Integrated transcriptomic and proteomic profiling reveals the key molecular signatures of brain endothelial reperfusion injury

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Here, we performed transcriptomic and proteomic analyses on human cerebral microvascular endothelial cells following oxygen-glucose deprivation (OGD) or OGD plus recovery (OGD/R), to identify molecules and signaling pathways dysregulated by reperfusion. Transcriptomic analysis identified 390 differentially expressed genes (301 upregulated and 89 downregulated) between the OGD/R and OGD groups. Pathway analysis indicated that the tumor necrosis factor (TNF) signaling pathway was the most significantly enriched. Furthermore, these genes were mostly associated with inflammation, including the TNF signaling pathway, TGF-β signaling pathway, cytokine-cytokine receptor interaction, NOD-like receptor signaling pathway, and NF-κB signaling pathway. On the other hand, 201 differentially expressed proteins (96 upregulated and 105 downregulated) were identified by proteomics between the OGD/R and OGD groups and were primarily associated with extracellular matrix destruction and remodeling, impairment of endothelial transport function, and inflammatory responses. Six genes ( DUSP1 , JUNB , NFKBIA , NR4A1 , SERPINE1 , and THBS1 ) were upregulated by OGD/R at both the mRNA and protein levels. The expression of genes related to inflammatory responses and extracellular matrix were further measured in a mouse model of cerebral ischemia/reperfusion in vivo . Overall, our study provides a comprehensive molecular atlas of brain endothelial reperfusion injury and may facilitate the understanding and treatment of reperfusion injury after ischemic stroke. endothelial cell inflammation ischemic stroke multi-omics reperfusion injury Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Ischemic stroke (IS), one of the major diseases that endangers the health and life of elderly individuals, is characterized by high incidence, high mortality rate, and high disability rate. The principal treatment of acute IS is to restore the blood supply to the ischemic brain tissue as soon as possible and reduce damage to the penumbra brain tissue. Thrombolysis and intravascular therapy are the main clinical treatment methods for IS [1, 2]. However, after a period of ischemia, blood reperfusion also causes severe damage to the brain, that is, reperfusion injury [3], although the mechanisms of reperfusion injury are not well understood. Microvascular injury and blood-brain barrier (BBB) disruption are the main pathological causes of reperfusion injury, which are mainly manifested as hemorrhage transformation, brain edema, and no-reflow phenomenon in the infarct area [4–7]. These complications not only limit the time window for reperfusion therapy but also affects the treatment efficacy. The BBB function is determined by the neurovascular unit that is composed of cerebral vascular endothelial cells, pericytes, astrocyte endfeet, and the surrounding basement membrane. BBB is essential for maintaining the homeostasis of brain tissue microenvironment, regulating brain metabolism, and facilitating the normal neuronal function [8]. Endothelial cells (ECs) as the main component of the brain vasculature and BBB are the primary target of reperfusion injury. Therefore, a systematic study of the dysregulated molecules and signaling pathways in brain ECs after cerebral ischemia and reperfusion is fundamental to elucidating the mechanisms of reperfusion injury. In the current study, we performed transcriptomic analysis by RNA-sequencing (RNA-seq) and proteomic analysis by liquid chromatography-tandem mass spectrometry (LC-MS/MS) with human cerebral microvascular endothelial cells (HCMECs) following oxygen-glucose deprivation and recovery (OGD/R). The major findings of the in vitro studies were further validated in a mouse model of cerebral ischemia and reperfusion in vivo . Our data provide a comprehensive molecular atlas of brain EC injury induced by ischemia/reperfusion, which will facilitate understanding reperfusion injury and exploring novel therapeutic approaches by targeting ECs after acute IS. Materials And Methods Cell line and in vitro OGD/R model We purchased an immortalized HCMEC line (HCMEC/D3) from Institut Cochin (Paris, France) and cultured it in Dulbecco’s modified Eagle’s medium (DMEM) containing 10% fetal bovine serum and 1% penicillin. All cultures were maintained in a humidified 5% CO 2 incubator at 37°C and routinely passaged three times after reaching 80–90% confluency. The cells were divided into a normal control (NC) group, an OGD-6 h group, and different oxygen and glucose recovery (OGD/R) time groups (observation time points: 2.5, 3, 4, 5, 7, 14, and 26 h). Endothelial cell activity decreased by > 30% in the OGD-6 h group, which was in line with the requirements of follow-up studies; setting the peak point of cell damage after OGD/R for follow-up studies was conducive to comparing the differences between reperfusion injury and ischemia-hypoxia injury at the molecular level. Three biological replicates were performed for each group of cells, and each biological replicate was performed in three wells. For OGD modeling, the cells were washed three times with phosphate-buffered saline (PBS) to ensure that no residual sugar remained. The cells were then resuspended in DMEM (Solarbio, Beijing, China) and placed in an OGD incubator (99% N 2 ) for 6 h. For OGD/R modeling, the cells were removed from the OGD incubator and placed in a conventional oxygen concentration incubator. The sugar/serum-free culture medium was replaced with complete DMEM, and the samples were taken at the predetermined time point for subsequent experiments. Cell Counting Kit-8 (CCK-8; TransGen Biotech, Beijing, China) was used to measure cell viabilities, according to the manufacturer's instructions. Briefly, cells were seeded in 96-well plates at a density of 5000 cells/well. Ten microliters of CCK-8 solution were added to each well containing 100 µL of medium. The cells were cultured at 37°C for 2 h. Optical density values were measured at 450 nm using Multiskan GO spectrophotometer (Thermo Scientific, MA, USA). RNA-seq RNA extraction and 3′-end sequencing for expression quantification (3′-seq). Total RNA was extracted using the Direct-zol RNA MiniPrep Kit (Zymo Research, Irvine, CA, USA). To quantify mRNA-expression levels, 3′-seq was performed as previously described [9]. Briefly, eukaryotic mRNA was first enriched by Oligo (dT) beads, while prokaryotic mRNA was enriched by removing rRNA using the Ribo-Zero™ Magnetic Kit (Epicentre, San Diego, CA, USA). The mRNA was then fragmented into short fragments and reverse-transcribed into complementary DNA (cDNA) with random primers. Second-strand cDNA was synthesized using DNA polymerase I, RNase H, and dNTPs. Then, the cDNA fragments were purified with a QiaQuick PCR Extraction Kit (Qiagen, Germany), end-repaired, modified by poly (A) addition, and ligated to sequencing adapters (Illumina, Hayward, CA, USA). The ligation products were size-selected by agarose gel electrophoresis, amplified by the polymerase chain reaction (PCR), and sequenced using a Hiseq PE150 System (Illumina). RNA-seq data analysis. The raw RNA-seq data were subjected to quality-control analysis (to filter the data and reduce noise) before conducting bioinformatics analysis. Analysis showed that high-quality reads accounted for > 98.95% of all reads in each sample (Supplementary Table 1). Then, short read alignment tool Bowtie2 (version 2.2.9) was used for mapping reads to the ribosome RNA (rRNA) database. Less than 4.20% of reads in each sample (< 1.29% in the treatment groups) matched with the rRNA database (Supplementary Table 2). The rRNA mapped reads were then removed. The remaining reads were mapped to the Ensembl human genome (GRCh38.p13) using TopHat2 (version 2.1.1) and then assembled using Cufflinks (version 2.2.1). At least 85.15% of the sequences were well aligned in each sample (Supplementary Table 3). Then, the gene expression levels were normalized using the fragments per kilobase of transcripts per million mapped reads (FPKM) method and the edgeR package (version 3.14.0) was utilized to identify the differentially expressed genes (DEGs) across groups. DEGs were defined as those showing a fold-change (FC) in expression of > 2.0 and an adjusted p-value for the false-discovery rate (FDR) of < 0.05. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to analyze gene abundances. DAVID is a bioinformatics resource that is used to explain the functions of numerous genes. A list of DEGs from the above group comparisons was uploaded to DAVID (version 6.7) to identify enriched biological functions, such as Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Proteomics Sample preparation. Fifty micrograms of protein were digested in 8 M urea buffer (8 M urea (U5378, Sigma-Aldrich, St Louis, MO, USA), 100 mM Tris 8.5 (15504020, Thermo Scientific, MA, USA), 10 mM dithiothreitol (D9779, Sigma-Aldrich)) using the filter-aided sample-preparation (FASP) protocol. Briefly, proteins were added to a 30 kDa cut-off filter (MRCF0R030, Millipore, Billerica, MA, USA) and centrifuged at 11,000 rotations/min at 20°C for 15 min. Iodoacetamide (I1149, Sigma-Aldrich) (50 mM) in urea buffer was used to alkylate the proteins at 20°C for 15 min. After a few washes with urea buffer and 50 mM ammonium bicarbonate (ABC; 09830, Sigma-Aldrich) buffer, 100 ng of trypsin (V5280, Promega, Beijing, China) in 50 mM ABC buffer was used to digest proteins in a wet chamber overnight at 37°C. Peptides were extracted using 50 mM ABC buffer and acidified with trifluoroacetic acid (TFA; 1082620100, Millipore). After performing the FASP protocol described above, the peptides were separated into 5 fractions (flow through, pH 11, pH 8, pH 5, pH 2). Each sample was measured by LC-MS/MS using a 4 h gradient. Proteomic analysis. Tryptic peptides were separated over 240 min using an Easy-nLC 1200 ultra-high-performance LC system, using the following gradient: 2–7% buffer B (80% acetonitrile (1000041001, Millipore) in 0.1% TFA) over 11 min, 7–28% buffer B over 200 min, and 28–36% buffer B over 15 min. Subsequently, the peptides were washed for 5 min while increasing buffer B from 36–60% and for 9 min while increasing buffer to 95%. The Easy-nLC 1200 system was connected online to a Fusion Lumos mass spectrometer equipped with a FAMIS Pro interface (Thermo Scientific). Scans were collected in a data-dependent top-speed mode with a dynamic exclusion of 90 s. Raw data were analyzed using MaxQuant software (version 1.6.0.1) by searching against the Human Fasta Database, with label-free quantification and matches between run functions enabled. The output protein list was analyzed and visualized using the DEP package. Middle cerebral artery occlusion/reperfusion (MCAO/R) model Male 8–10-week-old C57BL/6 mice, weighing 20–23 g, were obtained from Beijing Vital River Laboratory Animal Technologies Co., Ltd. Animals were housed in a pathogen-free animal facility with 12-h light–dark cycles. All animal experimental procedures were carried out in accordance with the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health and approved by the Animal Care and Use Committee of the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, which were in compliance with the ARRIVE guidelines 2.0: Updated guidelines for reporting animal research. Animals with similar body weights were randomly allocated to be used for the sham-operation and ischemic-stroke models. For the surgical procedures, anesthesia was induced with 4% isoflurane in an induction chamber; anesthesia was maintained with 2% isoflurane delivered through a face mask (RWD Life Science, Shenzhen, China). A heating pad was used to maintain each mouse’s core temperature at 37 ± 0.5°C throughout the surgical procedure. A modified intraluminal-filament model was used to induce middle cerebral artery occlusion (MCAO). After 60 min of MCAO, reperfusion was established by retracting the filament. To verify the success of MCAO surgery and reperfusion, blood flow in the cortex supplied by the MCA was measured using a laser Doppler flowmetry (PERIMED