Abstract It is well-established that reperfusion following cerebral ischemic injury gives rise to secondary injury accompanied by structural and functional damage. However, it remains unclear how global genes changes in cerebral ischemia-reperfusion injury (IRI). This study investigated global gene expression in the hippocampi of Wistar rats following transient cerebral IRI using an RNA-sequencing strategy. The results revealed ≥2-fold up-regulation of 156 genes and ≥2-fold down-regulation of 26 genes at 24 h post-reperfusion. Fifteen differentially expressed genes were selected to confirm the RNA-sequencing results. Gene expression levels were dynamic, with the peak expression level of each gene occurring at different time points post-reperfusion. Gene Ontology (GO) analysis classified the differentially expressed genes as mainly involved in inflammation, stress and immune response, glucose metabolism, proapoptosis, antiapoptosis, and biological processes. KEGG pathway analysis suggested that IRI activated different signaling pathways, including focal adhesion, regulation of actin cytoskeleton, cytokine-cytokine receptor interaction, MAPK signaling, and Jak-STAT signaling. This study describes global gene expression profiles in the hippocampi of Wistar rats using the middle cerebral artery occlusion (MCAO) model. These findings provide new insights into the molecular pathogenesis of IRI and potential drug targets for the prevention and treatment of IRI in the future. Keywords: gene expression profile, ischemia-reperfusion injury, MCAO model, RNA sequencing INTRODUCTION Ischemic stroke is one of the leading causes of death and disability worldwide, making its prevention and treatment urgent. Evidence from clinical trials suggests that the most effective treatment currently available is thrombolysis therapy [[32]1, [33]2]. However, reperfusion after thrombolysis may further exacerbate cerebral ischemic injury by disrupting cerebral microvascular endothelial cells, a phenomenon referred to as cerebral ischemia-reperfusion injury (IRI) [[34]3, [35]4]. Currently, there is no approved therapy for the treatment of IRI. The mechanisms of cerebral IRI are complex. During reperfusion, blood flow is reintroduced to the tissue, triggering a series of events including oxidative stress, energy failure, inflammation, excitatory amino acids toxicity, the activation of various cell-signaling pathways, apoptosis, and neuronal death [[36]5, [37]6]. Meanwhile, the ischemia-reperfusion process usually lead to increase or decrease of a number of genes, such as insulin-like growth factor-1 (IGF-1)[[38]7], heat shock protein (HSP27) [[39]8], protein tyrosine kinase type 2 beta (Ptk2b) [[40]9], high mobility group box 1 (HMGB1) [[41]10], and pyruvate dehydrogenase kinase 4 (PDK4) [[42]11]. We hypothesize that the process of reperfusion induces the abnormal expression of a large number of genes. This altered gene expression might activate or suppress corresponding signaling pathways, resulting in cellular necrosis or apoptosis. In the present study, we describe changes in global gene expression in rat hippocampi after cerebral IRI. These results provide new insights into understanding the mechanisms of cerebral IRI. Furthermore, the identified genes may be considered potential drug targets for the prevention and treatment of cerebral IRI. RESULTS IRI induces cerebral infarct The infarct areas appeared white when stained with TTC. As shown in Figure [43]1, brain tissue in the sham group appeared uniform red in color, indicating no infarction. After the right cerebral hemisphere was subjected to ischemia and reperfusion, the infarct volume was increased 33.64% ± 2.06% compared with the sham group. This result demonstrated that IRI induced cerebral infarct, indicating that the model was successful. Figure 1. Infarct volume in sham and IRI/24 h groups determined by TTC staining. [44]Figure 1 [45]Open in a new tab (Panel A) Representative coronal brain sections of the sham and IRI/24 h groups. Brain sections in the sham group showed no infarction. The infarct volumes in the IRI/24 h group were significantly increased compared with those in the sham group. (Panel B) Percentage of cerebral infarct volume to total brain volume. The percentage of