during OGD-R (n=3 per group), and the cells in the control group were treated with phosphate buffered solution (20 µL, 0.01 M, pH 7.4).
Transcriptome sequencing analysis
Total ribonucleic acid (RNA) was extracted from the BV2 cells, and its quality was detected. Briefly, total RNA was extracted using a RNeasy mini kit (Qiagen, Duesseldorf, Germany). Qualified total RNA (3 µg) was further purified by an RNA Clean XP Kit (Cat A63987, Beckman Coulter, Inc. Kraemer Boulevard Brea, CA, USA). An RNA library was generated using the RNA-Sequencing Library Prep Kit (Illumina, CA, USA) in accordance with the manufacturer’s instructions. First, total RNA was cut into short fragments by a fragment high mix buffer at 94 ℃ for 8 min. Next, the 1st-strand complementary deoxyribonucleic acid (cDNA) was synthesized at 25 ℃ for 10 min, followed by 42 ℃ for 15 min, and 70 ℃ for 15 min. The 2nd-strand cDNA was subsequently synthesized at 16 ℃ for 1 h. The cDNA fragments were then treated with A-Tailing Mix, and the adapters were ligated to the ends of these template DNAs. Finally, the cDNA library was synthesized by polymerase chain reaction (PCR) amplification, and library quality was evaluated on an Agilent 2100 BioAnalyzer (Agilent Technologies, Santa Clara, CA, USA). The RNA library was run on a NovaSeq 6000 platform (Illumina, CA, USA). The library construction and sequence analysis were performed at Shanghai Boho Biological Co. Ltd. (Shanghai, China).
Screening of DEGs
To normalize the expression levels of different genes and different samples, the reads were converted to fragments per kilobase of transcripts per million fragments mapped (FPKM) value. The false discovery rate (FDR) is the adjusted P value obtained by multiple hypothesis test correction on the original P value (16). The DEGs in the libraries were defined as having a FPKM value ≥1.5 and an FDR <0.05.
Functional annotation of the DEGs
To better understand the biological functions and pathways associated with the DEGs, GO and KEGG enrichment analyses were performed using the functional annotation tool of Database for Annotation, Visualization, and Integrated Discovery (DAVID) (version 6.8, https://david.ncifcrf.gov). The number of DEGs contained in each GO item and the rich factor of each GO item was calculated. A GO item with a P value <0.05 was considered significantly enriched. Additionally, a KEGG pathway analysis was conducted to harvest pathway clusters and describe the molecular interactions in differentially genes. In the pathway map, the DEGs were marked red if upregulated and green if downregulated.
Real-time polymerase chain reaction (RT-PCR) confirmation
The expression of 9 DEGs was selected and validated by RT-PCR. Among them, the following 6 genes were downregulated: interferon regulatory factor 7 (IRF7), interferon-gamma inducible protein 204 (IFI204), interferon-stimulated gene 20 (ISG20), ISG15, chemokine (C-C motif) ligand 2 (CCL2), and CCL7. Among them, the following 3 genes were upregulated: nuclear receptor subfamily 4 group A member 1 (Nr4a1), ATP Binding cassette subfamily A member 5 (Abca5), and cyclin B2 (CCNB2). Total RNA was isolated using Trizol (Invitrogen, Carlsbad, CA, USA). Next, the mRNA was reversely transcribed to cDNA. Subsequently, RT-PCR was performed using the SYBR-Green PCR Master Mix kit (Takara, Tokyo, Japan) at 55 ℃ for 2 min, followed by 35 cycles of 95 ℃ for 20 s and 60 ℃ for 1 min on a PCR Detection System (Applied Biosystems, Prism 7500, USA). All RT-PCR reactions were performed 3 times. The 2−ΔΔCt method was used to calculate the relative quantification with calibration to the internal control glyceraldehyde-3-phosphate dehydrogenase (GAPDH). The specific primers for the selected genes are listed in Table 1.
|Gene name||Forward primer (5'–3')||Reverse primer (5'–3')|
|ISG20||AACATC CAG AACAACTGG CGG||GTCTGACGTCCCAGGGCA|
IRF7, interferon regulatory factor 7; IFI204, interferon-gamma inducible protein 204; ISG20, interferon-stimulated gene 20; CCL2, chemokine (C-C motif) ligand 2; Abca5, ATP Binding cassette subfamily A member 5; CCNB2, cyclin B2; Nr4a1, nuclear receptor subfamily 4 group A member 1; GAPDH, glyceraldehyde-3-phosphate dehydrogenase.