AB, Sweden). The mice without a decrease in blood flow below 30% of the baseline value after filament insertion were eliminated from the study. The success of reperfusion was determined by an increase in blood flow above 70% of the baseline value after filament retraction. The animals had free access to food and water throughout the reperfusion period. The animals were divided into two different reperfusion groups (6 h and 24 h reperfusion periods; samples were taken at a predetermined time point), a control group (samples taken 1 h after inducing MCAO), and a sham-operation group (n = 5–8 mice per group). Sample sizes were determined based on previous experience. Animals were excluded if no neurological deficits were present after surgery, according to the pre-established exclusion plan. The investigators examining infarct size and data analysis were blind to the grouping of mice. Infarct size analysis The brains were sectioned into 2-mm-thick sections and placed in 1% 2,3,5-triphenyltetra-zolium chloride (TTC; #T8877, Sigma-Aldrich) for 10 min at 37°C. The TTC solution was then replaced with 4% paraformaldehyde, and the sections were incubated at room temperature for 1 h. The sections were photographed with a digital camera, and the infarct sizes were measured using ImageJ software (National Institutes of Health, USA). The contribution of post-ischemic edema to the volumes of sites of injury was corrected as described previously [10]. The infarct size (%) was calculated as ([volume of the left hemisphere – non-infarct volume of the right hemisphere] / volume of the left hemisphere) × 100%. Reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) Total RNA was extracted using the Direct-zol RNA MiniPrep Kit (Zymo Research). The RNA was reverse transcribed using iScript Reverse Transcription Supermix for RT-qPCR according to the manufacturer’s instructions (Vazyme, Nanjing, China). The primer sequences are shown in Supplementary Table 4. RT-qPCR was performed in a StepOnePlus Real-Time PCR System using Power SYBR Green (Roche, Switzerland). Relative RNA-expression levels were calculated using the comparative Ct method and normalized to actin mRNA expression. Data are expressed relative to a calibrator using the 2 −ΔΔCt method. Immunofluorescence staining Frozen sections (10 µm thick) were allowed to dry on adhesion microscope slides (Citotest Scientific Co., Ltd., Haimen, China) at room temperature and then rehydrated in PBS. The sections were blocked in 10% normal goat serum (Thermo Fisher) in PBS + 0.2% Triton X-100 for 1 h at room temperature. The samples were incubated at 4°C with the following primary antibodies in PBS + 5% goat serum + 0.2% Triton X-100: hamster anti-mouse CD31 (1:100, #MAB1398Z, Millipore, Billerica, MA, USA), donkey anti-mouse IgG (1:200, #715-545-150, Jackson ImmunoResearch, West Grove, PA, USA), mouse anti-TNF-α (1:100, #sc-52746, Santa Cruz Biotechnology, Dallas, TX), rabbit anti-IL-6 (1:100, #K009385P, Solarbio, Beijing, China), rabbit anti-CXCL3 (1:100, #orb13448, Biorbyt, Cambridge, United Kingdom), rabbit anti-THBS1 (1:100, #18304-1-AP, Proteintech, Wuhan, China). Excess antibody was removed by rinsing three times with PBS (7 min/wash step). Samples were then incubated at room temperature for 1 h with the following secondary fluorescently labeled antibodies from Jackson ImmunoResearch: Cy3 goat anti-hamster IgG (1:500, #127-165-099), Alexa Fluor 488 donkey anti-mouse IgG (1:500, #715-545-150), and Alexa Fluor 488 donkey anti-rabbit IgG (1:500, #711-545-152), diluted in PBS + 10% goat serum + 0.2% Triton X-100. Excess antibody was removed by rinsing three times with PBS (5 min/wash step). Slides were mounted in antifade mounting medium with DAPI (Solarbio) and imaged with an Olympus microscope to obtain 20× or 40× images. The immunofluorescence signal area or density was quantified using ImageJ software and normalized according to the vessel area (CD31-positive area) in three random ischemic areas per mouse. Statistical analysis Statistical analysis was performed in Graphpad Prism software (ver. 7). Error bars represent the mean ± standard deviation, and the number (n) of the samples employed is indicated in the legends. Statistical differences among multiple groups were compared using one-way analysis of variance (ANOVA), followed by Tukey’s multiple-comparisons test. Statistical significance was defined as p < 0.05. The RNA-seq data were assessed by a bioinformatics team at Sagene Biotech Co., Ltd. (Guangzhou, China), and proteomic analysis was performed by a bioinformatics team at LumingBio Company (Shanghai, China). Results Transcriptomic analysis of HCMECs subjected to OGD/R The HCMEC/D3 cells were divided into NC, OGD-6 h, and OGR groups and subjected to corresponding treatments. We observed that cells showed the most critical injury at 3 h reperfusion following 6 h of OGD, as determined by assessing cell viabilities in CCK-8 tests (Supplementary Fig. 1). Therefore, we chose the 3 h-OGD/R as a representative time point for reperfusion injury in vitro . We used FDR of 2.0 as thresholds for assigning significant differences. The results showed that ECs exhibited different transcriptomic characteristics during OGD and OGD/R. Compared to the NC group, the OGD group had 2478 DEGs with 761 upregulated genes and 1717 downregulated genes. Compared to the OGD group, the OGD/R group had 390 DEGs with 301 upregulated and 89 downregulated genes (Fig. 1 a and 1 b; Supplementary Data 1). KEGG analysis showed that 277, 268, and 177 signaling pathways were associated with DEGs when comparing the OGD and NC groups, the OGD/R and NC groups, and the OGD/R and OGD groups, respectively; among them, 13, 12, and 23 signaling pathways were significantly enriched (Q-value ≤ 0.05; Fig. 1 c; Supplementary Data 2). The top five pathway terms associated with DEGs between the OGD/R and OGD groups were the TNF signaling pathway, TGF-β signaling pathway, cytokine-cytokine receptor interaction, NOD-like receptor signaling pathway, and Salmonella infection (Table 1 ). This suggests that dysregulation of these signaling pathways (especially upregulation of the TNF signaling pathway, evidenced by upregulation of TNF , IL-6 , LIF , CXCL1 , CXCL2 , CXCL3 , NFKBIA , TNFAIP3 , SOCS3 , MAP2K3 , and other genes; Table 1 and Supplementary Table 5) may play important roles in endothelial reperfusion injury. Furthermore, significantly enriched signaling pathways between the OGD/R and OGD groups were mostly associated with the inflammatory response, including the TNF signaling pathway, NOD-like receptor signaling pathway, JAK-STAT signaling pathway, MAPK signaling pathway, and NF-κB signaling pathway (Table 1 ), suggesting that endothelial cell-mediated inflammatory responses are the most prominent mechanism underlying reperfusion injury at the mRNA level. Compared to the NC group, the OGD/R group and the OGD group demonstrated similarly altered genes and pathways, such as inhibited mitochondrial oxidative phosphorylation (Fig. 1 c), suggesting common molecular mechanisms leading to ischemia and reperfusion injury. Table 1 Top ten pathways associated with DEGs in HCMEC/D3 cells in the OGD/R group, as compared with those in the OGD group Pathway Pathway ID Overlap p value Q value Associated genes TNF signaling pathway ko04668 19/142 0.000000 0.000000 (Up) TNF, IL-6, LIF, CXCL1, CXCL2, CXCL3, CCL20, JUNB, JUN, FOS, EDN1, NFKBIA, TNFAIP3, SOCS3, PTGS2, CSF2, MAP2K3, TCONS_00118147, TCONS_00266386 TGF-beta signaling pathway ko04350 12/99 0.000000 0.000005 (Up) THBS1, FST, NOG, TNF, GDF6, MYC, INHBA, ID1, ID2, ID3, ID4 (Down) INHBE Cytokine-cytokine receptor interaction ko04060 19/334 0.000001 0.000086 (Up) IL-6, IL-8, IL-11, IL-24, CXCL1, CXCL2, CXCL3, CCL20, CXCR5, CLCF1, TNF, LIF, CSF2, GDF6, INHBA, TNFSF15, PDFGRB (Down) KIT, INHBE NOD-like receptor signaling pathway ko04621 9/76 0.000003 0.000141 (Up) NFKBIA, TNF, IL-6, IL-8, CXCL1, CXCL2, TNFAIP3, PYCARD, TCONS_00266386 Salmonella infection ko05132 10/107 0.000008 0.000277 (Up) IL-6, IL-8, CXCL1, CXCL2, CXCL3, FOS, JUN, CSF2, PYCARD, DYNC2H1 Legionellosis ko05134 9/92 0.000016 0.000461 (Up) HSPA6, IL-6, IL-8, CXCL1, CXCL2, CXCL3, NFKBIA, TNF, PYCARD Jak-STAT signaling pathway ko04630 12/185 0.000040 0.001022 (Up) SPRY2, SPRY4, SOCS1, IL-6, IL-11, IL-24, LIF, MYC, SOCS3, CSF2, CLCF1 (Down) SPRY1 Signaling pathways regulating pluripotency of stem cells ko04550 11/163 0.000060 0.001325 (Up) ID1, ID2, ID3, ID4, LIF, MYC, TBX3, INHBA (Down) OTX1, INHBE, POU5F1 MAPK signaling pathway ko04010 15/318 0.000173 0.003396 (Up) HSPA6, PDGFRB, BDNF, MYC, TNF, DUSP1, DUSP2, DUSP4, DUSP5, MAP2K3, JUN, FOS, NR4A1, TCONS_00118147, TCONS_00266386 Osteoclast differentiation ko04380 11/210 0.000554 0.009800 (Up) SOCS1, SOCS3, NFKBIA, TNF, JUN, JUNB, FOS, FOSB, FOSL1, TCONS_00118147, TCONS_00266386 DEG, differentially expressed gene; HCMEC/D3, a type of human cerebral microvascular endothelial cells; OGD/R, oxygen-glucose deprivation and recovery; TNF, tumor necrosis factor; TGF, transforming growth factor; STAT, signal transducers and activators of transcription; MAPK, mitogen-activated protein kinase GO analysis showed that for the DEGs between the OGD/R group and the OGD group, the top-three biological processes were cellular processes, single-organism processes, and response to stimuli; the top-three terms related to cellular components were cell, cell part, and organelle compartments, and the top-three terms related to molecular functions were binding, catalytic activity, and nucleic acid-binding transcription factor activity (Fig. 1 d). Proteomic analysis of HCMECs subjected to OGD/R By using a threshold of a 1.5-fold change and a p-value of < 0.05, 201 differentially expressed proteins (DEPs) between the OGD/R and OGD groups were identified, including 96 upregulated and 105 downregulated proteins (Fig. 2 a and 2 b); Supplementary Data 3). KEGG enrichment analysis showed that the top-five significantly enriched pathways between the OGD/R and OGD groups were Glycosaminoglycan biosynthesis-chondroitin sulfate/dermatan sulfate, extracellular matrix (ECM)-receptor interaction, ATP-binding cassette (ABC) transporters, Staphylococcus aureus infection, and Vitamin digestion and absorption (Fig. 2 c; Table 2 ; Supplementary Data 4), suggesting their potential roles in reperfusion injury. Further analysis of the top ten enriched pathways demonstrated that pathways related to ECM destruction (evidenced by downregulation of COL4A1, LAMA4, ITGA1, ITGA7, ITGB3, and HSPG2) and remodeling (evidenced by upregulation of PIGT and THBS1) were the most significantly enriched (Table 2 ). Our results show that destruction and remodeling of the extracellular matrix is an important feature of endothelial reperfusion injury at the protein level. In addition, reperfusion injury also resulted in impaired endothelial transport functions, shown by downregulation of ABC transporters (ABCD1, ABCA3, ABCB7) and multivitamin transporter SLC5A6 (Table 2 ). Consistent to the results of RNA-seq, proteomic analysis also revealed an inflammatory response in brain ECs after reperfusion injury, demonstrated by upregulation of proteins involved in Staphylococcus aureus infection and Cytosolic DNA-sensing pathway (Table 2 ). Table 2 Top ten pathways associated with DEPs in HCMEC/D3 cells in the OGD/R group, as compared with the OGD group Pathway Pathway ID Adjusted p value Enrichment score Associated proteins Glycosaminoglycan biosynthesis-chondroitin sulfate / dermatan sulfate hsa00532 0 29.677419 (Up) PIGT ECM-receptor interaction hsa04512 0.000187 7.419355 (Up) THBS1 (Down) COL4A1, LAMA4, ITGA1, ITGA7, ITGB3, HSPG2 ABC transporters hsa02010 0.004323 11.129032 (Down) ABCD1, ABCA3, ABCB7 Staphylococcus aureus infection hsa05150 0.006148 14.838710 (Up) FGG (Down) KRT10 Vitamin digestion and absorption hsa04977 0.010892 11.870968 (Up) APOB (Down) SLC5A6 Cytosolic DNA-sensing pathway hsa04623 0.010892 4.946237 (Up) POLR3H, NFKBIA, NFKBIB (Down) CGAS, POLR2L GPI-anchor biosynthesis hsa00563 0.027287 14.838710 (Up) PIGT PI3K-Akt signaling pathway hsa04151 0.031378 2.558398 (Up) THBS1, STK11, NR4A1 (Down) COL4A1, EIF4EBP1, ITGA7, ITGB3, PPP2R2D, LAMA4, ITGA1 Cholesterol metabolism hsa04979 0.056043 4.685908 (Up) APOB (Down) PCSK9, SORT1 Ubiquinone and other terpenoid-quinone biosynthesis hsa00130 0.056043 9.892473 (Up) NQO1 DEP, differentially expressed protein; HCMEC/D3, a type of human cerebral microvascular endothelial cells; OGD/R, oxygen-glucose deprivation and recovery; ECM, extracellular matrix; ABC, ATP-binding cassette; GPI, glycosylphosphatidylinositol; PI3K, phosphatidylinositol 3'-kinase We annotated 151 DEPs to 1010 GO function entries. The most enriched biological processes were cellular processes, biological regulation, metabolic processes, and regulation of biological processes; the most enriched cellular component GO terms were cell, cell part, and organelle compartments; the most enriched molecular function GO terms were binding, catalytic activity, enzyme regulator activity, and transporter activity (Fig. 2 d). Integrative analysis of the transcriptomic and proteomic data To investigate correlations between mRNA expression and protein expression levels, we performed a combined transcriptomic and proteomic analysis of data from the OGD/R and OGD groups (Fig. 3 a). We generated a scatter plot of the mRNA expression levels of known genes (X-axis) versus the corresponding protein expression levels (Y-axis) to compare the protein versus mRNA abundances. The correlation value (r = 0.492), calculated using Spearman’s rank correlation coefficient test (Fig. 3 b), suggested little correlation between mRNA expression and protein expression. Integrative analysis showed that six genes ( DUSP1 , JUNB , NFKBIA , NR4A1 , SERPINE1 , and THBS1 ) were differentially expressed at both the mRNA and protein levels, and all of them were upregulated (Fig. 3 a and 3 c; Supplementary Data 5). Measurement of the expression changes of selected genes in a mouse model of MCAO/R In mice subjected to 1 h MCAO without reperfusion, infarction had almost not developed based on TTC staining. Extensive infarction was detected in the cortical and striatum regions in brain tissues at both 6 h (12.0% ± 1.7%, p < 0.0001) and 24 h (28.6% ± 2.4%, p < 0.0001) after reperfusion, especially at 24 h post-reperfusion (Supplementary Fig. 2a and 2b). We next assessed BBB leakage by detecting mouse blood IgG extravasation from vessels in the ischemic and infarct areas. The results showed that blood IgG leakage increased significantly in mice after 24 h of reperfusion (p < 0.0001; Supplementary Fig. 2c and 2d). These results demonstrate that reperfusion following cerebral ischemia led to severe brain infarction and microvascular injury shown by BBB disruption. Differentially expressed TNF signaling pathway members ( TNF , IL-6 , CXCL3 ) and THBS1 found by omics data were selected for further measurement in the mouse MCAO/R model using immunofluorescence analyses. We selected genes in the TNF signaling pathway because it was the most significantly enriched pathway, and selected THBS1 because it was found to be upregulated in both transcriptomic and proteomic data. However, fluorescence colocalization showed that these genes only partially colocalized with CD31; that is, they were not specifically expressed in ECs. Overall, TNF, IL-6, and CXCL3 were significantly upregulated after ischemia for 1 h and remained at high levels after reperfusion, and THBS1 was significantly upregulated after reperfusion compared to ischemia (Supplementary Fig. 3), which were consistent with the omics data. We further quantified mRNA expression changes of all differentially expressed TNF signaling pathway members and THBS1 in whole brain tissues but not only ECs, due to their nonspecific characteristics for ECs, by RT-qPCR. Consistent with the RNA-seq data, RT-qPCR data showed that the mRNA levels of most genes ( TNF , IL-6 , LIF , CXCL2 , CXCL3 , NFKBIA , SOCS3 , MAP2K3 , and THBS1 ) also showed significant upregulations (Fig. 4 and Supplementary Fig. 4). Discussion The mechanisms underlying reperfusion injury are complex and involve many pathological factors, such as inflammatory responses, mitochondrial dysfunction, free radical production, and Ca 2+ overload, etc [11, 12]. Previous studies have shown that anti-inflammatory molecules [13], antioxidants [14, 15], and calcium channel blockers can reduce cerebral reperfusion injury. Targeting mitochondria may also be helpful for preventing and treating reperfusion injury [12]. However, it is worth mentioning that although many neuroprotective agents have shown efficacy in experimental reperfusion injury, clinical studies have failed to demonstrate positive outcomes, and only a few drugs, such as edaravone and butylphthalide [15, 16], are currently in clinical use. The low efficacy of such neuroprotectants may reflect the facts that the mechanisms of reperfusion injury are still not fully clear. As mentioned above, damage to ECs is the main pathological basis of reperfusion injury. Thus, gaining insight into the molecular signatures of brain ECs during reperfusion injury will aid in developing multi-pathway and multi-target intervention strategies against reperfusion injury. In this study, we analyzed the transcriptomic and proteomic changes in HCMECs during reperfusion injury. Pathway analysis of the transcriptomics data showed that the TNF signaling pathway was the most significantly enriched pathway after OGD/R versus OGD, suggesting that dysfunction of the pathway is an important cause of endothelial reperfusion injury. In addition, the top significantly changed signaling pathways were mostly associated with the inflammatory response, including TNF signaling pathway, NOD-like receptor signaling pathway, JAK-STAT signaling pathway, MAPK signaling pathway, and NF-κB signaling pathway. Previous results showed that an excessive inflammatory response contributes to the pathogenesis of cerebral ischemia reperfusion injury [13, 17] and that the inflammatory response is mainly mediated by microglia, astrocytes, and peripheral leukocytes [18]. Our results suggest that endothelial cell-mediated inflammatory responses are also prominent mechanisms promoting reperfusion injury, especially through the TNF signaling pathway. Previous data revealed that signaling pathways that mediate the upregulation of proinflammatory factors and cell death include the TNF, JAK-STAT, MAPK, NF-κB, and TLR signaling pathways. The most extensively studied proinflammatory factors include TNF-a, IL-1, IL-6, and NF-κB [13, 19]. Our RNA-seq results for ECs are generally consistent with previous findings. Studies have indicated that dysfunctional TNF-pathway signaling plays important roles in the occurrence and development of many diseases, such as inflammatory and autoimmune diseases, cancer, and cardiovascular disease [20, 21]. TNF-a is the most important molecule in the TNF signaling pathway and is considered as a major proinflammatory mediator. Interestingly, in pathological and physiological situations, TNF-a serves dual functions [21]. TNF-a has been reported to be both neurotoxic and neuroprotective in nervous system diseases. Previous data generated using a rat model of MCAO/R showed that TNF-a mRNA expression was increased in brain tissue [22]. Anti-TNF-α antibody can reduce cerebral infarction and brain edema volumes [23–25]. In vitro OGR can enhance TNF-α secretion from microglia. Inhibiting TNF-α secretion in a mouse model of MCAO/R reduced leukocyte infiltration, decreased cerebral infarction, and improved the functional prognosis [26]. However, TNF-α may also have a neuroprotective effect in cerebral ischemia/reperfusion, as a previous study showed that pretreatment with a TNF-α inhibitor aggravated reperfusion injury [27]. In summary, the functions and mechanisms of TNF-α are complex and diverse and are not yet fully understood. In addition, inhibiting other proinflammatory genes, such as NF-κB, MAPK, and IL-1, can also reduce cerebral ischemia reperfusion injury in animals [13]. The results of our study further suggest that targeting inflammatory signaling pathway members, especially those in the TNF signaling pathway, may be an important way to protect the BBB and alleviate reperfusion injury. Proteomic analysis showed that pathways related to ECM destruction and remodeling, impairment of endothelial transport function, and inflammatory responses were significantly enriched after OGD/R versus OGD, suggesting that the associated proteins may also play important roles in reperfusion injury. Our results show that destruction and remodeling of the extracellular matrix is an important feature of endothelial reperfusion injury, such as downregulation of collagen IV, laminin, and integrin, and upregulation of PIGT and THBS1. Collagen IV, laminin and integrin proteins represent the main components of the basement membrane, and reperfusion can cause the activation of matrix metalloproteinases and degradation of basement membrane proteins [28]. THBS1 is one of the few genes found to be upregulated in both transcriptomic and proteomic data. THBS1 was identified as an endogenous inhibitor of angiogenesis and regulates endothelial cell adhesion, growth, motility, and survival [29]. A previous study showed that focal cerebral ischemia and subsequent reperfusion led to THBS1 expression at both the mRNA and protein levels [30]. In a permanent mouse model of MCAO, THBS1 knockout mice displayed significant deficits in their abilities to recover motor function [31], and in a mouse model of traumatic brain injury, THBS1 knockout significantly worsened BBB leakage and exhibited significantly worse neurological deficits in terms of motor and cognitive functions [32]. These data suggest that THBS1 may have a neuroprotective effect. However, information is limited regarding the role of THBS1 in cerebral ischemia-reperfusion injury and further studies are needed. Our transcriptomic and proteomic data showed little correlation between the mRNA and protein expression levels. In fact, steady-state mRNA and protein levels have been compared in numerous studies, which also showed poor correlations [33]. Protein expression is influenced by an array of post-transcriptional regulatory mechanisms, the correlation between protein and mRNA levels is generally modest [34, 35]. Furthermore, extremely low-abundance proteins may be undetected when they are below the detection limits of current methods. Nevertheless, integrated transcriptomic and proteomic analyses provide a new paradigm for understanding endothelial reperfusion injury, in that protein analysis portrays the current states and reflects the immediate impact of reperfusion on pre-existing proteins of ECs, and transcriptomic analysis reflects the impact of reperfusion on the transcripts of ECs that will manifest as proteins and modulate cell functionality at subsequent timepoints. Protein expression levels of TNF, IL-6, CXCL3 and THBS1 were further measure in a mouse MCAO/R model using immunofluorescence analyses. However, fluorescence colocalization showed that these genes were not specifically expressed in ECs. Overall, TNF, IL-6, and CXCL3 were significantly upregulated after ischemia for 1 h and remained at high levels after reperfusion, and THBS1 was significantly upregulated after reperfusion compared to ischemia, which were consistent with the omics data, suggesting that these genes are important targets for preventing and treating reperfusion injury. IL-6 is a proinflammatory cytokine and CXCL3 is a chemokine, but their specific roles in cerebral reperfusion injury have not been clarified. RT-qPCR was performed to further quantify mRNA expression changes of all differentially expressed TNF signaling pathway members and THBS1. By comparing the RT-qPCR and RNA-seq data, we found that most genes showed consistent expression changes. However, it must be mentioned that there are limitations in translating results from a human endothelial cell line to a mouse model as we have done in this study. Nevertheless, our findings indicate that targeting these genes and their associated pathways may significantly attenuate endothelial reperfusion injury in addition to attenuating global brain reperfusion injury. Conclusions In this study, we identified the dysregulated genes and pathways in brain ECs during reperfusion, and provided comprehensive molecular mechanism information for further