The microarray dataset from GSE58294 was obtained from Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/gds/) and preprocessed by the R software (Version 4.0.3; R Foundation for Statistical Computing, Vienna, Austria). The dataset includes blood samples from cardiogenic stroke patients (n = 69) and control subjects (n = 23). The average expression value of these probe sets was computed for genes with multiple probe sets. Differential analysis was performed using the GEO2R tool after the probe IDs were converted to gene symbols. Fold change (FC) threshold $>$1 was called upregulated differentially expressed genes (DEGs), FC 1 was called downregulated DEGs, and the adjusted p-value threshold was 0.05.

We conducted a network analysis utilizing the Search Tool for the Retrieval of Interacting Genes (STRING, https://string-db.org/) database to investigate PPI networks within the DEGs. The resulting network, which included clusters recognized by maximal neighborhood component (MNC) and molecular complex detection (MCODE), was shown with Cytoscape (Version 3.7.1; Institute for Systems Biology, Seattle, WA, USA), a platform for network visualization that is open-source. This allowed for a comprehensive examination of the interactions among proteins. The criterion for statistical significance was fixed at p 0.05.

The genes in the MCODE and MNC modules were cross-analyzed to acquire overlapping genes using the bioinformatics platform (https://bioinformatics.psb.ugent.be/webtools/Venn/). To evaluate the expression levels of overlapping genes, we used R for data processing and boxplot visualization and examined their expression in the case and regular groups of the GSE58294 dataset.

Mouse microglia (BV2) were obtained from the National Collection of Authenticated Cell Cultures (Shanghai, China) and maintained in Dulbecco’s Modified Eagle Medium (DMEM; Gibco; Thermo Fisher Scientific, Inc.; Waltham, MA, USA; cat. no. 11965) supplemented with 1% penicillin–streptomycin (Gibco; Thermo Fisher Scientific, Inc.; Waltham, MA, USA; cat. no. 15140122) and 10% fetal bovine serum (FBS; Gibco; Thermo Fisher Scientific, Inc.; Waltham, MA, USA; cat. no. 16000044). The temperature of the cell cultures was kept at 37 °C in a humidified environment with 5% CO₂.

OGD/R is a popular in vitro experimental method to simulate ischemia-reperfusion damage and investigate associated cellular protection mechanisms. The medium was replaced with D-glucose-free DMEM (Corning; Riverfront Plaza, Corning, NY, USA; cat. No. 17-207-CV) and incubated at 37 °C in a hypoxic incubator (94% nitrogen and 5% carbon dioxide) for 2 h to simulate OGD damage. The cells were transferred to a standard glucose-containing DMEM (Gibco; Thermo Fisher Scientific, Inc.; Waltham, MA, USA; cat. no. 11965) medium in a regular incubator (reoxygenation). In this investigation, BV2 cells were given OGD/R conditions for 12, 24, and 48 hours to model ischemic injury. To induce an inflammatory response and promote M1 polarization, BV2 cells were treated with IFN-$\gamma$ (MeilunBio; Dalian, Liaoning, China; cat. no. MB5954; 20 ng/mL) and LPS (Beyotime Biotechnology; Haimen, Jiangsu, China; cat. no. ST1470; 100 ng/mL) for 24 hours. Additionally, rapamycin (PAP; Beyotime Biotechnology; Haimen, Jiangsu, China; cat. no. S1842), an allosteric mTOR inhibitor, was administered at a concentration of 50 nM, and BV2 cells were treated for 24 hours to investigate its effects on cellular signaling pathways.

For transient transfection, a density of 2 $\times$ 10⁵ cells per well was applied to seed BV2 cells in 24-well plates. Overexpression of FCGR1A in BV2 cells was performed by transfecting the cells with a plasmid encoding the FCGR1A gene. According to the manufacturer’s protocol, transfections utilized Lipofectamine 3000 (Invitrogen; Thermo Fisher Scientific, Inc.; Carlsbad, CA, USA; cat. no. L3000). To knock down FCGR1A expression, BV2 cells were transfected with specific small interfering RNA (siRNA) targeting FCGR1A. After transfection, cells were cultured for 48 hours to allow for efficient overexpression or knockdown of FCGR1A. The sequence of si-FCGR1A is CGUUCAGAUCUCCACGCCUAGUUAU (sense Sequence); AUAACUAGGCGUGGAGAUCUGAACG (antisense Sequence). The sequence for small interfering RNA negative control (si-NC) is CGUUAGACCUCCGCAGAUCUCUUAU (sense Sequence); AUAACUAGGCGUGGAGAUCUGAACG (antisense Sequence).