infarct volume was approximately 33.6% at 24 h in the IRI model. **P < 0.01. Global gene expression profile of IRI A global gene expression profile provides comprehensive insights into which hippocampal genes participate in the pathology of cerebral IRI. Genes with similar expression patterns often have a functional correlation. We carried out cluster analysis of gene expression patterns using cluster and java Treeview software. There were significant differences in cluster analysis between the sham group and the IRI/24 h group (Figure [46]2A). The genes with increased expression were divided into four groups. Genes with decreased expression were divided into six groups. Figure 2. Comparison between sham and IRI/24 h groups by RNA sequencing. Figure 2 [47]Open in a new tab (Panel A) The differentially expressed genes between the sham group (n = 6) and the IRI/24 h group (n = 6) were analyzed using SAM software and visualized with Treeview tools upon hierarchical clustering. A colored tag represents gene expression levels. Red spectrum denotes up-regulated genes, and blue denotes down-regulated genes. (Panel B) Scatter plot display of the differentially expressed genes between sham and IRI groups. The X and Y axes represent the log2 fold change in gene expression. Yellow points represent the up-regulated genes with fold change less than 1 and P < 0.05. Blue points denote the down-regulated genes with fold change greater than 1 and P < 0.05. Brown points indicate genes in which there were no significant differences in expression. 182 genes demonstrated differential expression in the IRI/24 h group relative to the sham group. As shown in Figure [48]2B, 156 genes were significantly up-regulated in the IRI/24 h group when compared with the sham group, and 26 genes were significantly down-regulated. There was a more than 2-fold change in expression levels of the indicated genes in the IRI/24 h group relative to the sham group. It is likely that these 182 genes involve in the pathology of IRI, and their annotation will help to further elaborate the molecular mechanism of IRI. A portion of the differentially expressed genes are listed in Table [49]1. Table 1. Genes differentially expressed between sham and IRI/24 h groups. Gene ID Description Symbol Fold change 24471 heat shock protein B1 Hspb1 10.84648 24498 interleukin 6 IL-6 10.40939 25544 selectin E Sele 7.965784 89829 suppressor of cytokine signaling 3 Socs3 7.267831 307351 tubulin, beta 6 class V Tubb6 6.173174 24472 heat shock 70 kD protein 1A Hspa1a 4.493855 24577 myelocytomatosis oncogene Myc 3.284498 680611 B-cell CLL/lymphoma 3 Bcl3 2.885765 24887 Bcl2-associated X protein Bax 2.819229 314322 FBJ osteosarcoma oncogene Fos 2.981011 304962 activating transcription factor 6 Atf6 2.781895 24387 glial fibrillary acidic protein Gfap 2.479572 24451 heme oxygenase (decycling) 1 Hmox1 2.285812 308995 integrin, alpha L Itgal 2.165753 361321 autophagy related 12 Atg12 3.980192 84398 CD93 molecule Cd93 3.581731 60350 CD14 molecule CD14 2.475128 83785 vascular endothelial growth factor A Vegfa 2.773833 497942 chemokine ligand 16 Cxcl16 2.54374 315609 CD3 molecule, epsilon Cd3e -6.21785 292843 sialic acid-binding Ig-like lectin 5 Siglec5 -5.69617 299757 neurotensin Nts -4.54653 83725 Wolfram syndrome 1 (wolframin) Wfs1 -2.85056 24224 B-cell CLL/lymphoma 2 Bcl-2 -1.68324 [50]Open in a new tab Confirmation of differentially expressed genes by RT-PCR To confirm the results obtained from RNA sequencing, we examined the expression of representative genes at the mRNA level using RT-PCR (Figure [51]3). The sequences of all primers used are listed in Tables [52]2 and [53]3. The genes belonging to the heat shock protein (Hsp) family were selected for amplification because their expression showed the most significant up-regulation in the RNA-sequencing studies. As shown in Figure [54]3A, expression of Hspb1, Hspa1a, and Hspa1b was significantly increased in the IRI/24 h group compared with the sham group with a fold change of 4.06, 2.43, and 20.88, respectively (Figure [55]3E, **P < 0.01, ***P < 0.001). Two genes, Atf6 and Chop2, related to endoplasmic reticulum (ER) stress, were also significantly up-regulated in IRI/24 h (Figure [56]3B). The fold change in the IRI/24 h group