functional studies. Our findings suggest that targeting the inflammatory pathways (especially the TNF signaling pathway) and ECM destruction may be important approaches for attenuating reperfusion injury after ischemic stroke. Abbreviations ABC, ATP-binding cassette; BBB, Blood-brain barrier; DEGs, Differentially expressed genes; DEPs, Differentially expressed proteins; ECs, Endothelial cells; ECM, Extracellular matrix; FC, Fold change; FDR, False-discovery rate; GO, Gene Ontology; HCMECs, Human cerebral microvascular endothelial cells; IS, Ischemic stroke; KEGG, Kyoto Encyclopedia of Genes and Genomes; LC-MS/MS, Liquid chromatography-tandem mass spectrometry; MCAO, Middle cerebral artery occlusion; NC, Normal control; OGD, Oxygen-glucose deprivation; OGD/R, OGD plus recovery; RNA-seq, RNA-sequencing; TNF, Tumor necrosis factor. Declarations Funding This work was supported by the Guangdong Province Basic and Applied Basic Research Grant (2021A1515220105, 2021B1515120089); National Natural Science Foundation of China (32170985); National Key Research and Development Program of China (2021YFA0910000); Science Technology and Innovation Commission of Shenzhen Municipality (JCYJ20210324115800003, SGLH20180625142404672, JCYJ20200109114608075); International collaboration project of Chinese Academy of Sciences (172644KYSB20200045); President Foundation of Nanfang Hospital (2021B021); CAS-Croucher Funding Scheme for Joint Laboratories; and Guangdong Innovation Platform of Translational Research for Cerebrovascular Diseases. Competing interests The authors have no relevant financial or non-financial interests to disclose. Author contribution statement Yabin Ji: Conceptualization, Methodology, Validation, Formal analysis, Writing - review & editing, Project administration, Funding acquisition. Yiman Chen: Methodology, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review & editing, Visualization. Xixi Tan: Investigation. Xiaowen Huang: Investigation. Qiang Gao: Investigation. Yinzhong Ma: Investigation. Min Yu: Visualization. Cheng Fang: Visualization. Yu Wang: Writing - review & editing. Zhu Shi: Writing - review & editing, Supervision. Junlei Chang: Conceptualization, Writing - review & editing, Visualization, Supervision, Project administration, Funding acquisition. Data availability Detailed information for this study can be found in the supplementary materials. Additional data information is available upon reasonable request from the authors. Acknowledgments Not applicable. Ethics approval All animal experimental procedures were carried out in accordance with the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health and approved by the Animal Care and Use Committee of the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences (Approval number SIAT-IRB-170304-YYS-CJL-A0301). Consent to participate Not applicable. Consent to publish Not applicable. References Rabinstein A (2017) Treatment of Acute Ischemic Stroke. CONTINUUM: Lifelong Learning in Neurology 23:62–81. https://doi.org/10.1212/CON.0000000000000420 Berkhemer O, Fransen P, Beumer D et al (2014) A Randomized Trial of Intraarterial Treatment for Acute Ischemic Stroke. New England Journal of Medicine 372. https://doi.org/10.1056/NEJMoa1411587 Catanese L, Tarsia J and Fisher M (2017) Acute Ischemic Stroke Therapy Overview. 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Nature 473:337–342. https://doi.org/10.1038/nature10098 Supplementary Files SupplementaryData1.xlsx SupplementaryData2.xlsx SupplementaryData3.xlsx SupplementaryData4.xlsx SupplementaryData5.xlsx SupplementaryMaterial.doc Cite Share Download PDF Status: Published Journal Publication published 03 Oct, 2023 Read the published version in CNS Neuroscience & Therapeutics → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2216319","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":148247810,"identity":"e7c4dd32-86c9-45de-8bae-ca5d89a2e64b","order_by":0,"name":"Yabin Ji","email":"","orcid":"","institution":"Southern Medical University Nanfang Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yabin","middleName":"","lastName":"Ji","suffix":""},{"id":148247811,"identity":"9fa990ea-0fb0-48da-b130-351632318377","order_by":1,"name":"Yiman Chen","email":"","orcid":"","institution":"Southern Medical University Nanfang 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(a) Volcano plots of DEGs. Red dots represent upregulated genes, and green dots represent downregulated genes. (b) Bar graph demonstrating the number of upregulated and downregulated genes. DEGs were defined as those with an FDR of \u0026lt; 0.05 and an absolute fold-change of \u0026gt; 2.0. (c) KEGG enrichment analysis for DEGs. The colors changing from green to red indicate higher Q-values. (d) GO term-enrichment analysis for DEGs. DEG, differentially expressed gene; FDR, false-discovery rate; HCMEC/D3, a type of human cerebral microvascular endothelial cells; NC, normal control; OGD/R, oxygen-glucose deprivation and recovery\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2216319/v1/4bff7921b26dc67826631dea.png"},{"id":28579497,"identity":"695d21ee-5859-463f-9009-d80d342426fc","added_by":"auto","created_at":"2022-11-02 19:35:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":343080,"visible":true,"origin":"","legend":"\u003cp\u003eComparisons of protein expression profiles of HCMEC/D3 cells subjected to OGD/R. (a) Volcano plots of DEPs. Red dots represent upregulated proteins, and green dots represent downregulated proteins. (b) Bar graph demonstrating the number of upregulated and downregulated proteins. DEPs were defined as those with a p value of \u0026lt; 0.05 and an absolute fold-change of ≥ 1.5. (c) KEGG enrichment analysis for DEPs. (d) GO term-enrichment analysis for DEPs. DEP, differentially expressed protein; HCMEC/D3, a type of human cerebral microvascular endothelial cells; OGD/R, oxygen-glucose deprivation and recovery\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2216319/v1/afe99af2b341ff752fa14cb6.png"},{"id":28579501,"identity":"1d9b024a-735b-4749-9496-b6f912ad4740","added_by":"auto","created_at":"2022-11-02 19:35:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":210893,"visible":true,"origin":"","legend":"\u003cp\u003eIntegrative transcriptomic and proteomic analysis of HCMEC/D3 cells subjected to OGD/R. (a) Quadrant map of DEGs and DEPs. The red dots represent co-upregulated mRNAs and proteins, and the green dots represent co-downregulated mRNAs and proteins. (b) Correlation between mRNA and protein expression levels. (c) An alphabetical list of co-upregulated genes at both the mRNA and protein level. DEG, differentially expressed gene; DEP, differentially expressed protein; HCMEC/D3, a type of human cerebral microvascular endothelial cells; OGD/R, oxygen-glucose deprivation and recovery\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2216319/v1/afd318fc72aa2fb6105ed660.png"},{"id":28579498,"identity":"38a7fb89-2aca-4336-a60f-6de4f7dac75a","added_by":"auto","created_at":"2022-11-02 19:35:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":308402,"visible":true,"origin":"","legend":"\u003cp\u003eConsistent upregulation of TNF signaling pathway members and THBS1 in both HCMEC/D3 cells and a mouse MCAO/R model. The left part of the histograms shows the RNA-seq data of HCMEC/D3 cells (#p \u0026lt; 0.05, n = 3 biological replicates per group), and the right part of the histograms shows the relative mRNA levels in MCAO/R mouse brain tissues determined by RT-qPCR (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, n = 3 mice per group). THBS1, thrombospondin 1; TNF, tumor necrosis factor; HCMEC/D3, a type of human cerebral microvascular endothelial cells; MCAO/R, middle cerebral artery occlusion/reperfusion\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-2216319/v1/e30acc659796c7cfa2ea856f.png"},{"id":54696545,"identity":"4752293f-bfc9-46e3-b755-625141bbdc5a","added_by":"auto","created_at":"2024-04-15 11:27:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1858562,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2216319/v1/55490bc9-9945-4959-8c1f-b2c18865c549.pdf"},{"id":28580148,"identity":"c8e37279-76ad-48ea-bf90-da345bc7d6a5","added_by":"auto","created_at":"2022-11-02 19:43:50","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":948839,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2216319/v1/6a62ffafac940e769d2e378c.xlsx"},{"id":28580149,"identity":"92244155-b40a-4f01-8f28-6aee13d4bcb8","added_by":"auto","created_at":"2022-11-02 19:43:50","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":93062,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2216319/v1/58ae5571f78a2037ae2c50cb.xlsx"},{"id":28579502,"identity":"0667bc9c-53d3-436b-aae6-8ecb79f3b8a4","added_by":"auto","created_at":"2022-11-02 19:35:50","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":44630,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2216319/v1/61831983b05b7ca17aba90e2.xlsx"},{"id":28579504,"identity":"424bd832-8097-45aa-b050-f843166f3383","added_by":"auto","created_at":"2022-11-02 19:35:50","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":25375,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2216319/v1/6b5103919fd94135fa6a48ba.xlsx"},{"id":28579506,"identity":"670a109a-d7c4-491a-981c-36604219a106","added_by":"auto","created_at":"2022-11-02 19:35:50","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":13135,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2216319/v1/cafc6c1b95b2e1ddd72e2f97.xlsx"},{"id":28579507,"identity":"9203d776-46bb-48ba-aa13-5ec004c0464b","added_by":"auto","created_at":"2022-11-02 19:35:50","extension":"doc","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":11490304,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.doc","url":"https://assets-eu.researchsquare.com/files/rs-2216319/v1/25a16f62a2e7292874566bec.doc"}],"financialInterests":"","formattedTitle":"Integrated transcriptomic and proteomic profiling reveals the key molecular signatures of brain endothelial reperfusion injury","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIschemic stroke (IS), one of the major diseases that endangers the health and life of elderly individuals, is characterized by high incidence, high mortality rate, and high disability rate. The principal treatment of acute IS is to restore the blood supply to the ischemic brain tissue as soon as possible and reduce damage to the penumbra brain tissue. Thrombolysis and intravascular therapy are the main clinical treatment methods for IS [1, 2]. However, after a period of ischemia, blood reperfusion also causes severe damage to the brain, that is, reperfusion injury [3], although the mechanisms of reperfusion injury are not well understood. Microvascular injury and blood-brain barrier (BBB) disruption are the main pathological causes of reperfusion injury, which are mainly manifested as hemorrhage transformation, brain edema, and no-reflow phenomenon in the infarct area [4\u0026ndash;7]. These complications not only limit the time window for reperfusion therapy but also affects the treatment efficacy.\u003c/p\u003e \u003cp\u003eThe BBB function is determined by the neurovascular unit that is composed of cerebral vascular endothelial cells, pericytes, astrocyte endfeet, and the surrounding basement membrane. BBB is essential for maintaining the homeostasis of brain tissue microenvironment, regulating brain metabolism, and facilitating the normal neuronal function [8]. Endothelial cells (ECs) as the main component of the brain vasculature and BBB are the primary target of reperfusion injury. Therefore, a systematic study of the dysregulated molecules and signaling pathways in brain ECs after cerebral ischemia and reperfusion is fundamental to elucidating the mechanisms of reperfusion injury.\u003c/p\u003e \u003cp\u003eIn the current study, we performed transcriptomic analysis by RNA-sequencing (RNA-seq) and proteomic analysis by liquid chromatography-tandem mass spectrometry (LC-MS/MS) with human cerebral microvascular endothelial cells (HCMECs) following oxygen-glucose deprivation and recovery (OGD/R). The major findings of the \u003cem\u003ein vitro\u003c/em\u003e studies were further validated in a mouse model of cerebral ischemia and reperfusion \u003cem\u003ein vivo\u003c/em\u003e. Our data provide a comprehensive molecular atlas of brain EC injury induced by ischemia/reperfusion, which will facilitate understanding reperfusion injury and exploring novel therapeutic approaches by targeting ECs after acute IS.