The total RNA of BV2 cells was extracted using the TRIzol reagent (Tiangen; Xuhui District, Shanghai, China; Cat. no. 4992730) according to the manufacturer’s instructions. To synthesize cDNA, we utilized a PrimeScript RT kit (Takara Biotechnology Co., Ltd.; Changping District, Beijing, China; cat. no. RR037). SYBR Green PCR Master Mix (Vazyme Biotech Co., Ltd.; Nanjing Economic and Technological Development Zone, Nanjing, China; cat. no. A0012) was applied for qRT-PCR via the StepOnePlus Real-Time PCR System (Applied Biosystems; Thermo Fisher Scientific, Inc.; Waltham, MA, USA). The levels of gene expression were measured and adjusted for GAPDH. The 2^{-Δ⁢Δ⁢CT} method was utilized to compute each target expression level. A primer sequence was set in Table 1.

Protease and phosphatase inhibitors (CoWin Biosciences; Taizhou, Jiangsu, China; cat. no. CW2200S) were added to radio-immunoprecipitation assay (RIPA) lysis buffer (Solarbio; Tongzhou Dist, Beijing, China; cat. no. R0010) to obtain protein lysates from BV2 cells. The bicinchoninic acid (BCA) Protein Assay Kit (Beyotime Biotechnology; Haimen, Jiangsu, China; cat. no. P0009) was applied to measure the protein concentration. Polyvinylidene fluoride membranes (PVDF; Beyotime Biotechnology; Haimen, Jiangsu, China; cat. no. FFP22) were utilized to receive equal quantities of protein that had been separated with 10% Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). Membranes were blocked with 5% skim milk and then incubated with primary antibodies against FCGR1A (cat. no. ab140779), interleukin (IL)-6 (cat. no. ab233706), induction of brown adipocytes 1 (Iba1) (cat. no. ab178847), FC gamma receptor IIIa (CD16) (cat. no. ab246222), inducible nitric oxide synthase (iNOS) (cat. no. ab178945), CD206 (cat. no. ab64693), Arginase-1 (Arg-1) (cat. no. ab133543), p-AMPK (cat. no. ab133448), AMPK (cat. no. ab32047), p-mTOR (cat. no. ab109268), and mTOR (cat. no. ab32028) (all from Abcam, Cambridge, MA, USA). Except for FCGR1A, which was diluted at a ratio of 1:4000, all other antibodies were diluted at a ratio of 1:1000. Membranes were cleaned and then treated with the relevant secondary antibodies, including goat anti-rabbit IgG-HRP (cat. no. ab6721; dilution 1:5000) and goat anti-mouse IgG-HRP (cat. no. ab6789; dilution 1:5000) (all from Abcam, Cambridge, MA, USA). GAPDH (Kangcheng; Caohejing Hi-Tech Development Zone, Shanghai, China; cat. no. KC-5G4; 1:5000) was an internal reference. A ChemiDoc imaging system (Bio-Rad, Shanghai, China; cat. no. 12003153) was employed to obtain the images of the protein bands, which were viewed using an enhanced chemiluminescence (ECL) kit (Tiangen; Xuhui District, Shanghai, China; cat. no. ZN1926).

Samples of cell culture supernatant were appropriately diluted and put into the wells of an ELISA plate that had been coated beforehand with tumor necrosis factor-alpha (TNF-$\alpha$) (Invitrogen; Thermo Fisher Scientific, Inc.; Carlsbad, CA, USA; cat. no. BMS607-3), IL-1$\beta$ (Invitrogen; Thermo Fisher Scientific, Inc.; Carlsbad, CA, USA; cat. no. BMS6002-2), IL-10 (Invitrogen; Thermo Fisher Scientific, Inc.; Carlsbad, CA, USA; cat. no. BMS614), and IL-6 (Invitrogen; Thermo Fisher Scientific, Inc.; Carlsbad, CA, USA; cat. no. BMS603-2)-specific antibodies. Following incubation, plates were washed to remove unbound material. Enzyme-linked secondary antibodies were added to each well, after which a chromogenic substrate was added. The response was terminated using an appropriate stop solution, and absorbance was detected at the specific wavelength recommended for the substrate utilizing a microplate reader. By comparing the absorbance readings to a standard curve created from standards of known quantities, the amounts of TNF-$\alpha$, IL-1$\beta$, IL-10, and IL-6 were determined.

