Zongkai Wu's research while affiliated with Hebei Medical University and other places

This study extensively investigated immune-inflammatory responses following ischemic brain injury using cutting-edge technologies. By analyzing the changes in T cells, neutrophils, and monocytes in patients' blood and identifying key immune cells in necrotic brain zones, the study uncovered crucial insights into the dynamics of immune cells during brain injury. Moreover, targeting the Hspa8 gene effectively reduced neutrophil infiltration and ROS production, suggesting a promising therapeutic approach for alleviating ischemic brain injury. Overall, the study not only provided new theoretical perspectives but also proposed a potential strategy for managing functional decline in ischemic brain injury.

Acute ischemic stroke (AIS) is a severe disorder characterized by complex pathophysiological processes, which can lead to disability and death. This study aimed to determine necroptosis-associated genes in Acute ischemic stroke (AIS) and to investigate their potential as diagnostic and therapeutic targets for AIS. Expression profiling data were acquired from the Gene Expression Omnibus database, and necroptosis-associated genes were retrieved from GeneCards. The differentially expressed genes (DEGs) and necroptosis-related genes were intersected to obtain the necroptosis-related DEGs (NRDEGs) in AIS. In AIS, a total of 76 genes associated with necroptosis (referred to as NRDEGs) were identified. Enrichment analysis of these genes revealed that they were primarily enriched in pathways known to induce necroptosis. Using Weighted gene co-expression network analysis (WGCNA), five co-expression modules consisting of NRDEGs were identified, along with two modules that exhibited a strong correlation with AIS. Protein-protein interaction (PPI) analysis resulted in the identification of 20 hub genes. The Least absolute shrinkage and selection operator (LASSO) regression model demonstrated promising potential for diagnostic prediction. The receiver operating characteristic (ROC) curve validated the diagnostic model and selected nine characteristic genes that exhibited statistically significant differences (p < 0.05). By employing consensus clustering, distinct patterns of necroptosis were identified using these nine signature genes. The results were verified by quantitative PCR (qPCR) in HT22 cells and an external data set. Furthermore, the analyzed ceRNA network included nine lncRNAs, six miRNAs, and three mRNAs. Overall, this study offers novel insights into the molecular mechanisms underlying NRDEGs in AIS. The findings provide valuable evidence and contribute to our understanding of the disease.

Background: Ligustrazine is a traditional Chinese herbal medicine that has long been used to treat cerebral ischemic disorders. However, the molecular mechanisms of ligustrazine in cerebral ischemia/reperfusion (I/R) damage have not been clear elucidated. The aim of this study was to examine the neuroprotective mechanisms of ligustrazine in cerebral I/R. Methods: 9 C57BL/6 mice were randomly divided to three groups: Sham group (n=3), Middle cerebral artery occlusion (MCAO) group (n=3), and MCAO+ Ligustrazine group (n=3). The neurological deficit score was evaluated, the cerebral infarct volume was measured by triphenylterazolium chloride (TTC) staining. Differentially expressed (DE) messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) were analyzed using the R package DEseq2 based on P-value < 0.05 and Log2 |fold change (FC)| ≥ 2 in sham group vs MCAO group and MCAO group vs ligustrazine group by high-throughput sequencing. Function enrichment analysis, the protein-protein interaction (PPI) of neurogenesis related genes were performed. The neurogenesis related competitive endogenous RNA (ceRNA) network was constructed. Results: The expression of circ_0008146 was considerably higher in the MCAO group than the Sham group, and ligustrazine treatment markedly decreased the expression of circ_0008146 in MCAO. Next, the circ_0008146 ceRNA network was established, including circ_0008146-miR-709-Cx3cr1 ceRNA network. Besides, real time quantitative polymerase chain reaction (RT-qPCR) assay identified that miR-709 expression was considerably lower and Cx3cr1 expression was higher in the MCAO group than Sham group, and ligustrazine treatment markedly increased the miR-709 expression and reduced Cx3cr1 expression in MCAO. Further, silencing of circ_0008146 inhibited the concentration of Interleukin 6 (IL-6), Tumor Necrosis Factor alpha (TNF-α) and reduced neuron cell death and up-regulated miR-709 expression and down-regulated Cx3cr1 expression in Lipopolysaccharide (LPS) induced BV-2 cells. Dual-Luciferase reporter gene assay verified that circ_0008146 targeted miR-709. Conclusion: Ligustrazine targets circ_0008146/miR-709/Cx3cr1 axis to inhibit cell apoptosis and inflammation after cerebral ischemia/reperfusion injury.