relative to the sham group of Atf6 and Chop2 was 1.69 and 1.33, respectively (Figure [57]3F, *P < 0.05). The expression of Wfs1, another gene related to ER stress, was down-regulated 2.4-fold (Figure [58]3F, **P < 0.01) in the IRI/24 h group. Three genes, calreticulin, Atg3, and Atg12, were related to autophagy. Their relative mRNA levels were significantly higher with fold changes of 2.13, 3.25, and 2.36, respectively. (Figure [59]3C and [60]3G, *P < 0.05, **P < 0.01). The expression of Bcl-2 was lower, while the mRNA levels of Bax and Caspase-12 were higher, in the IRI/24 h group than in the sham group (Figure [61]3D and [62]3H, *P < 0.05, **P < 0.01). These results were consistent with the sequencing results. Figure 3. Differential gene expression between sham and IRI/24 h groups was detected by RNA sequencing. [63]Figure 3 [64]Open in a new tab (Panel A-D) Representative images of RT-PCR confirming differential gene expression. (Panel E-H) Quantitative analysis of gene expression. *P < 0.05, **P < 0.01, ***P < 0.001 vs sham. Table 2. Primer sequences used for gene amplification in the rat model. Primer names Forward sequences ( 5′-3′) Reverse sequences ( 5′-3′) β-actin tggaatcctgtggcatccatgaac taaaacgcagctcagtaacagtccg Hspa1a gagggtctcaagggcaagat ttctcagccagcgtgttaga Hspa1b tctaacacgctggctgagaa agcagccatcaagagtctgt Hspb1 acgaagaaaggcaggatgaa gacagggaagaggacaccag Atf6 cccaccaaaggtcagactgt cttgggactttgagcctctg Chop2 agcagaggtcacaagcacct acagttcatgcttggtgcag Wfs1 cagccgagaagggacagata ggcaagtcgcaggtagtgtt Calreticulin gaatggaagccacgtcaaat tggcctctacagctcatcct Atg3 cggtcctcaaggaatcaaaa gcctccaattcatccgaata Atg12 aagatggcagaagacccaga tgaagtcgatgagtgcttgg Bcl-2 aagctgtcacagaggggcta ctcacttgtggcccaggtat Bax ctgcagaggatgattgctga gatcagctcgggcactttag Caspase-12 gctgccaagagaacacatga tccctttgcttgtggatacc Canx gatgctgtcaagccagatga ttagggttggcaatctgagg Cacna1e gcccttaaagctcgtgtcag gcattggaagacagtcagca Slc24a2 ccgaggaagatgatgaccag tccagagagggaacacgatg Ttr actggaaggctcttggcatt gctgtaggagtacgggctga [65]Open in a new tab Table 3. Primer sequences used for gene amplification in the cell model. Primer names Forward sequences ( 5′-3′) Reverse sequences ( 5′-3′) β-actin acactgtgcccatctacgagggg atgatggagttgaaggtagtttcgtggat Hspa1a gatcaacgacggagacaagc gctgcgagtcgttgaagtag Hspa1b gatcaacgacggagacaagc gctgcgagtcgttgaagtag Hspb1 ccaagtttcctcctccctgt ggtgactgggatggtgatct Atf6 gcagaacctcagccactttc gctgctggaagcaataaagg Chop2 tcttgaccctgcttctctgg gctgtgccactttcctttca Wfs1 ccctacctggtgtgcttcat gagctccagagacgtgaacc Calreticulin ccccagtgattcagaaccct gccaaactcctcagcgtatg Atg3 acggcagcctttaacagttg cttcatcacctcagcatgcc Atg12 ctggaggggaaggacttacg gggaaggagcaaaggactga Bcl-2 gggtacgataaccgggagat ctgaagagctcctccaccac Bax ggggacgaactggacagtaa cagttgaagttgccgtcaga Caspase-12 caagcactgggatcaagag aaccaaacaatcccagcacc [66]Open in a new tab To understand the expression of the above genes in the OGD cell model, we compared their expression between the OGD model and the control model. As shown in Figure [67]4, the expression trends of these genes in the OGD model were consistent with the trends in the animal models. The up-regulated genes were Hspb1, Hspa1a, Hspa1b, Atf6, Chop2, calreticulin, Atg3, Atg12, Bax, and Caspase-12. The down-regulated genes were Wfs1 and Bcl-2. The results obtained using RT-PCR were consistent with the results obtained by RNA sequencing. Figure 4. Differential gene expression between OGD cell model and control was detected by RNA sequencing. [68]Figure 4 [69]Open in a new tab (Panel A-D) Representative images of RT-PCR confirming the differential gene expression. (Panel E-H) Quantitative analysis of gene expression. *P < 0.05, **P < 0.01 vs control. Gene expression patterns vary over the course of IRI In addition, we detected the expression of several genes at different times post-reperfusion. As shown in Figure [70]5, the expression patterns of four genes displayed significant differences at various times during the post-reperfusion period. The expression of the Canx gene was highest at 6 h post-reperfusion, and then gradually decreased. The Cacna1e gene reached its highest expression levels at 12 h post-reperfusion, then decreased. The expression of Slc24a2 first decreased, then gradually increased, peaked 24 h post-reperfusion, then