Cells or tissue sections were fixed with 4% paraformaldehyde (Beyotime Biotechnology; Haimen, Jiangsu, China; cat. no. PFA; P0099) at room temperature for 15 minutes. Following fixation, samples were permeabilized with 0.2% Triton X-100 (Bioss; Tongzhou District, Beijing, China; cat. no. C03-02002) for 10 minutes. To reduce non-specific binding, samples were stopped utilizing 5% BSA (Solarbio; Tongzhou Dist, Beijing, China; cat. no. SW3015) in PBS for 1 hour. The samples were treated with primary antibodies diluted in 1% BSA in PBS and incubated at 4 °C for a whole night. The primary antibodies used were Iba1 (cat. no. ab178847, dilution 1:100), FCGR1A (cat. no. ab140779, dilution 1:100), CD16/32 (cat. no. ab25235, dilution 1:200), and CD206 (cat. no. ab64693, dilution 1:100) (all from Abcam, Cambridge, MA, USA). After washing with PBS, samples were incubated with fluorescent dye-conjugated secondary antibodies, including goat anti-rabbit IgG Alexa Fluor® 488 (cat. no. ab150077, dilution 1:500) and goat anti-mouse IgG Alexa Fluor® 594 (cat. no. ab150116, dilution 1:500) (all from Abcam, Cambridge, MA, USA) at room temperature for 1 hour. DAPI (Invitrogen; Thermo Fisher Scientific, Inc.; Carlsbad, CA, USA; cat. no. D1306) was applied as a five-minute counterstain on the nuclei. Finally, samples were elevated using Fluoromount-G (Yeasen, Shanghai, China; cat. no. 36307ES) and pictured using a confocal microscope (FV1000, Olympus, Beijing, China), magnification 20$\times$. Images were captured and analyzed with ZEN software (Version 3.1, Zeiss, Oberkochen, Germany).

R software was employed to conduct statistical analysis. Data from experiments carried out thrice are displayed as mean $\pm$ standard deviation (SD) [17]. An independent t-test was employed to compare the two groups. Tukey’s posthoc test was applied to determine specific group differences in comparisons involving several groups, after which a one-way ANOVA was conducted. A p-value of less than 0.05 was statistically significant.

In this study, we followed strict standard operating procedures for cell culture and assays to ensure the quality and authenticity of the cell lines used. The cells we used, BV2, were obtained from the National Center for the Preservation of Certified Cell Cultures. To further validate these cell lines’ authenticity and rule out the possibility of mycoplasma contamination, we performed mycoplasma testing on all cell lines. The assay results showed no mycoplasma contamination was detected in any of the cell lines tested, and all results were negative (Supplementary Fig. 1). While STR profiling was not independently performed in our lab, STR reference data are available from the supplier (https://www.cellbank.org.cn/search-detail.php?id=899), and the cell line has not been reported to be misidentified or contaminated. We are committed to complying with scientific and ethical standards for all cell lines used in our research, and we will continue to monitor and maintain the integrity of our cell lines.

A total of 197 downregulated and 130 upregulated DEGs were found in the GSE58294 dataset, comprising 23 control and 69 cardioembolic stroke samples (Fig. 1A). Through the STRING database, PPI network analysis of these DEGs revealed a network with 164 nodes and 302 edges. The MCODE algorithm identified a subnetwork containing 12 nodes and 24 edges, while the MNC algorithm identified a subnetwork with 10 nodes and 22 edges (Fig. 1B,C). Five overlapping genes (catenin beta 1 (CTNNB1), SRY-box transcription factor 9 (SOX9), matrix metallopeptidase 9 (MMP9), CD19 molecule (CD19), and FCGR1A) were identified using the bioinformatics platform from the MCODE and MNC subnetworks (Fig. 1D). Expression analysis indicated that CTNNB1, MMP9, and FCGR1A were upregulated in the stroke samples, whereas SOX9 and CD19 were upregulated in the non-stroke control samples within the GSE58294 dataset (Fig. 1E and Supplementary Table 1). FCGR1A plays a role in the immune system, helping to clear damaged tissue and control inflammation, which is essential for stroke recovery. FCGR1A may work by affecting the activity and movement of immune cells, affecting inflammation and repair processes after stroke. The increase in FCGR1A in stroke patient samples in the GSE58294 dataset may indicate its role in the early stages of stroke, which may be in response to ischemic injury. FCGR1A was bioinformatically associated with ischemic stroke in a previous study [14]. Still, its detailed functional role and mechanism in stroke have not been fully explored and deserve further study, so we chose it as a hub gene.