Background: Known as a disease associated with high mortality, disability and a significant financial burden, ischemic stroke ranks as one of the three diseases threatening human health. Recent advances in omics technology created opportunities to uncover the mechanism in ischemic stroke occurrence and treatment. In this study, we aimed to construct the competitive endogenous RNA (ceRNA) networks of ischemic stroke treated by oxymatrine intervention. Method: The middle cerebral artery occlusion (MCAO) mouse model of ischemic stroke was constructed, and oxymatrine was administered. Then RNA-Sequencing was performed and integrated analysis of mRNAs, lncRNAs and circRNAs was conducted to reveal the pharmacology of oxymatrine. Functional enrichment analysis was performed to explore the underlying mechanism of differentially expressed (DE) mRNAs. The protein-protein interaction (PPI) network of neurogenesis-related genes and long noncoding RNAs (lncRNAs)/circular RNAs (circRNAs) based ceRNA networks were constructed. Results: First, this study revealed the DE-mRNAs, DE-lncRNAs and DE-circRNAs between Oxymatrine treated group and the MCAO group. Then, the common 1231 DE-mRNAs, 32 DE-lncRNAs and 31 DE-circRNAs with opposite trends were identified. The Kyoto Encyclopedia of Genes and Genomes to identify the functional enrichment of 1231 DE-mRNAs were enriched in neurogenesis-related biological processes. Based on neurogenesis-related DE-mRNAs, the PPI network was constructed, and hub genes were identified based on centrality. Finally, both the lncRNA-based and circRNAs-based ceRNA networks were constructed. Conclusion: In summary, this study identified novel coding and noncoding ischemic stroke targets of oxymatrine-treated MCAO. Most importantly, we identified lncRNAs and circRNAs candidates as potential oxymatrine targets and constructed the neurogenesis-related ceRNA networks.

Background: Acute ischemic stroke (AIS) is a severe neurological disease with complex pathophysiology, resulting in the disability and death. The goal of this study is to explore the underlying molecular mechanisms of AIS and search for new potential biomarkers and therapeutic targets. Methods: Integrative analysis of mRNA and miRNA profiles downloaded from Gene Expression Omnibus (GEO) was performed. We explored differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMirs) after AIS. Target mRNAs of DEMirs and target miRNAs of DEGs were predicted with target prediction tools, and the intersections between DEGs and target genes were determined. Subsequently, Gene Ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses, Gene set enrichment analysis (GSEA), Gene set variation analysis (GSVA), competitive endogenous RNA (ceRNA) (lncRNA-miRNA-mRNA) network, protein–protein interaction (PPI) network, and gene transcription factors (TFs) network analyses were performed to identify hub genes and associated pathways. Furthermore, we obtained AIS samples with evaluation of immune cell infiltration and used CIBERSORT to determine the relationship between the expression of hub genes and infiltrating immune cells. Finally, we used the Genomics of Drug Sensitivity in Cancer (GDSC) database to predict the effect of the identified targets on drug sensitivity. Result: We identified 293 DEGs and 26 DEMirs associated with AIS. DEGs were found to be mainly enriched in inflammation and immune-related signaling pathways through enrichment analysis. The ceRNA network included nine lncRNAs, 13 miRNAs, and 21 mRNAs. We used the criterion AUC >0.8, to screen a 3-gene signature (FBL, RPS3, and RPS15) and the aberrantly expressed miRNAs (hsa-miR-125a-5p, hsa-miR-125b-5p, hsa-miR-148b-3p, and hsa-miR-143-3p) in AIS, which were verified by a method of quantitative PCR (qPCR) in HT22 cells. T cells CD8, B cells naïve, and activated NK cells had statistical increased in number compared with the acute cerebral infarction group. By predicting the IC50 of the patient to the drug, AZD0530, Z.LLNle.CHO and NSC-87877 with significant differences between the groups were screened out. AIS demonstrated heterogeneity in immune infiltrates that correlated with the occurrence and development of diseases. Conclusion: These findings may contribute to a better understanding of the molecular mechanisms of AIS and provide the basis for the development of novel treatment targets in AIS.