For the sequencing data, the FDR value was P<0.05 or a difference multiple ≥2 between the 2 groups indicated statistical significance. For the RT-PCR, the data are expressed as the mean ± standard error of the mean (SEM). The statistical analysis was performed using GraphPad Prism version 5.0 (GraphPad Software Inc., San Diego, CA, USA).
Identification of DEGs
A principal component analysis (PCA) is a statistical technique for dimensionality reduction. Using a covariance or correlation matrix, the PCA performs a linear transformation, replacing possibly related variables with a set of smaller linear independent variables (17). In this study, the score plots of the PCA showed an obvious separation between the α-PDPN (PC1, 60.6%) and OGD-R (PC2, 11.9%) groups (see Figure 1A). A larger PC1 value indicated a larger degree of genetic change by α-PDPN treatment.
Further, 338 mRNAs were downregulated and 340 mRNAs were upregulated in the α-PDPN treatment group as compared to OGD-R group (fold change ≥2; q<0.05; see Figure 1B). The top 40 downregulated and upregulated genes are shown in Figure 1C,1D, respectively. Several downregulated genes were related to interferon (IFN) regulators and chemokines, such as IRF7, ISG20, ISG15, IFI204, CCL2, CCL7, and CCL12.
Verification of DEGs by RT-PCR
To validate the DEGs identified by the RNA-sequencing platform, 9 DEGs were selected for the RT-PCR confirmation. In line with the results of the transcriptome sequencing analysis, the RT-PCR results indicated that the gene expression of IRF7, IFI204, ISG20, ISG15, CCL2, and CCL7 was more reduced, and the gene expression of Nr4a1, Abca5, and CCNB2 was more increased in the BV2 cells in the α-PDPN treatment group than the OGD-R group (fold change ≥2; P<0.05; see Figure 2A-2I). Thus, the results of the transcriptome sequencing analysis were largely consistent with those of the RT-PCR analysis, which provides some evidence of the credibility of the transcriptome sequencing data.
GO functional enrichment analysis
To detect the biological functions of the identified DEGs, a GO functional enrichment analysis was conducted. The top 30 enriched categories of the downregulated DEGs in the BV2 cells after the α-PDPN treatment are shown in Figure 3A. The downregulated GO terms mainly involved biological processes, such as response to IFNs (IFN-α, and IFN-ꞵ), progesterone, the lipopolysaccharide-mediated signaling pathway, viral genome replication, and the regulation of cell chemotaxis (including macrophages, eosinophils, monocytes, granulocytes, and lymphocytes), and migration (including mononuclear cells, eosinophils, and lymphocytes). The downregulated molecular function was enriched in C-C motif chemokine receptor (CCR) binding. The upregulated expressed GO terms are shown in Figure 3B. The upregulated molecular functions mainly involved glucocorticoid receptor (GR) binding. These results indicate that the α-PDPN treatment inhibited IFN signaling and cell chemotaxis and migration, but upregulated GR binding in the I/R-injured BV2 cells.
KEGG pathway enrichment analysis
A KEGG pathway analysis of the DEGs was conducted. The top 30 rich factors of the downregulated pathways are shown in Figure 4A. The enriched categories of the upregulated DEGs mainly involved the ATP-binding cassette (ABC) transporters (see Figure 4B). As Table 2 shows, the downregulated pathways included the IL-17 signaling pathway, osteoclast differentiation, hepatitis C, influenza A, the tumor necrosis factor (TNF) signaling pathway, Epstein-Barr virus infection, the chemokine signaling pathway, the transforming growth factor beta (TGF-ꞵ) signaling pathway, the nucleotide-binding and oligomerization domain (NOD)-like receptor (NLR) signaling pathway, and cytokine-cytokine receptor interaction (rich factor >3, Q value <0.05).