decreased once more. Ttr gene expression was reduced during the first 12 h post-reperfusion, but was dramatically raised at 24 h and 48 h post-reperfusion. These results demonstrate that the patterns of gene expression are complex and diverse, increasing the difficulty of understanding the molecular pathology of IRI. Figure 5. Differential gene expression at different reperfusion times was detected by RNA sequencing. [71]Figure 5 [72]Open in a new tab (Panel A) Representative images of RT-PCR demonstrating differential gene expression. (Panel B) Quantitative analysis of gene expression. Elevation of TNF-α, IL-6, and Icam-1 levels in rat and cell models The expression of proinflammatory factors and adhesion molecules was detected using ELISA. As shown in Figure [73]6A, the levels of TNF-α, IL-6, and ICAM-1 in the hippocampi of the IRI/24 h group were all higher than the levels in the hippocampi of the sham group. TNF-α was increased by 2.62-fold (2.04 ± 0.42 vs 0.78 ± 0.07 ng/g), IL-6 by 2.86-fold (2.66 ± 0.16 vs 0.93 ± 0.41 ng/g), and Icam-1 by 3.95-fold (111.72 ± 3.69 vs 28.29 ± 1.64 ng/g). The levels of TNF-α, IL-6, and ICAM-1 were also detected in the OGD cell model (Figure [74]6B). TNF-α, IL-6, and ICAM-1 were up-regulated with fold increases of 2.13 (1.11 ± 0.07 vs 0.52 ± 0.06 ng/g), 1.73 (1.96 ± 0.14 vs 1.13 ± 0.11 ng/g), and 1.79 (44.08 ± 2.71 vs 24.63 ± 2.63 ng/g), respectively, compared with the sham group. These findings indicate that reperfusion following ischemic injury promotes the activation of inflammatory factors and adhesion molecules. Figure 6. TNF-α, IL-6, and ICAM-1 levels were detected by ELISA. [75]Figure 6 [76]Open in a new tab (Panel A) TNF-α, IL-6, and ICAM-1 levels in hippocampi of sham and IRI/24 h groups. (Panel B) TNF-α, IL-6, and ICAM-1 levels in the control and the OGD cell model. The levels of TNF-α, IL-6, and ICAM-1 were significantly increased in the ischemia-reperfusion group compared with the sham group. **P < 0.01, ***P < 0.001. GO analysis of biological functions of differentially expressed genes To elucidate the roles of differentially expressed genes, GO analysis was used to analyze the biological functions of those genes. As shown in Table [77]4 and Figure [78]7, the differentially expressed genes were divided into three functional groups. In the biological process group, the top four categories were single-organism processes, cellular processes, response to stimulus, and biological regulation. The genes belonging to these categories were Atf3, Ssc5d, Lgals3, Olr1, Sema3d, Pter, Myd88, Selplg, and Wfs1. They are involved in responding to various stimuli, metabolic processes, the inflammatory response, and cell signaling. In the molecular function group, the identified categories were binding and activation. Genes belonging to this category were Igfbp2, Fibcd1, Socs3, Gfap, Pla1a, and Cd3e. They are involved in growth factor binding, carbohydrate derivative binding, protein kinase regulator activity, structural molecule activity, catalytic activity, and signaling receptor activity. The cellular components group categories included cell, cell part, membrane components, and organelles. The genes Abca4, Tspo, and Siglec5 belonged to the membrane components category. These results suggest that IRI induces abnormalities of biological processes, molecular function, and cellular components. Table 4. Gene ontology classification of differentially expressed genes. GO term Input symbol Expression P-value Biological process GO:0050896//response to stimulus Atf3 up 5.20E-08 GO:0006950//response to stress GO:0019538//protein metabolic process Ssc5d Olr1 up up 6.23E-16 6.20E-06 GO:0006952//defense response Lgals3 up 4.12E-06 GO:0006954//inflammatory response Myd88 up 6.79E-07 GO:0048869//cellular developmental process Sema3d up 8.52E-06 GO:0007154//cell communication Nts down 1.15E-26 GO:0006984//ER-nucleus signaling pathway GO:0007159//leukocyte cell-cell adhesion Wfs1 Selplg down down 2.06E-07 5.65E-14 GO:0008152//metabolic process Pter down 6.51E-10 Molecular function GO:0019887//protein kinase regulator activity Socs3 up 6.07E-12 GO:0005198//structural molecule activity Gfap up 0 GO:0003824//catalytic activity GO:0005216//ion channel activity Pla1a Piezo1 up up 1.05E-07 3.45E-08 