Fig. 1.

Identification of DEGs and selection of hub genes in the GSE58294 dataset. (A) Volcano plot of DEG screening results in the GSE58294 dataset. Red represents upregulated DEGs, black represents downregulated DEGs, and gray represents genes with insignificant expression. (B,C) PPI network analysis of DEGs, including MCODE (B) and MNC (C) algorithms. The MCODE algorithm contains 12 nodes and 24 edges, and the MNC algorithm includes 10 and 22 edges. Nodes represent proteins or protein domains, and edges represent interactions between these proteins. (D) Venn diagram of 5 overlapping genes in MCODE and MNC, including CTNNB1, SOX9, MMP9, CD19, and FCGR1A. (E) Analysis of overlapping gene expression in non-stroke control and stroke samples of the GSE58294 dataset. Red represents the control sample, and black represents the case sample. DEGs, differentially expressed genes; PPI, protein–protein interaction; MCODE, molecular complex detection; MNC, maximal neighborhood component; CTNNB1, catenin beta 1; SOX9, SRY-box transcription factor 9; MMP9, matrix metallopeptidase 9. ****p 0.0001.

WB analysis was conducted to evaluate the protein levels of FCGR1A in BV2 cells exposed to 12, 24, and 48 hours of OGD/R. The results demonstrated a time-dependent increase in FCGR1A protein expression, with higher levels observed at longer treatment durations (Fig. 2A,B). In addition, we used immunofluorescence staining to detect the expression of FCGR1A and Iba1, which are specific markers for microglia and macrophages and whose expression plays a role in microglia activation and polarization. Immunofluorescence results showed enhanced fluorescence signals of FCGR1A and Iba1 in BV2 cells after 24 hours of reoxygenation, indicating increased FCGR1A activation (Fig. 2C). These observations suggest that FCGR1A is activated in response to OGD/R-induced injury. Furthermore, the protein levels of IL-6 in BV2 cells increased progressively with OGD/R treatment durations of 0, 12, 24, and 48 hours, as shown by WB analysis (Fig. 2D,E).

Fig. 2.

Time-dependent upregulation of FCGR1A and IL-6 in BV2 cells after OGD/R treatment. (A,B) WB detection of FCGR1A protein level in BV2 cells after OGD/R treatment for 12 h, 24 h, and 48 h. (C) Iba1 (green) and FCGR1A (red) protein expressions in BV2 cells were detected by immunofluorescence after 24 h of OGD/R treatment. (D,E) WB detection of inflammatory factor IL-6 protein level in BV2 cells after OGD/R treatment (12, 24, 48 h). WB, Western blot; OGD/R, oxygen–glucose deprivation/reoxygenation; Iba1, induction of brown adipocytes 1. *p 0.05. Original magnification: 20$\times$. Scale, 50 µm. Statistical plots of WB experiments represent the SD $\pm$ mean of a single occasion, and immunofluorescence experiments were performed with at least three independent replicates.

The effectiveness of FCGR1A overexpression in BV2 cells was confirmed by qRT-PCR and WB methods (Fig. 3A–C). The levels of pro-inflammatory cytokines in BV2 cells were calculated using the ELISA method after treatment with LPS+IFN-$\gamma$ or FCGR1A overexpression. The outcomes demonstrated that the expression of these cytokines enhanced significantly after treatment (Fig. 3D–F), indicating that FCGR1A contributes to the activation of inflammatory response in BV2 cells. In addition, qRT-PCR analysis demonstrated that both LPS+IFN-$\gamma$ stimulation and FCGR1A overexpression significantly increased M1 polarization markers CD32, CD16, and iNOS (Fig. 3G–I). In contrast, the M2 markers Arg-1, IL-10, and CD206 levels under the same conditions were not significantly affected (Fig. 3J–L). These findings suggest that FCGR1A promotes inflammatory responses and enhances M1 polarization in BV2 cells.

Fig. 3.