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCell line and in vitro OGD/R model\u003c/h2\u003e \u003cp\u003eWe purchased an immortalized HCMEC line (HCMEC/D3) from Institut Cochin (Paris, France) and cultured it in Dulbecco\u0026rsquo;s modified Eagle\u0026rsquo;s medium (DMEM) containing 10% fetal bovine serum and 1% penicillin. All cultures were maintained in a humidified 5% CO\u003csub\u003e2\u003c/sub\u003e incubator at 37\u0026deg;C and routinely passaged three times after reaching 80\u0026ndash;90% confluency. The cells were divided into a normal control (NC) group, an OGD-6 h group, and different oxygen and glucose recovery (OGD/R) time groups (observation time points: 2.5, 3, 4, 5, 7, 14, and 26 h). Endothelial cell activity decreased by \u0026gt;\u0026thinsp;30% in the OGD-6 h group, which was in line with the requirements of follow-up studies; setting the peak point of cell damage after OGD/R for follow-up studies was conducive to comparing the differences between reperfusion injury and ischemia-hypoxia injury at the molecular level. Three biological replicates were performed for each group of cells, and each biological replicate was performed in three wells. For OGD modeling, the cells were washed three times with phosphate-buffered saline (PBS) to ensure that no residual sugar remained. The cells were then resuspended in DMEM (Solarbio, Beijing, China) and placed in an OGD incubator (99% N\u003csub\u003e2\u003c/sub\u003e) for 6 h. For OGD/R modeling, the cells were removed from the OGD incubator and placed in a conventional oxygen concentration incubator. The sugar/serum-free culture medium was replaced with complete DMEM, and the samples were taken at the predetermined time point for subsequent experiments. Cell Counting Kit-8 (CCK-8; TransGen Biotech, Beijing, China) was used to measure cell viabilities, according to the manufacturer's instructions. Briefly, cells were seeded in 96-well plates at a density of 5000 cells/well. Ten microliters of CCK-8 solution were added to each well containing 100 \u0026micro;L of medium. The cells were cultured at 37\u0026deg;C for 2 h. Optical density values were measured at 450 nm using Multiskan GO spectrophotometer (Thermo Scientific, MA, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eRNA-seq\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eRNA extraction and 3\u0026prime;-end sequencing for expression quantification (3\u0026prime;-seq).\u003c/span\u003e Total RNA was extracted using the Direct-zol RNA MiniPrep Kit (Zymo Research, Irvine, CA, USA). To quantify mRNA-expression levels, 3\u0026prime;-seq was performed as previously described [9]. Briefly, eukaryotic mRNA was first enriched by Oligo (dT) beads, while prokaryotic mRNA was enriched by removing rRNA using the Ribo-Zero\u0026trade; Magnetic Kit (Epicentre, San Diego, CA, USA). The mRNA was then fragmented into short fragments and reverse-transcribed into complementary DNA (cDNA) with random primers. Second-strand cDNA was synthesized using DNA polymerase I, RNase H, and dNTPs. Then, the cDNA fragments were purified with a QiaQuick PCR Extraction Kit (Qiagen, Germany), end-repaired, modified by poly (A) addition, and ligated to sequencing adapters (Illumina, Hayward, CA, USA). The ligation products were size-selected by agarose gel electrophoresis, amplified by the polymerase chain reaction (PCR), and sequenced using a Hiseq PE150 System (Illumina).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eRNA-seq data analysis.\u003c/span\u003e The raw RNA-seq data were subjected to quality-control analysis (to filter the data and reduce noise) before conducting bioinformatics analysis. Analysis showed that high-quality reads accounted for \u0026gt;\u0026thinsp;98.95% of all reads in each sample (Supplementary Table\u0026nbsp;1). Then, short read alignment tool Bowtie2 (version 2.2.9) was used for mapping reads to the ribosome RNA (rRNA) database. Less than 4.20% of reads in each sample (\u0026lt;\u0026thinsp;1.29% in the treatment groups) matched with the rRNA database (Supplementary Table\u0026nbsp;2). The rRNA mapped reads were then removed. The remaining reads were mapped to the Ensembl human genome (GRCh38.p13) using TopHat2 (version 2.1.1) and then assembled using Cufflinks (version 2.2.1). At least 85.15% of the sequences were well aligned in each sample (Supplementary Table\u0026nbsp;3). Then, the gene expression levels were normalized using the fragments per kilobase of transcripts per million mapped reads (FPKM) method and the edgeR package (version 3.14.0) was utilized to identify the differentially expressed genes (DEGs) across groups. DEGs were defined as those showing a fold-change (FC) in expression of \u0026gt;\u0026thinsp;2.0 and an adjusted p-value for the false-discovery rate (FDR) of \u0026lt;\u0026thinsp;0.05. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to analyze gene abundances. DAVID is a bioinformatics resource that is used to explain the functions of numerous genes. A list of DEGs from the above group comparisons was uploaded to DAVID (version 6.7) to identify enriched biological functions, such as Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eProteomics\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eSample preparation.\u003c/span\u003e Fifty micrograms of protein were digested in 8 M urea buffer (8 M urea (U5378, Sigma-Aldrich, St Louis, MO, USA), 100 mM Tris 8.5 (15504020, Thermo Scientific, MA, USA), 10 mM dithiothreitol (D9779, Sigma-Aldrich)) using the filter-aided sample-preparation (FASP) protocol. Briefly, proteins were added to a 30 kDa cut-off filter (MRCF0R030, Millipore, Billerica, MA, USA) and centrifuged at 11,000 rotations/min at 20\u0026deg;C for 15 min. Iodoacetamide (I1149, Sigma-Aldrich) (50 mM) in urea buffer was used to alkylate the proteins at 20\u0026deg;C for 15 min. After a few washes with urea buffer and 50 mM ammonium bicarbonate (ABC; 09830, Sigma-Aldrich) buffer, 100 ng of trypsin (V5280, Promega, Beijing, China) in 50 mM ABC buffer was used to digest proteins in a wet chamber overnight at 37\u0026deg;C. Peptides were extracted using 50 mM ABC buffer and acidified with trifluoroacetic acid (TFA; 1082620100, Millipore). After performing the FASP protocol described above, the peptides were separated into 5 fractions (flow through, pH 11, pH 8, pH 5, pH 2). Each sample was measured by LC-MS/MS using a 4 h gradient.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eProteomic analysis.\u003c/span\u003e Tryptic peptides were separated over 240 min using an Easy-nLC 1200 ultra-high-performance LC system, using the following gradient: 2\u0026ndash;7% buffer B (80% acetonitrile (1000041001, Millipore) in 0.1% TFA) over 11 min, 7\u0026ndash;28% buffer B over 200 min, and 28\u0026ndash;36% buffer B over 15 min. Subsequently, the peptides were washed for 5 min while increasing buffer B from 36\u0026ndash;60% and for 9 min while increasing buffer to 95%. The Easy-nLC 1200 system was connected online to a Fusion Lumos mass spectrometer equipped with a FAMIS Pro interface (Thermo Scientific). Scans were collected in a data-dependent top-speed mode with a dynamic exclusion of 90 s. Raw data were analyzed using MaxQuant software (version 1.6.0.1) by searching against the Human Fasta Database, with label-free quantification and matches between run functions enabled. The output protein list was analyzed and visualized using the DEP package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMiddle cerebral artery occlusion/reperfusion (MCAO/R) model\u003c/h2\u003e \u003cp\u003eMale 8\u0026ndash;10-week-old C57BL/6 mice, weighing 20\u0026ndash;23 g, were obtained from Beijing Vital River Laboratory Animal Technologies Co., Ltd. Animals were housed in a pathogen-free animal facility with 12-h light\u0026ndash;dark cycles. All animal experimental procedures were carried out in accordance with the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health and approved by the Animal Care and Use Committee of the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, which were in compliance with the ARRIVE guidelines 2.0: Updated guidelines for reporting animal research.\u003c/p\u003e \u003cp\u003eAnimals with similar body weights were randomly allocated to be used for the sham-operation and ischemic-stroke models. For the surgical procedures, anesthesia was induced with 4% isoflurane in an induction chamber; anesthesia was maintained with 2% isoflurane delivered through a face mask (RWD Life Science, Shenzhen, China). A heating pad was used to maintain each mouse\u0026rsquo;s core temperature at 37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u0026deg;C throughout the surgical procedure. A modified intraluminal-filament model was used to induce middle cerebral artery occlusion (MCAO). After 60 min of MCAO, reperfusion was established by retracting the filament. To verify the success of MCAO surgery and reperfusion, blood flow in the cortex supplied by the MCA was measured using a laser Doppler flowmetry (PERIMED AB, Sweden). The mice without a decrease in blood flow below 30% of the baseline value after filament insertion were eliminated from the study. The success of reperfusion was determined by an increase in blood flow above 70% of the baseline value after filament retraction. The animals had free access to food and water throughout the reperfusion period. The animals were divided into two different reperfusion groups (6 h and 24 h reperfusion periods; samples were taken at a predetermined time point), a control group (samples taken 1 h after inducing MCAO), and a sham-operation group (n\u0026thinsp;=\u0026thinsp;5\u0026ndash;8 mice per group). Sample sizes were determined based on previous experience. Animals were excluded if no neurological deficits were present after surgery, according to the pre-established exclusion plan. The investigators examining infarct size and data analysis were blind to the grouping of mice.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eInfarct size analysis\u003c/h2\u003e \u003cp\u003eThe brains were sectioned into 2-mm-thick sections and placed in 1% 2,3,5-triphenyltetra-zolium chloride (TTC; #T8877, Sigma-Aldrich) for 10 min at 37\u0026deg;C. The TTC solution was then replaced with 4% paraformaldehyde, and the sections were incubated at room temperature for 1 h. The sections were photographed with a digital camera, and the infarct sizes were measured using ImageJ software (National Institutes of Health, USA). The contribution of post-ischemic edema to the volumes of sites of injury was corrected as described previously [10]. The infarct size (%) was calculated as ([volume of the left hemisphere \u0026ndash; non-infarct volume of the right hemisphere] / volume of the left hemisphere) \u0026times; 100%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eReverse transcriptase-quantitative polymerase chain reaction (RT-qPCR)\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted using the Direct-zol RNA MiniPrep Kit (Zymo Research). The RNA was reverse transcribed using iScript Reverse Transcription Supermix for RT-qPCR according to the manufacturer\u0026rsquo;s instructions (Vazyme, Nanjing, China). The primer sequences are shown in Supplementary Table\u0026nbsp;4. RT-qPCR was performed in a StepOnePlus Real-Time PCR System using Power SYBR Green (Roche, Switzerland). Relative RNA-expression levels were calculated using the comparative Ct method and normalized to actin mRNA expression. Data are expressed relative to a calibrator using the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eImmunofluorescence staining\u003c/h2\u003e \u003cp\u003eFrozen sections (10 \u0026micro;m thick) were allowed to dry on adhesion microscope slides (Citotest Scientific Co., Ltd., Haimen, China) at room temperature and then rehydrated in PBS. The sections were blocked in 10% normal goat serum (Thermo Fisher) in PBS\u0026thinsp;+\u0026thinsp;0.2% Triton X-100 for 1 h at room temperature. The samples were incubated at 4\u0026deg;C with the following primary antibodies in PBS\u0026thinsp;+\u0026thinsp;5% goat serum\u0026thinsp;+\u0026thinsp;0.2% Triton X-100: hamster anti-mouse CD31 (1:100, #MAB1398Z, Millipore, Billerica, MA, USA), donkey anti-mouse IgG (1:200, #715-545-150, Jackson ImmunoResearch, West Grove, PA, USA), mouse anti-TNF-α (1:100, #sc-52746, Santa Cruz Biotechnology, Dallas, TX), rabbit anti-IL-6 (1:100, #K009385P, Solarbio, Beijing, China), rabbit anti-CXCL3 (1:100, #orb13448, Biorbyt, Cambridge, United Kingdom), rabbit anti-THBS1 (1:100, #18304-1-AP, Proteintech, Wuhan, China). Excess antibody was removed by rinsing three times with PBS (7 min/wash step). Samples were then incubated at room temperature for 1 h with the following secondary fluorescently labeled antibodies from Jackson ImmunoResearch: Cy3 goat anti-hamster IgG (1:500, #127-165-099), Alexa Fluor 488 donkey anti-mouse IgG (1:500, #715-545-150), and Alexa Fluor 488 donkey anti-rabbit IgG (1:500, #711-545-152), diluted in PBS\u0026thinsp;+\u0026thinsp;10% goat serum\u0026thinsp;+\u0026thinsp;0.2% Triton X-100. Excess antibody was removed by rinsing three times with PBS (5 min/wash step). Slides were mounted in antifade mounting medium with DAPI (Solarbio) and imaged with an Olympus microscope to obtain 20\u0026times; or 40\u0026times; images. The immunofluorescence signal area or density was quantified using ImageJ software and normalized according to the vessel area (CD31-positive area) in three random ischemic areas per mouse.