GO:0005520//insulin-like growth factor binding Igfbp2 up 2.03E-11 GO:0038023//signaling receptor activity Cd3e down 6.23E-08 GO:0097367//carbohydrate derivative binding Fibcd1 down 1.53E-31 Cellular components GO:0032991//macromolecular complex GO:0043229//intracellular organelle Tubb6 Slc1a4 up up 6.83E-23 1.72E-09 GO:0031966//mitochondrial membrane GO:0042101//T cell receptor complex Tspo Cd3e up down 5.40E-06 6.23E-08 GO:0031224//component of membrane Siglec5 down 4.08E-06 [79]Open in a new tab Figure 7. GO functional classification of IRI and sham groups. [80]Figure 7 [81]Open in a new tab Blue denotes biological process; brown denotes cellular component; yellow denotes molecular function. Pathway analysis of differentially expressed genes KEGG pathway analysis was used to provide a more in-depth understanding of the biological functions of the differentially expressed genes. Pathways that were significantly induced after cerebral IRI are listed in Table [82]5. This analysis suggests that the cellular pathways involved in IRI include focal adhesion, regulation of the actin cytoskeleton, cytokine-cytokine receptor interaction, MAPK signaling, Jak-STAT signaling, cell adhesion molecules, and ER stress. Figure [83]8 shows two significantly induced pathways, ER stress signaling and autophagy. In the ER stress signaling pathway, the genes ATF6, WFS1, CHOP2, Bcl2, and Bax were differentially expressed in the IRI/24 h group, as detected by RNA sequencing. In the autophagy pathway, the expression of ATG3 and ATG12 was significantly increased at 24 h post-reperfusion. These results suggest that signaling pathways containing different genes were actived or inhibited in the process of IRI. Table 5. KEGG pathway classification of differentially expressed genes. Pathway Genes P-value Pathway ID Focal adhesion Itga, Dock1, Rock, Rtk 2.094476e-05 ko04510 Regulation of actin cytoskeleton Pxn, Rock, Itg, Limk, Arp2/3 0.002440363 0.0009153268 ko04810 ko04060 Cytokine-cytokine receptor interaction Lxcl16, cl2, Sf1a 0.0009153268 ko04060 Phagosome Tubb, MhcII, αMβ2 0.001802472 ko04145 MAPK signaling pathway Fgfr, Hsp72, c-fos 0.001802472 ko04010 Jak-STAT signaling pathway STAT, Myc, Socs 0.001047253 ko04630 Cell adhesion molecules Itgb2, Selplg, Ptprf 0.001865349 ko04514 Toll-like receptor signaling pathway Lbp, Opn, Myd88 0.001876453 ko04620 ER stress Atf6, Chop, Wfs1 0.003695505 ko04141 [84]Open in a new tab Figure 8. KEGG pathway classification of differentially expressed genes. [85]Figure 8 [86]Open in a new tab (Panel A) ER stress signaling pathway. Blue denotes differentially expressed genes in ER stress signaling pathways. (Panel B) Autophagy pathway. Blue denotes differentially expressed genes in autophagy pathways. DISCUSSION The MCAO is a universal model used to establish a focal cerebral IRI without the need for a craniotomy operation. This model enables accurate control of ischemia-reperfusion time. Previous experiments in our lab confirmed other reports [[87]12] that transient cerebral ischemic injury for 2 h followed by post-reperfusion measurements at 24 h provides the most appropriate model of IRI. Accordingly, 2 h of ischemia followed by analysis of hippocampi sampled at 24 h post-reperfusion were the conditions under which abnormal expression of genes was evaluated. It has been previously established that genes related to inflammation, apoptosis, proliferation, cell cycle, ER stress, and autophagy [[88]13–[89]16] are involved in the cellular events observed in cerebral IRI. Our results obtained from RNA sequencing were similar to those of previous studies, and draw a consistent conclusion regarding the genes involved in IRI. The expression of inflammatory factors was increased in the pathological process of cerebral IRI. The release of inflammatory molecules, including TNF-α, IL-1β, IL-6, NF-κB, and TGF-β, likely further aggravates cell injury during ischemia-reperfusion [[90]17, [91]18]. Consistent with previous studies in rat and cell models, IRI significantly induced the increased expression of TNF-α, IL-6, and intercellular adhesion molecule (ICAM1), indicating that the inflammatory response was actived by cerebral IRI. Specific molecules that bind neutrophils, including CAMs and selectins (ICAM2, ICAM4, ICAM5, VCAM, selectin E, and selectin L), exhibited abnormal expression in our experiment. These genes play an important role in leukocyte infiltration and transmigration [[92]19]. Gfap is a strong marker of gliosis, and may also serve as a marker of neuroinflammation [[93]20, [94]21]. In the current study, the expression of Gfap was up-regulated 2.286-fold (Table [95]3). This suggests that the over-activation of glia may be induced during IRI. Cellular apoptosis may accompany cerebral IRI due to the expression of apoptosis-related genes [[96]22, [97]23]. Bcl-2, a key antiapoptotic factor, could inhibit the release of cytochrome c. Bax, as a proapoptotic factor, may increase the release of cytochrome c. Various reports suggest that Bcl-2 expression is decreased and Bax expression is increased at different reperfusion times [[98]24, [99]25]. Consistent with these results, we demonstrated that Bcl-2 expression was decreased and Bax expression was increased in the hippocampus at 24 h post-reperfusion, as measured by RNA sequencing and RT-PCR. The differential expression of other genes related to apoptosis, including Bad, Bcl-6, Bak, Bcl2-like 1-15 (apoptosis facilitator), Bcl2-associated factors, and Bag (BCL2-associated athanogene), was detected through RNA sequencing. The expression of Caspase family members was also altered. Caspase-4, Caspase-7, and Caspase-8 exhibited 4.19-, 2.19-, and 1.57-fold increases, respectively, indicating that these genes may involve in IRI. Hsps are ubiquitous and highly conserved proteins that are induced in response to stressors including high temperature, ischemia, hypoxia, and pathological state [[100]26, [101]27]. Hsps may be induced after cerebral ischemia, and their expression may protect against cellular damage via anti-inflammatory and antiapoptotic mechanisms, or the repair of damaged molecules [[102]28–[103]30]. In the present study, Hspb1, Hspa1a, and Hspa1b were significantly increased at 24 h post-reperfusion in cellular and rat models. It is essential to further research the function of these genes to fully understand the mechanism of IRI. Wolfram syndrome 1 (Wfs1) and activating transcription factor 6α (Atf6α) are transmembrane proteins localized to the ER [[104]31, [105]32]. Wfs1 negatively regulates Atf6α through the ubiquitin-proteasome pathway. Wfs1 stabilizes the E3 ubiquitin ligase HRD1, leading to the suppression of ER stress signaling. In the current study, we further verified the inverse relationship between Wfs1 and Atf6. The expression level of Wfs1 was significantly decreased, but Atf6 and Chop2 expression levels were significantly increased. This indicates that cerebral IRI induces a decrease in Wfs1 expression, which in turn activates the Atf-Chop pathway (Figure [106]8A). Previous reports [[107]24] demonstrated that extended periods of ER stress activate Caspase-12, leading to cell death. Our results confirm these reports. Expression of Caspase-12 increased 3.59-fold based on RNA-sequencing analysis, and 2.85-fold based on RT-PCR analysis. These results suggest that ER stress is involved in cerebral IRI. Previous reports established a connection between cerebral IRI and the activation of autophagy [[108]33, [109]34]. Autophagy is a conserved homeostatic process that maintains cellular balance through the lysosomal system [[110]35, [111]36]. In normal physiological processes, misfolded molecules or damaged organelles are eliminated through autophagy. However, non-apoptotic programmed cell death is initiated through excessive self-digestion and degradation of cellular components if autophagy is activated inappropriately [[112]37, [113]38]. Both cerebral ischemia and ischemia-reperfusion can activate autophagy by inducing high expression of certain genes, including Becn1, LC3-II, and Atg5 [[114]39, [115]40]. To assess the level of autophagy after cerebral ischemia-reperfusion, we carried out global gene profiling using RNA sequencing. We found abnormal levels of calreticulin, Atg3, Atg12, Becn1, Atg5, and Ukl1. Calreticulin, Atg3, and Atg12 were selected for amplification using RT-PCR. Consistent with the results obtained from RNA sequencing, these genes were dramatically elevated 24 h post-reperfusion. These results suggest that autophagy was over-activated