FCGR1A overexpression induces inflammatory responses and promotes M1 polarization in BV2 cells. (A–C) qRT-PCR (A) and WB (B,C) were used to detect the efficiency of FCGR1A overexpression in BV2 cells. *p 0.05. (D–F) ELISA was used to detect the concentration changes of inflammatory factors IL-1$\beta$ (D), IL-6 (E), and TNF-$\alpha$ (F) in BV2 cells after LPS+IFN-$\gamma$ or over-FCGR1A. *p 0.05 vs. control. #p 0.05 vs. vector. (G–L) qRT-PCR was used to detect the expression levels of M1 polarization markers (CD16 (G), CD32 (H), and iNOS (I)) and M2 polarization markers (CD206 (J), Arg-1 (K), and IL-10 (L)) in BV2 cells after LPS+IFN-$\gamma$ or over-FCGR1A treatment. *p 0.05 vs. control. #p 0.05 vs. vector. qRT-PCR stands for quantitative real-time polymerase chain reaction; WB stands for Western blot; and ELISA stands for enzyme-linked immunosorbent assay. Statistical plots for WB experiments represent the SD $\pm$ mean of one trial, and statistical plots for qPCR and ELISA experiments represent the SD $\pm$ mean of at least three independent replicates. ELISA, enzyme-linked immunosorbent assay; LPS, lipopolysaccharide; IFN-$\gamma$, interferon-gamma.

The knockdown efficiency of FCGR1A in BV2 cells was verified using qRT-PCR and WB analysis (Fig. 4A–C). Subsequent qRT-PCR analysis under OGD/R treatment conditions showed that silencing FCGR1A significantly reduced the mRNA expression levels of IL-1$\beta$, IL-1$\alpha$, TNF-$\alpha$, and IL-6 (Fig. 4D–G). Conversely, the mRNA expression of transforming growth factor $\beta$ (TGF-$\beta$) and IL-10 were markedly upregulated after FCGR1A knockdown (Fig. 4H,I). These results indicate that the knockdown of FCGR1A reduces the inflammatory reactions in BV2 cells and encourages the expression of anti-inflammatory cytokines under OGD/R-induced stress conditions.

Fig. 4.

Knockdown of FCGR1A alleviates OGD/R-induced inflammatory response in BV2 cells. (A–C) qRT-PCR (A) and WB (B,C) were used to detect the knockdown efficiency of FCGR1A in BV2 cells under OGD/R conditions. (D–I) qRT-PCR was used to detect the changes in the mRNA levels of pro-inflammatory factors IL-1$\alpha$ (D), IL-1$\beta$ (E), IL-6 (F), and TNF-$\alpha$ (G) and anti-inflammatory factors IL-10 (H) and TGF-$\beta$ (I) in BV2 cells after 24 h of OGD/R treatment. qRT-PCR, quantitative real-time polymerase chain reaction; WB, Western blot; OGD/R, oxygen–glucose deprivation/reoxygenation; si-NC, small interfering RNA negative control. *p 0.05. Statistical plots of WB experiments represent the SD $\pm$ mean of a single run, and statistical plots of qPCR experiments were performed with the SD $\pm$ mean of at least three independent replicates.

The expression of M1 markers (CD16 and CD32) and M2 markers (CD206) in BV2 cells following OGD/R therapy and FCGR1A knockdown was assessed by immunofluorescence labeling. CD16/32 is usually associated with M1-type microglia, whereas CD206 is with M2-type microglia. Differences in the expression of Iba1, CD16/32, and CD206 reveal the polarization characteristics of microglia in different states. The results showed that the number of Iba1-positive microglia/macrophages expressing M1 markers CD16 and CD32 was significantly reduced in FCGR1A knockdown cells compared with cells only exposed to OGD/R. Conversely, the number of cells expressing M2 marker CD206 increased, indicating a shift from M1 to M2 phenotype (Fig. 5A). Further qRT-PCR analysis showed that FCGR1A knockdown significantly elevated the levels of M2 polarization markers and reduced the levels of M1 polarization markers compared with the OGD/R group (Fig. 5B–F). ELISA results revealed that IL-10 was elevated and IL-1$\beta$ was markedly decreased after FCGR1A knockdown compared with OGD/R treatment alone (Fig. 5G,H). WB analysis confirmed these findings, and the Iba1, CD16, and iNOS protein levels were considerably decreased, while Arg-1 and CD206 levels were raised in the FCGR1A knockdown group in contrast to the OGD/R group alone (Fig. 5I,J). These results suggest that FCGR1A knockdown promotes the transition of BV2 cells to the anti-inflammatory M2 phenotype, thereby alleviating OGD/R-induced inflammation.

Fig. 5.