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed in Graphpad Prism software (ver. 7). Error bars represent the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and the number (n) of the samples employed is indicated in the legends. Statistical differences among multiple groups were compared using one-way analysis of variance (ANOVA), followed by Tukey\u0026rsquo;s multiple-comparisons test. Statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The RNA-seq data were assessed by a bioinformatics team at Sagene Biotech Co., Ltd. (Guangzhou, China), and proteomic analysis was performed by a bioinformatics team at LumingBio Company (Shanghai, China).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptomic analysis of HCMECs subjected to OGD/R\u003c/h2\u003e \u003cp\u003eThe HCMEC/D3 cells were divided into NC, OGD-6 h, and OGR groups and subjected to corresponding treatments. We observed that cells showed the most critical injury at 3 h reperfusion following 6 h of OGD, as determined by assessing cell viabilities in CCK-8 tests (Supplementary Fig.\u0026nbsp;1). Therefore, we chose the 3 h-OGD/R as a representative time point for reperfusion injury \u003cem\u003ein vitro\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eWe used FDR of \u0026lt;\u0026thinsp;0.05 and FC of \u0026gt;\u0026thinsp;2.0 as thresholds for assigning significant differences. The results showed that ECs exhibited different transcriptomic characteristics during OGD and OGD/R. Compared to the NC group, the OGD group had 2478 DEGs with 761 upregulated genes and 1717 downregulated genes. Compared to the OGD group, the OGD/R group had 390 DEGs with 301 upregulated and 89 downregulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb; Supplementary Data 1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eKEGG analysis showed that 277, 268, and 177 signaling pathways were associated with DEGs when comparing the OGD and NC groups, the OGD/R and NC groups, and the OGD/R and OGD groups, respectively; among them, 13, 12, and 23 signaling pathways were significantly enriched (Q-value\u0026thinsp;\u0026le;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec; Supplementary Data 2). The top five pathway terms associated with DEGs between the OGD/R and OGD groups were the TNF signaling pathway, TGF-β signaling pathway, cytokine-cytokine receptor interaction, NOD-like receptor signaling pathway, and Salmonella infection (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This suggests that dysregulation of these signaling pathways (especially upregulation of the TNF signaling pathway, evidenced by upregulation of \u003cem\u003eTNF\u003c/em\u003e, \u003cem\u003eIL-6\u003c/em\u003e, \u003cem\u003eLIF\u003c/em\u003e, \u003cem\u003eCXCL1\u003c/em\u003e, \u003cem\u003eCXCL2\u003c/em\u003e, \u003cem\u003eCXCL3\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003e, \u003cem\u003eTNFAIP3\u003c/em\u003e, \u003cem\u003eSOCS3\u003c/em\u003e, \u003cem\u003eMAP2K3\u003c/em\u003e, and other genes; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplementary Table\u0026nbsp;5) may play important roles in endothelial reperfusion injury. Furthermore, significantly enriched signaling pathways between the OGD/R and OGD groups were mostly associated with the inflammatory response, including the TNF signaling pathway, NOD-like receptor signaling pathway, JAK-STAT signaling pathway, MAPK signaling pathway, and NF-κB signaling pathway (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), suggesting that endothelial cell-mediated inflammatory responses are the most prominent mechanism underlying reperfusion injury at the mRNA level. Compared to the NC group, the OGD/R group and the OGD group demonstrated similarly altered genes and pathways, such as inhibited mitochondrial oxidative phosphorylation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec), suggesting common molecular mechanisms leading to ischemia and reperfusion injury.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop ten pathways associated with DEGs in HCMEC/D3 cells in the OGD/R group, as compared with those in the OGD group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePathway ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverlap\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAssociated genes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNF signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko04668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19/142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(Up) \u003cem\u003eTNF, IL-6, LIF, CXCL1, CXCL2, CXCL3, CCL20, JUNB, JUN, FOS, EDN1, NFKBIA, TNFAIP3, SOCS3, PTGS2, CSF2, MAP2K3, TCONS_00118147, TCONS_00266386\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGF-beta signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko04350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12/99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(Up) \u003cem\u003eTHBS1, FST, NOG, TNF, GDF6, MYC, INHBA, ID1, ID2, ID3, ID4\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(Down) \u003cem\u003eINHBE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCytokine-cytokine\u003c/p\u003e \u003cp\u003ereceptor interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko04060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19/334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(Up) \u003cem\u003eIL-6, IL-8, IL-11, IL-24, CXCL1, CXCL2, CXCL3, CCL20, CXCR5, CLCF1, TNF, LIF, CSF2, GDF6, INHBA, TNFSF15, PDFGRB\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(Down) \u003cem\u003eKIT, INHBE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNOD-like receptor\u003c/p\u003e \u003cp\u003esignaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko04621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9/76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(Up) \u003cem\u003eNFKBIA, TNF, IL-6, IL-8, CXCL1, CXCL2, TNFAIP3, PYCARD, TCONS_00266386\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalmonella infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko05132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10/107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(Up) \u003cem\u003eIL-6, IL-8, CXCL1, CXCL2, CXCL3, FOS, JUN, CSF2, PYCARD, DYNC2H1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLegionellosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko05134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9/92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(Up) \u003cem\u003eHSPA6, IL-6, IL-8, CXCL1, CXCL2, CXCL3, NFKBIA, TNF, PYCARD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJak-STAT signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko04630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12/185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(Up) \u003cem\u003eSPRY2, SPRY4, SOCS1, IL-6, IL-11, IL-24, LIF, MYC, SOCS3, CSF2, CLCF1\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(Down) \u003cem\u003eSPRY1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSignaling pathways regulating pluripotency of stem cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko04550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11/163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(Up) \u003cem\u003eID1, ID2, ID3, ID4, LIF, MYC, TBX3, INHBA\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(Down) \u003cem\u003eOTX1, INHBE, POU5F1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAPK signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko04010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15/318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(Up) \u003cem\u003eHSPA6, PDGFRB, BDNF, MYC, TNF, DUSP1, DUSP2, DUSP4, DUSP5, MAP2K3, JUN, FOS, NR4A1, TCONS_00118147, TCONS_00266386\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteoclast differentiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko04380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11/210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(Up) \u003cem\u003eSOCS1, SOCS3, NFKBIA, TNF, JUN, JUNB, FOS, FOSB, FOSL1, TCONS_00118147, TCONS_00266386\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eDEG, differentially expressed gene; HCMEC/D3, a type of human cerebral microvascular endothelial cells; OGD/R, oxygen-glucose deprivation and recovery; TNF, tumor necrosis factor; TGF, transforming growth factor; STAT, signal transducers and activators of transcription; MAPK, mitogen-activated protein kinase\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eGO analysis showed that for the DEGs between the OGD/R group and the OGD group, the top-three biological processes were cellular processes, single-organism processes, and response to stimuli; the top-three terms related to cellular components were cell, cell part, and organelle compartments, and the top-three terms related to molecular functions were binding, catalytic activity, and nucleic acid-binding transcription factor activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eProteomic analysis of HCMECs subjected to OGD/R\u003c/h2\u003e \u003cp\u003eBy using a threshold of a 1.5-fold change and a p-value of \u0026lt;\u0026thinsp;0.05, 201 differentially expressed proteins (DEPs) between the OGD/R and OGD groups were identified, including 96 upregulated and 105 downregulated proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb); Supplementary Data 3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eKEGG enrichment analysis showed that the top-five significantly enriched pathways between the OGD/R and OGD groups were Glycosaminoglycan biosynthesis-chondroitin sulfate/dermatan sulfate, extracellular matrix (ECM)-receptor interaction, ATP-binding cassette (ABC) transporters, Staphylococcus aureus infection, and Vitamin digestion and absorption (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Supplementary Data 4), suggesting their potential roles in reperfusion injury. Further analysis of the top ten enriched pathways demonstrated that pathways related to ECM destruction (evidenced by downregulation of COL4A1, LAMA4, ITGA1, ITGA7, ITGB3, and HSPG2) and remodeling (evidenced by upregulation of PIGT and THBS1) were the most significantly enriched (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Our results show that destruction and remodeling of the extracellular matrix is an important feature of endothelial reperfusion injury at the protein level. In addition, reperfusion injury also resulted in impaired endothelial transport functions, shown by downregulation of ABC transporters (ABCD1, ABCA3, ABCB7) and multivitamin transporter SLC5A6 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Consistent to the results of RNA-seq, proteomic