by cerebral IRI. Reperfusion after ischemic injury may give rise to two openings of the blood-brain barrier (BBB) during the refractory period [[116]41]. The initial opening phase occurs several hours post-reperfusion and is transient. An irreversible second phase opening occurs 24 h to 72 h post-reperfusion. Consistent with this pathological phenomenon, a therapeutic strategy for ischemic stroke should be carried out as early as possible. In the present study, Canx, Cacna1e, Slc24a2, and Ttr were selected, and their expression levels were examined at 6, 12, 24, and 48 h after reperfusion. The expression of these genes was dynamic during the 48 h following reperfusion, and the peak expression time of each gene was not identical. This suggests that these genes play different role in IRI at different times following ischemia-reperfusion, underscoring the idea that the pathological processes of IRI are complex and diverse. In summary, a global gene expression profile for the hippocampus after cerebral IRI was obtained using RNA sequencing. 156 genes were up-regulated and 26 genes were down-regulated. GO analysis and KEGG pathway analysis suggested that differentially expressed genes were involved in inflammation, oxidative stress, ER stress, apoptosis, autophagy, and metabolism. This study provides new insights into understanding the complicated molecular mechanisms of cerebral IRI. Further elaboration of the differentially expressed genes and relevant pathways will be beneficial for the prevention and treatment of cerebral IRI. MATERIALS AND METHODS Experimental animals Male Wistar rats, aged 6–8 weeks, weighing 230–280 g, were purchased from Pengyue Experimental Animal Breeding Institute of Jinan, China (license no. SCXK (Lu) 2014-0007). The rats were housed in a controlled environment of 22–26°C, 50–60% humidity, and a 12 h light/dark cycle. All animal experiments were approved by the Ethics Committee of Jining Medical University and performed in accordance with the international Health Guide for the Care and Use of Laboratory Animals. Middle cerebral artery occlusion (MCAO) model The rat middle cerebral artery occlusion (MCAO) model was established as follows: Rats were anesthetized using 10% chloral hydrate (0.3 mL/100 g) through intraperitoneal injection, and fixed in the supine position. The right common carotid artery was isolated, and a 0.26 mm diameter fishing line was inserted along the right common carotid arteries into the internal carotid artery until it blocked the origin of the middle cerebral artery. The depth of penetration was approximately 18 mm. The incision was sutured. According to the experimental protocol of our lab [[117]10, [118]42], rats were subjected to focal ischemia for 2 h, and the fishing lines were pulled out post-reperfusion at 2, 6, 12, 24, and 48 h. In the sham group, the right carotid artery was isolated without occlusion of the common carotid arteries. The surgery was performed at 22 ± 2°C. A neurological score was used to evaluate the success of the model. After the neurological score was obtained, the qualifying rats in each group were decapitated, and their hippocampi were isolated and stored on ice for use in the experiments described below. 2, 3, 5-Triphenyl-2H-tetrazolium chloride (TTC) staining Brains were isolated from the 24 h post-reperfusion (IRI/24 h) group and the sham group. Brains were sliced into 2-mm-thick coronal sections. Tissue sections were incubated for 30 min at 37°C in 1% 2, 3, 5-triphenyl-2H-tetrazolium chloride (TTC) solution (Sigma, St. Louis, MO, USA) in the dark. Slices were then collected for imaging. Infarct volume was determined using Image-Pro Plus v 6.0 software. Infarct volume ratio was calculated using the following formula: Infarct volume ratio = (Σ infarct area × thickness) / (Σ brain slices area × thickness) × 100%. RNA extraction and sequencing Total RNA from the IRI/24 h and sham groups was extracted using Trizol reagent (Invitrogen, Carlsbad, USA) and treated with DNase I to degrade contaminating DNA. The quality and concentration of the RNA was evaluated using agarose gel electrophoresis and the BioSpec-nano spectrophotometer (Shimadzu, Japan). Total mRNA was then isolated using oligo (dT), and fragmented into short fragments. First