|Pathway ID||Pathway description||Gene down list||Rich factor||P value||Q value|
|mmu04657||IL-17 signaling pathway||S100a8, Mmp13, Fosl1, Il17ra, Ccl7, Ccl12, Ccl2, Lcn2||7.408642||5.97E-06||0.000543|
|mmu05144||Malaria||Tgfb1, Lrp1, Ccl12, Ccl2||7.254296||0.00061||0.018499|
|mmu04380||Osteoclast differentiation||Gm15931, Tgfb1, Socs3, Fosl1, Sirpb1a, Stat2, Fcgr4, Fcgr1, Junb, Pirb||6.645156||1.87E-06||0.000341|
|mmu05323||Rheumatoid arthritis||Flt1, Tgfb1, Ccl5, Ccl12, Ccl2||5.061136||0.001304||0.026374|
|mmu05150||Staphylococcus aureus infection||C3ar1, Cfb, Fcgr4, C5ar1, Fcgr1||4.946111||0.001467||0.026695|
|mmu05160||Hepatitis C||Irf7, Myc, Ifit1bl1, Socs3, Stat2, E2f2, Oas3, Ifit1, Mx2||4.866236||7.08E-05||0.004298|
|mmu04350||TGF-beta signaling pathway||Myc, Tgfb1, Id3, Id1, Id2||4.731062||0.001838||0.02788|
|mmu05164||Influenza A||Irf7, Ccl5, Socs3, Stat2, Oas3, Ccl12, Ccl2, Tnfsf10, Mx2||4.635881||0.000105||0.004786|
|mmu04640||Hematopoietic cell lineage||Anpep, Itga5, Csf2ra, Cd34, Fcgr1||4.630401||0.002049||0.026638|
|mmu05220||Chronic myeloid leukemia||Myc, Tgfb1, E2f2, Gadd45g||4.522158||0.004861||0.05529|
|mmu04668||TNF signaling pathway||Ccl5, Socs3, Junb, Bcl3, Ccl12, Ccl2||4.502666||0.001134||0.025797|
|mmu05169||Epstein-Barr virus infection||Irf7, H2-T24, Myc, Isg15, Stat2, E2f2, Oas3, Gadd45g, Jak3, Tapbp||3.851838||0.000255||0.009279|
|mmu05162||Measles||Irf7, Stat2, Oas3, Jak3, Tnfsf10, Mx2||3.812476||0.002916||0.035386|
|mmu04062||Chemokine signaling pathway||Ccr4, Ccl5, Stat2, Ccl7, Jak3, Cxcr3, Ccl12, Ccl2||3.482062||0.00166||0.027458|
|mmu04621||NOD-like receptor signaling pathway||Irf7, Gbp2, Ccl5, Stat2, Oas3, Ccl12, Ccl2, Ifi204||3.380643||0.002029||0.028403|
|mmu04060||Cytokine-cytokine receptor interaction||Ccr4, Tgfb1, Csf2ra, Ccl5, Il17ra, Ccl7, Tnfsf12, Cxcr3, Ccl12, Ccl2, Tnfsf10||3.202565||0.000739||0.019208|
KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes; α-PDPN, anti-podoplanin antibody; IL-17, interleukin-17; TGF, transforming growth factor; TNF, tumor necrosis factor; NOD, nucleotide-binding and oligomerization domain.
The genes involved in the hepatitis C signaling pathway were significantly downregulated in the BV2 cells after the α-PDPN treatment (see Table 2). Similarly, the α-PDPN treatment suppressed the IFN-related pathways, such as influenza A, Epstein-Barr virus infection, and measles (see Table 2). Additionally, many genes involved in the IL-17 signaling pathway were downregulated, such as IL-17A receptor (IL-17RA), activator protein-1, S100 calcium binding proteins A8 (S100A8), matrix metallopeptidase-13 (MMP13), CCL7, CCL2, and lipocalin 2 (LCN2) (see Table 2). Further, the KEGG pathway analysis indicated that many genes involved in the NLR signaling pathway were downregulated, such as IRF3/7, IFI16/IFI204, guanylate-binding protein 2 (Gbp2), signal transducer and activator of transcription 2 (Stat2), 2'-5'-oligoadenylate synthetase 3 (Oas3), monocyte chemoattractant protein-1 (MCP-1, also known as CCL2), and chemokine RANTES (also known as CCL5) (see Table 2). These results suggested that the α-PDPN treatment inhibited the inflammatory response in the OGD-R injured microglia.
The molecular mechanism of α-PDPN treatment on I/R-injured microglia remains unclear. In this study, we used OGD-R injured BV2 cells to explore changes in the transcriptome sequencing data after the α-PDPN treatment. The results indicated that a total of 338 genes were downregulated and 340 genes were upregulated in the α-PDPN-treated BV2 cells. Notably, the GO and KEGG enrichment analyses revealed that the α-PDPN treatment inhibited the inflammation-related signaling pathways in the OGD-R injured microglia.