analysis also revealed an inflammatory response in brain ECs after reperfusion injury, demonstrated by upregulation of proteins involved in Staphylococcus aureus infection and Cytosolic DNA-sensing pathway (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop ten pathways associated with DEPs in HCMEC/D3 cells in the OGD/R group, as compared with the OGD group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePathway ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted p value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnrichment\u003c/p\u003e \u003cp\u003escore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAssociated proteins\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycosaminoglycan biosynthesis-chondroitin sulfate / dermatan sulfate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa00532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.677419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Up) PIGT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECM-receptor interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa04512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.419355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Up) THBS1\u003c/p\u003e \u003cp\u003e(Down) COL4A1, LAMA4, ITGA1, ITGA7, ITGB3,\u003c/p\u003e \u003cp\u003eHSPG2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABC transporters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa02010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.129032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Down) ABCD1, ABCA3, ABCB7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStaphylococcus\u003c/p\u003e \u003cp\u003eaureus infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa05150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.838710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Up) FGG\u003c/p\u003e \u003cp\u003e(Down) KRT10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin digestion\u003c/p\u003e \u003cp\u003eand absorption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa04977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.010892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.870968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Up) APOB\u003c/p\u003e \u003cp\u003e(Down) SLC5A6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCytosolic\u003c/p\u003e \u003cp\u003eDNA-sensing pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa04623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.010892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.946237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Up) POLR3H, NFKBIA, NFKBIB\u003c/p\u003e \u003cp\u003e(Down) CGAS, POLR2L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGPI-anchor biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa00563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.027287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.838710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Up) PIGT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePI3K-Akt signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa04151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.031378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.558398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Up) THBS1, STK11, NR4A1\u003c/p\u003e \u003cp\u003e(Down) COL4A1, EIF4EBP1, ITGA7, ITGB3, PPP2R2D, LAMA4, ITGA1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa04979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.056043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.685908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Up) APOB\u003c/p\u003e \u003cp\u003e(Down) PCSK9, SORT1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUbiquinone and other\u003c/p\u003e \u003cp\u003eterpenoid-quinone biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa00130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.056043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.892473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Up) NQO1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eDEP, differentially expressed protein; HCMEC/D3, a type of human cerebral microvascular endothelial cells; OGD/R, oxygen-glucose deprivation and recovery; ECM, extracellular matrix; ABC, ATP-binding cassette; GPI, glycosylphosphatidylinositol; PI3K, phosphatidylinositol 3'-kinase\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe annotated 151 DEPs to 1010 GO function entries. The most enriched biological processes were cellular processes, biological regulation, metabolic processes, and regulation of biological processes; the most enriched cellular component GO terms were cell, cell part, and organelle compartments; the most enriched molecular function GO terms were binding, catalytic activity, enzyme regulator activity, and transporter activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIntegrative analysis of the transcriptomic and proteomic data\u003c/h2\u003e \u003cp\u003eTo investigate correlations between mRNA expression and protein expression levels, we performed a combined transcriptomic and proteomic analysis of data from the OGD/R and OGD groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). We generated a scatter plot of the mRNA expression levels of known genes (X-axis) versus the corresponding protein expression levels (Y-axis) to compare the protein versus mRNA abundances. The correlation value (r\u0026thinsp;=\u0026thinsp;0.492), calculated using Spearman\u0026rsquo;s rank correlation coefficient test (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), suggested little correlation between mRNA expression and protein expression. Integrative analysis showed that six genes (\u003cem\u003eDUSP1\u003c/em\u003e, \u003cem\u003eJUNB\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003e, \u003cem\u003eNR4A1\u003c/em\u003e, \u003cem\u003eSERPINE1\u003c/em\u003e, and \u003cem\u003eTHBS1\u003c/em\u003e) were differentially expressed at both the mRNA and protein levels, and all of them were upregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec; Supplementary Data 5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement of the expression changes of selected genes in a mouse model of MCAO/R\u003c/h2\u003e \u003cp\u003eIn mice subjected to 1 h MCAO without reperfusion, infarction had almost not developed based on TTC staining. Extensive infarction was detected in the cortical and striatum regions in brain tissues at both 6 h (12.0% \u0026plusmn; 1.7%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and 24 h (28.6% \u0026plusmn; 2.4%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) after reperfusion, especially at 24 h post-reperfusion (Supplementary Fig.\u0026nbsp;2a and 2b). We next assessed BBB leakage by detecting mouse blood IgG extravasation from vessels in the ischemic and infarct areas. The results showed that blood IgG leakage increased significantly in mice after 24 h of reperfusion (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Supplementary Fig.\u0026nbsp;2c and 2d). These results demonstrate that reperfusion following cerebral ischemia led to severe brain infarction and microvascular injury shown by BBB disruption.\u003c/p\u003e \u003cp\u003eDifferentially expressed TNF signaling pathway members (\u003cem\u003eTNF\u003c/em\u003e, \u003cem\u003eIL-6\u003c/em\u003e, \u003cem\u003eCXCL3\u003c/em\u003e) and THBS1 found by omics data were selected for further measurement in the mouse MCAO/R model using immunofluorescence analyses. We selected genes in the TNF signaling pathway because it was the most significantly enriched pathway, and selected THBS1 because it was found to be upregulated in both transcriptomic and proteomic data. However, fluorescence colocalization showed that these genes only partially colocalized with CD31; that is, they were not specifically expressed in ECs. Overall, TNF, IL-6, and CXCL3 were significantly upregulated after ischemia for 1 h and remained at high levels after reperfusion, and THBS1 was significantly upregulated after reperfusion compared to ischemia (Supplementary Fig.\u0026nbsp;3), which were consistent with the omics data.\u003c/p\u003e \u003cp\u003eWe further quantified mRNA expression changes of all differentially expressed TNF signaling pathway members and THBS1 in whole brain tissues but not only ECs, due to their nonspecific characteristics for ECs, by RT-qPCR. Consistent with the RNA-seq data, RT-qPCR data showed that the mRNA levels of most genes (\u003cem\u003eTNF\u003c/em\u003e, \u003cem\u003eIL-6\u003c/em\u003e, \u003cem\u003eLIF\u003c/em\u003e, \u003cem\u003eCXCL2\u003c/em\u003e, \u003cem\u003eCXCL3\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003e, \u003cem\u003eSOCS3\u003c/em\u003e, \u003cem\u003eMAP2K3\u003c/em\u003e, and \u003cem\u003eTHBS1\u003c/em\u003e) also showed significant upregulations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Supplementary Fig.\u0026nbsp;4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe mechanisms underlying reperfusion injury are complex and involve many pathological factors, such as inflammatory responses, mitochondrial dysfunction, free radical production, and Ca\u003csup\u003e2+\u003c/sup\u003e overload, etc [11, 12]. Previous studies have shown that anti-inflammatory molecules [13], antioxidants [14, 15], and calcium channel blockers can reduce cerebral reperfusion injury. Targeting mitochondria may also be helpful for preventing and treating reperfusion injury [12]. However, it is worth mentioning that although many neuroprotective agents have shown efficacy in experimental reperfusion injury, clinical studies have failed to demonstrate positive outcomes, and only a few drugs, such as edaravone and butylphthalide [15, 16], are currently in clinical use. The low efficacy of such neuroprotectants may reflect the facts that the mechanisms of reperfusion injury are still not fully clear. As mentioned above, damage to ECs is the main pathological basis of reperfusion injury. Thus, gaining insight into the molecular signatures of brain ECs during reperfusion injury will aid in developing multi-pathway and multi-target intervention strategies against reperfusion injury.\u003c/p\u003e \u003cp\u003eIn this study, we analyzed the transcriptomic and proteomic changes in HCMECs during reperfusion injury. Pathway analysis of the transcriptomics data showed that the TNF signaling pathway was the most significantly enriched pathway after OGD/R versus OGD, suggesting that dysfunction of the pathway is an important cause of endothelial reperfusion injury. In addition, the top significantly changed signaling pathways were mostly associated with the inflammatory response, including TNF signaling pathway, NOD-like receptor signaling pathway, JAK-STAT signaling pathway, MAPK signaling pathway, and NF-κB signaling pathway. Previous results showed that an excessive inflammatory response contributes to the pathogenesis of cerebral ischemia reperfusion injury [13, 17] and that the inflammatory response is mainly mediated by microglia, astrocytes, and peripheral leukocytes [18]. Our results suggest that endothelial cell-mediated inflammatory responses are also prominent mechanisms promoting reperfusion injury, especially through the TNF signaling pathway. Previous data revealed that signaling pathways that mediate the upregulation of proinflammatory factors and cell death include the TNF, JAK-STAT, MAPK, NF-κB, and TLR signaling pathways. The most extensively studied proinflammatory factors include TNF-a, IL-1, IL-6, and NF-κB [13, 19]. Our RNA-seq results for ECs are generally consistent with previous findings.