and second strand cDNA synthesis was carried out on individual mRNAs. Double-stranded cDNA was purified and modified with end repair, 3’-end single nucleotide adenine (A) addition, and sequencing adaptors. Finally, the fragments were enriched by PCR amplification to construct a library ready for sequencing using Illumina HiSeq TM 2000 in Shenzhen Huada Genetics Corporation (Shenzhen, China). Bioinformatics analysis Primary sequencing data produced by RNA sequencing, called raw reads, were subjected to quality control (QC). After QC, raw reads were filtered into clean reads. QC of alignment was performed to determine if re-sequencing was needed. If the alignment result passed QC, downstream analysis including gene expression, differentially expressed genes, cluster analysis, Gene Ontology (GO) enrichment analysis, and pathway enrichment analysis was proceeded with. Cluster analysis Distances of expressed genes were calculated using the Euclidean method. The sum of the squared deviations algorithm was used to calculate distance. The cluster analysis of gene expression patterns was performed using java Treeview software. Screening of differentially expressed genes To identify differentially expressed genes when comparing the IRI/24 h and sham groups, the Bonferroni correction was performed on P-values. The thresholds required for the results to be considered significant were as follows: FDR ≤0.001 and absolute value of log^2 Ratio ≥1. Semi-quantitative PCR The extracted total RNA was reverse-transcribed into cDNA using the FastQuant RT kit (Tiangen Biotech, Beijing, China). The cDNAs were then used in amplification reactions with 2× Taq PCR Master Mix (Tiangen Biotech, Beijing, China). β-actin was used as an internal reference gene. The reaction parameters were as follows: 94°C for 3 min, followed by 30 cycles at 94°C for 40 sec, 55°C for 50 sec, and 72°C for 50 sec. The amplification products were separated by electrophoresis on a 1% agarose gel, and analyzed using ImageJ2x software. Oxygen-glucose deprivation (OGD) model SHY-5Y cells were purchased from the Cell Resource Center of the Chinese Academy of Sciences (Shanghai, China). Cells were grown in DMEM medium supplemented with 10% FBS, 100 U/mL penicillin, and 100 μg/mL streptomycin in a humidified atmosphere of 5% CO[2] at 37°C. For the oxygen-glucose deprivation (OGD) model, SHY-5Y cells were divided into two groups: (1) Control group: Cells were cultured in DMEM without OGD; (2) OGD model group: Cells were incubated in Eagle’s medium for 2 h at 37°C in an incubator under 5% O[2], 5% CO[2], and 90% N[2]. Cells were then cultured in fresh medium for 24 h at 37°C in an incubator under 5% CO[2]. Enzyme-linked immunosorbent assay (ELISA) Homogenates of hippocampi from IRI/24 h and sham groups were made and centrifuged at 4°C. The supernatants were collected, and used to detect the levels of TNF-α, IL-6, and ICAM-1 using enzyme-linked immunosorbent assay (ELISA) kits from Cloud-Clone Corp. (Wuhan, China), according to the manufacturer’s instructions. All ELISA analyses were carried out in triplicate. The concentrations of TNF-α, IL-6, and ICAM-1 were determined according to a standard curve. GO enrichment analysis GO enrichment was used to note the function of differentially expressed genes via the GO database ([119]http://www.geneontology.org/). A Bonferroni correction was used to obtain a corrected P-value. GO terms with P-values ≥0.5 were regarded as significantly enriched in the target candidates. After obtaining GO annotation for differentially expressed genes, WEGO software was used for GO functional classification of differentially expressed genes [[120]43]. Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database ([121]http://www.genome.jp/kegg/) was used to determine pathway enrichment of differentially expressed genes compared with the reference gene background. Genes with FDR ≤0.05 were regarded as significantly enriched. In addition, a scatter plot for KEGG enrichment results was generated. Statistical analysis All data were presented as mean ± SEM and analyzed using GraphPad Prism 5.0 software. Significance was determined using Student’s t-test. Differences between group means with P < 0.05 were considered statistically significant. Acknowledgments