It is widely known that the activation of the type-I IFN pathway in microglia is detrimental to the prognosis of IS, as it creates a pro-inflammatory environment (18,19). Hypoxia and ischemia can directly induce microglial activation, and numerous interferon-stimulated genes (ISG) have been shown to be increased in brain tissue subjected to I/R, which in turn activates the type-I IFN pathway, leading to neuroinflammation (20). Recently, Liao et al. proposed that the cyclic GMP-AMP synthase (cGAS) is activated in the ischemic penumbra of mice by self-mitochondrial DNA released from the mitochondria of cells, which in turn promotes the activation of the IFN signaling pathway (21). In the current study, we found that several GO items involved in the IFN signaling pathway were downregulated after the α-PDPN treatment. Similar results were found in the KEGG enrichment analysis, and the α-PDPN treatment suppressed a number of IFN signaling pathways, such as the hepatitis C, influenza A, and Epstein-Barr virus infection pathways.
The GO enrichment analysis revealed that the α-PDPN treatment inhibited the chemotaxis and migration of a variety of cells, including eosinophils, lymphocytes, macrophages, monocytes, and granulocytes. In the presence of tissue damage, microglia are polarized toward the pro-inflammatory M1 phenotype, releasing pro-inflammatory chemokines such as CCl2, CCL12, and CCL7, which guide other immune cells to migrate to the damaged area and initiate an inflammatory response (22). Consistent with these findings, the KEGG enrichment analysis showed that the α-PDPN treatment inhibited the IL-17 signaling pathway. The binding of IL-17 to the IL-17R activates the IL-17R signaling pathway and ultimately triggers the expression of proinflammatory chemokines (CCL2, CCL7, and CCL20) (23). Similarly, the effect of α-PDPN on IL-17 signaling pathway was reported in an in-vitro experimental study, which showed that the preincubation of isolated peripheral blood mononuclear cells with α-PDPN reduced the secretion of IL-17 and IL-1ꞵ (24).
Our study also showed that the α-PDPN treatment increased GR binding and ABC transporters in I/R-injured BV2 cells. Ligand-occupied GRs have powerful anti-inflammatory effects, which act by repressing or inducing the transcription of target genes by direct binding to DNA response elements and/or by other transcription factors (25). For example, nuclear receptor 4a1 (Nr4a1), a member of GRs, has been identified as a key rheostat that controls microglial activation and pro-inflammatory polarization (26,27). Notably, we demonstrated that the α-PDPN treatment increased the Nr4a1 expression in the BV2 cells. Consistently, Gibson et al. reported that ABC transporters inhibit the inflammatory response of microglia, and that the decreased function of ABC transporters leads to the retention of toxic chemicals and abnormal cell-cell communication (28).
Further, it is increasingly accepted that the NLR signaling pathways have a detrimental effect on the progression of IS. For example, the NLR protein 3 (NLRP3) inflammasome drives robust inflammation and pyroptosis (lytic cell death) in neurological diseases such as ischemic brain injury (29). Recently, several research groups have shown that the IFN signaling pathways play an important role in the activation of the NLR signaling pathways. For example, Li et al. reported that phosphorylated IRF3 translocated into the nucleus and increased the expression of NLRP3 in a mouse cardiomyocytes model (30). Additionally, IFI204 and its human homologue IFI16 are involved in the detection of aberrant nucleic acid, originating from mitochondrial or DNA damage. The elevated expression of IFI16/IFI204 initiates the activation of the cGAS/stimulator of interferon genes (STING) (a stimulator of IFN genes) pathway, which in turn promotes the activation of the NLR signaling pathways (31,32). Our study showed that the α-PDPN treatment decreased the expression of IRF7, ISG15, and IFI204 in BV2 cells.
In conclusion, there were a large number of DEGs in the OGD-R injured BV2 cells after the α-PDPN treatment. The GO and KEGG enrichment analyses showed that the α-PDPN treatment mainly inhibited the expression of inflammation-related genes and signaling pathways. Our findings not only provide insights into the molecular regulation mechanism of α-PDPN treatment, but also provide a basis for extending its use in clinical trials.
Funding: This work was supported by the National Natural Science Foundation of China (No. 81873431).
Reporting Checklist: The authors have completed the MDAR reporting checklist. Available at https://atm.amegroups.com/article/view/10.21037/atm-22-1952/rc
Data Sharing Statement: Available at https://atm.amegroups.com/article/view/10.21037/atm-22-1952/dss
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-22-1952/coif). The authors have no conflicts of interest to declare.
Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.
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