\u003c/p\u003e \u003cp\u003eStudies have indicated that dysfunctional TNF-pathway signaling plays important roles in the occurrence and development of many diseases, such as inflammatory and autoimmune diseases, cancer, and cardiovascular disease [20, 21]. TNF-a is the most important molecule in the TNF signaling pathway and is considered as a major proinflammatory mediator. Interestingly, in pathological and physiological situations, TNF-a serves dual functions [21]. TNF-a has been reported to be both neurotoxic and neuroprotective in nervous system diseases. Previous data generated using a rat model of MCAO/R showed that TNF-a mRNA expression was increased in brain tissue [22]. Anti-TNF-α antibody can reduce cerebral infarction and brain edema volumes [23\u0026ndash;25]. \u003cem\u003eIn vitro\u003c/em\u003e OGR can enhance TNF-α secretion from microglia. Inhibiting TNF-α secretion in a mouse model of MCAO/R reduced leukocyte infiltration, decreased cerebral infarction, and improved the functional prognosis [26]. However, TNF-α may also have a neuroprotective effect in cerebral ischemia/reperfusion, as a previous study showed that pretreatment with a TNF-α inhibitor aggravated reperfusion injury [27]. In summary, the functions and mechanisms of TNF-α are complex and diverse and are not yet fully understood. In addition, inhibiting other proinflammatory genes, such as NF-κB, MAPK, and IL-1, can also reduce cerebral ischemia reperfusion injury in animals [13]. The results of our study further suggest that targeting inflammatory signaling pathway members, especially those in the TNF signaling pathway, may be an important way to protect the BBB and alleviate reperfusion injury.\u003c/p\u003e \u003cp\u003eProteomic analysis showed that pathways related to ECM destruction and remodeling, impairment of endothelial transport function, and inflammatory responses were significantly enriched after OGD/R versus OGD, suggesting that the associated proteins may also play important roles in reperfusion injury. Our results show that destruction and remodeling of the extracellular matrix is an important feature of endothelial reperfusion injury, such as downregulation of collagen IV, laminin, and integrin, and upregulation of PIGT and THBS1. Collagen IV, laminin and integrin proteins represent the main components of the basement membrane, and reperfusion can cause the activation of matrix metalloproteinases and degradation of basement membrane proteins [28]. THBS1 is one of the few genes found to be upregulated in both transcriptomic and proteomic data. THBS1 was identified as an endogenous inhibitor of angiogenesis and regulates endothelial cell adhesion, growth, motility, and survival [29]. A previous study showed that focal cerebral ischemia and subsequent reperfusion led to THBS1 expression at both the mRNA and protein levels [30]. In a permanent mouse model of MCAO, THBS1 knockout mice displayed significant deficits in their abilities to recover motor function [31], and in a mouse model of traumatic brain injury, THBS1 knockout significantly worsened BBB leakage and exhibited significantly worse neurological deficits in terms of motor and cognitive functions [32]. These data suggest that THBS1 may have a neuroprotective effect. However, information is limited regarding the role of THBS1 in cerebral ischemia-reperfusion injury and further studies are needed.\u003c/p\u003e \u003cp\u003eOur transcriptomic and proteomic data showed little correlation between the mRNA and protein expression levels. In fact, steady-state mRNA and protein levels have been compared in numerous studies, which also showed poor correlations [33]. Protein expression is influenced by an array of post-transcriptional regulatory mechanisms, the correlation between protein and mRNA levels is generally modest [34, 35]. Furthermore, extremely low-abundance proteins may be undetected when they are below the detection limits of current methods. Nevertheless, integrated transcriptomic and proteomic analyses provide a new paradigm for understanding endothelial reperfusion injury, in that protein analysis portrays the current states and reflects the immediate impact of reperfusion on pre-existing proteins of ECs, and transcriptomic analysis reflects the impact of reperfusion on the transcripts of ECs that will manifest as proteins and modulate cell functionality at subsequent timepoints.\u003c/p\u003e \u003cp\u003eProtein expression levels of TNF, IL-6, CXCL3 and THBS1 were further measure in a mouse MCAO/R model using immunofluorescence analyses. However, fluorescence colocalization showed that these genes were not specifically expressed in ECs. Overall, TNF, IL-6, and CXCL3 were significantly upregulated after ischemia for 1 h and remained at high levels after reperfusion, and THBS1 was significantly upregulated after reperfusion compared to ischemia, which were consistent with the omics data, suggesting that these genes are important targets for preventing and treating reperfusion injury. IL-6 is a proinflammatory cytokine and CXCL3 is a chemokine, but their specific roles in cerebral reperfusion injury have not been clarified. RT-qPCR was performed to further quantify mRNA expression changes of all differentially expressed TNF signaling pathway members and THBS1. By comparing the RT-qPCR and RNA-seq data, we found that most genes showed consistent expression changes. However, it must be mentioned that there are limitations in translating results from a human endothelial cell line to a mouse model as we have done in this study. Nevertheless, our findings indicate that targeting these genes and their associated pathways may significantly attenuate endothelial reperfusion injury in addition to attenuating global brain reperfusion injury.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, we identified the dysregulated genes and pathways in brain ECs during reperfusion, and provided comprehensive molecular mechanism information for further functional studies. Our findings suggest that targeting the inflammatory pathways (especially the TNF signaling pathway) and ECM destruction may be important approaches for attenuating reperfusion injury after ischemic stroke.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eABC,\u0026nbsp;ATP-binding cassette; BBB, Blood-brain barrier;\u0026nbsp;DEGs, Differentially expressed genes; DEPs, Differentially expressed proteins; ECs, Endothelial cells; ECM, Extracellular matrix; FC, Fold change; FDR, False-discovery rate; GO, Gene Ontology; HCMECs, Human cerebral microvascular endothelial cells; IS, Ischemic stroke; KEGG, Kyoto Encyclopedia of Genes and Genomes;\u0026nbsp;LC-MS/MS, Liquid chromatography-tandem mass spectrometry;\u0026nbsp;MCAO, Middle cerebral artery occlusion; NC, Normal control; OGD, Oxygen-glucose deprivation; OGD/R,\u0026nbsp;OGD plus recovery;\u0026nbsp;RNA-seq,\u0026nbsp;RNA-sequencing;\u0026nbsp;TNF, Tumor necrosis factor.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Guangdong Province Basic and Applied Basic Research Grant (2021A1515220105, 2021B1515120089); National Natural Science Foundation of China (32170985); National Key Research and Development Program of China \u0026nbsp;(2021YFA0910000); Science Technology and Innovation Commission of Shenzhen Municipality (JCYJ20210324115800003,\u0026nbsp;SGLH20180625142404672, JCYJ20200109114608075); International collaboration project of Chinese Academy of Sciences (172644KYSB20200045);\u0026nbsp;President Foundation of Nanfang Hospital (2021B021);\u0026nbsp;CAS-Croucher Funding Scheme for Joint Laboratories; and Guangdong Innovation Platform of Translational Research for Cerebrovascular Diseases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eYabin Ji:\u003c/strong\u003e Conceptualization, Methodology, Validation, Formal analysis, Writing - review \u0026amp; editing, Project administration, Funding acquisition. \u003cstrong\u003eYiman Chen:\u003c/strong\u003e Methodology, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review \u0026amp; editing, Visualization. \u003cstrong\u003eXixi Tan:\u003c/strong\u003e Investigation. \u003cstrong\u003eXiaowen Huang:\u003c/strong\u003e Investigation. \u003cstrong\u003eQiang Gao:\u003c/strong\u003e Investigation. \u003cstrong\u003eYinzhong Ma:\u003c/strong\u003e Investigation. \u003cstrong\u003eMin Yu:\u003c/strong\u003e Visualization. \u003cstrong\u003eCheng Fang:\u003c/strong\u003e Visualization. \u003cstrong\u003eYu Wang:\u003c/strong\u003e Writing - review \u0026amp; editing. \u003cstrong\u003eZhu Shi:\u003c/strong\u003e Writing - review \u0026amp; editing, Supervision. \u003cstrong\u003eJunlei Chang:\u003c/strong\u003e Conceptualization, Writing - review \u0026amp; editing, Visualization, Supervision, Project administration, Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDetailed\u0026nbsp;information\u0026nbsp;for this study can be found in the supplementary materials. Additional data information is available upon reasonable request from the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;All animal experimental procedures were carried out in accordance with the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health and approved by the Animal Care and Use Committee of the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences (Approval number SIAT-IRB-170304-YYS-CJL-A0301).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRabinstein A (2017) Treatment of Acute Ischemic Stroke. 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Nature 473:337\u0026ndash;342. https://doi.org/10.1038/nature10098\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"endothelial cell, inflammation, ischemic stroke, multi-omics, reperfusion injury ","lastPublishedDoi":"10.21203/rs.3.rs-2216319/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2216319/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eReperfusion after ischemic stroke often causes brain microvascular injury and blood-brain barrier disruption; however, the underlying mechanisms are unclear. Here, we performed transcriptomic and proteomic analyses on human cerebral microvascular endothelial cells following oxygen-glucose deprivation (OGD) or OGD plus recovery (OGD/R), to identify molecules and signaling pathways dysregulated by reperfusion. Transcriptomic analysis identified 390 differentially expressed genes (301 upregulated and 89 downregulated) between the OGD/R and OGD groups. Pathway analysis indicated that the tumor necrosis factor (TNF) signaling pathway was the most significantly enriched. Furthermore, these genes were mostly associated with inflammation, including the TNF signaling pathway, TGF-β signaling pathway, cytokine-cytokine receptor interaction, NOD-like receptor signaling pathway, and NF-κB signaling pathway. On the other hand, 201 differentially expressed proteins (96 upregulated and 105 downregulated) were identified by proteomics between the OGD/R and OGD groups and were primarily associated with extracellular matrix destruction and remodeling, impairment of endothelial transport function, and inflammatory responses. Six genes (\u003cem\u003eDUSP1\u003c/em\u003e, \u003cem\u003eJUNB\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003e, \u003cem\u003eNR4A1\u003c/em\u003e, \u003cem\u003eSERPINE1\u003c/em\u003e, and \u003cem\u003eTHBS1\u003c/em\u003e) were upregulated by OGD/R at both the mRNA and protein levels. The expression of genes related to inflammatory responses and extracellular matrix were further measured in a mouse model of cerebral ischemia/reperfusion \u003cem\u003ein vivo\u003c/em\u003e. Overall, our study provides a comprehensive molecular atlas of brain endothelial reperfusion injury and may facilitate the understanding and treatment of reperfusion injury after ischemic stroke.\u003c/p\u003e","manuscriptTitle":"Integrated transcriptomic and proteomic profiling reveals the key molecular signatures of brain endothelial reperfusion injury","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-11-02 19:35:45","doi":"10.21203/rs.3.rs-2216319/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c972f7fe-41b2-4e20-9454-3a92296d6a93","owner":[],"postedDate":"November 2nd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-04-15T11:26:50+00:00","versionOfRecord":{"articleIdentity":"rs-2216319","link":"https://doi.org/10.1111/cns.14483","journal":{"identity":"cns-neuroscience-and-therapeutics","isVorOnly":true,"title":"CNS Neuroscience \u0026 Therapeutics"},"publishedOn":"2023-10-03 11:26:50","publishedOnDateReadable":"October 3rd, 2023"},"versionCreatedAt":"2022-11-02 19:35:45","video":"","vorDoi":"10.1111/cns.14483","vorDoiUrl":"https://doi.org/10.1111/cns.14483","workflowStages":[]},"version":"v1","identity":"rs-2216319","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2216319","identity":"rs-2216319","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}