FCGR1A knockdown promotes the transition of BV2 cells from M1 to M2 phenotype after OGD/R treatment. (A) Immunofluorescence staining of BV2 cells for Iba1, M1 markers (CD16, CD32), and M2 marker (CD206) after OGD/R and FCGR1A knockdown. (B–F) qRT-PCR detection of the mRNA levels of M1 polarization markers CD16 (B), CD32 (C) and iNOS (D), M2 markers CD206 (E) and Arg-1 (F) in BV2 cells after OGD/R and FCGR1A knockdown. (G,H) ELISA detection of the protein concentrations of M1 polarization inflammatory factor IL-1$\beta$ and M2 polarization inflammatory factor IL-10 in BV2 cells after OGD/R and FCGR1A knockdown. (I,J) WB analysis of Iba1, CD16, iNOS, CD206, and Arg-1 protein levels in BV2 cells after OGD/R and FCGR1A knockdown. qRT-PCR, quantitative real-time polymerase chain reaction; WB, Western blot; ELISA, enzyme-linked immunosorbent assay; OGD/R, oxygen–glucose deprivation/reoxygenation. *p 0.05. **p 0.01. Original magnification: 20$\times$. Scale, 50 µm. Statistical plots of WB experiments represent the SD $\pm$ mean of a single run, statistical plots of qPCR experiments and ELISA experiments were carried out with at least three independent replicates of the SD $\pm$ mean, and immunofluorescence experiments were carried out with at least three independent replicates.

Rapamycin (RAP), a well-known mTOR inhibitor, was applied to BV2 cells subjected to OGD/R. WB analysis revealed that both si-FCGR1A and 50 nM RAP treatment significantly increased p-AMPK protein levels while decreasing p-mTOR protein levels compared to OGD/R treatment alone, with no notable alterations in total AMPK and mTOR protein expression. The combination of si-FCGR1A and RAP further amplified these effects (Fig. 6A,B). qRT-PCR analysis demonstrated that M1 polarization markers expression was significantly reduced, while M2 polarization markers were correspondingly increased following si-FCGR1A or RAP treatment. These changes were even more pronounced when the two interventions were combined (Fig. 6C–G). ELISA results showed that both si-FCGR1A and RAP treatment led to a rise in the anti-inflammatory cytokine and a significant decline in the pro-inflammatory cytokine, with a more substantial effect observed with combined treatment (Fig. 6H,I). WB analysis further confirmed these findings, showing a marked decrease in Iba1 and the M1 marker CD16 and a significant increase in CD206 in cells treated with either si-FCGR1A or RAP, with more pronounced changes following the combined treatment (Fig. 6J,K). These findings suggest that under OGD/R conditions, FCGR1A knockdown and mTOR inhibition synergistically enhance AMPK activation, leading to the promotion of M2 polarization and suppression of M1 polarization in BV2 cells.

Fig. 6.

FCGR1A regulates the AMPK–mTOR signaling pathway to promote the transition of BV2 cells to the M2 phenotype. (A,B) WB detected the levels of proteins related to the AMPK–mTOR signaling pathway in BV2 cells after OGD/R, si-FCGR1A, and RAP (mTOR inhibitor) treatment. (C–G) qRT-PCR detected the mRNA levels of M1 polarization markers CD16 (C), CD32 (D), and iNOS (E) and M2 markers CD206 (F) and Arg-1 (G) in BV2 cells after OGD/R, si-FCGR1A and RAP treatment. (H,I) ELISA detected the protein concentrations of M1 polarization pro-inflammatory factor IL-1$\beta$ (H) and M2 polarization anti-inflammatory factor IL-10 (I) in BV2 cells after OGD/R, si-FCGR1A, and RAP treatment. (J,K) WB analysis of Iba1, CD16, and CD206 protein levels in BV2 cells after OGD/R, si-FCGR1A, and RAP treatment. AMPK, AMP-activated protein kinase; qRT-PCR, quantitative real-time polymerase chain reaction; WB, Western blot; ELISA, enzyme-linked immunosorbent assay; OGD/R, oxygen–glucose deprivation/reoxygenation; RAP, rapamycin. *p 0.05, **p 0.01 vs. OGD/R. #p 0.05 vs. OGD/R+si-FCGR1A. Statistical plots for WB experiments represent the SD $\pm$ mean of one trial, and statistical plots for qPCR and ELISA experiments represent the SD $\pm$ mean of at least three independent replicates.