Objective. With the growing incidence of ischemic stroke worldwide, there is an urgent need to identify blood biomarkers for ischemic stroke patients. Thus, our aim was to identify potential circulating microRNA (miRNA) as a potential biomarker and to explore its potential mechanism for ischemic stroke in rats. Methods. The mRNA dataset GSE97537 and miRNA dataset GSE97532 were downloaded from the Gene Expression Omnibus (GEO) GSE97537 including 7 middle cerebral artery occlusion (MCAO) rat brain tissues and 5 sham-operated rat brain tissues GSE97532 including 6 MCAO rat blood samples and 3 sham-operated rat blood samples. Differentially expressed mRNAs and miRNAs with corrected value ≤ 0.01 and fold change ≥2 or ≤0.05 were identified. To explore potential biological processes and pathways of differentially expressed mRNAs, functional enrichment analysis was performed. The target mRNAs of differentially expressed miRNAs were predicted using DNA Intelligent Analysis (DIANA)-microT tools. The target mRNAs and differentially expressed mRNAs were intersected. Results. 1228 differentially expressed mRNAs in MCAO rat brain tissues were identified. Highly expressed mRNAs were mainly enriched in the inflammatory responses. Nine differentially expressed miRNAs were identified in MCAO rat blood samples. A total of 673 target mRNAs were predicted to significantly bind these differentially expressed miRNAs. Among them, 54 target mRNAs were differentially expressed in MCAO rat blood samples. Enrichment analysis results showed that these 54 target mRNAs were closely related to neurological diseases and immune responses. Among all miRNA-mRNA relationship, miR-3552-CASP3 interaction was identified, indicating that CASP3 might be mediated by miR-3552. Functional enrichment analysis revealed that CASP3 was involved in the apoptosis pathway, indicating that miR-3552 might participate in apoptosis by CASP3. Conclusion. Our findings reveal that circulating miR-3552 shows promise as a potential biomarker for ischemic stroke in rats. 1. Introduction Stroke is the third leading cause of death after heart disease and cancer, classified as ischemic (85%) and hemorrhagic (15%) [1]. The world causes more than 6 million deaths each year. Ischemic stroke occurs when cerebral blood flow is interrupted, usually due to arterial thrombosis or embolism. In addition to ischemia, reperfusion also impairs the brain after ischemia, including inflammation and oxidative stress. Ischemic stroke poses a major threat to patient health and quality of life. However, the pathogenic mechanisms of ischemic stroke remain to be clarified, leading to an ideal clinical treatment [2, 3]. Therefore, understanding the molecular neuroinflammatory mechanism of ischemic stroke can significantly enhance the development of treatment. Prognostic blood biomarkers provide a novel strategy for the diagnosis and prognosis of stroke [4]. Currently, the diagnosis of ischemic stroke usually relies on computed tomography. The diagnostic value of blood biomarkers in ischemic stroke remains limited. MicroRNAs (miRNAs), a class of noncoding RNAs, play an essential role in neurological diseases, including ischemic stroke [5]. Increasing evidence demonstrates that miRNAs participate in various biological processes, such as proliferation, apoptosis, and inflammatory response [6–9]. MiRNAs may regulate gene expression at the posttranscriptional level by binding 3-UTR of target mRNAs [10, 11]. Due to the stability of miRNAs in peripheral blood and specific expression patterns in cell types, miRNAs show promise as diagnostic and prognostic markers of ischemic stroke [12]. Ischemic stroke may affect the expression levels of circulating miRNAs through various pathophysiological processes such as immune and inflammatory responses [13]. Therefore, it is necessary to explore the pathophysiological processes involved in miRNAs. It has been reported that serum miRNA-mRNA interactions play critical roles in the progression of ischemic stroke [14]. High-throughput sequencing technology of miRNAs and mRNAs for ischemic stroke both in animal models and Homo sapiens has been conducted [15–17]. However, the relationships between miRNAs and mRNAs remain to be clarified. GEO, an online public database, is provided by the National Center for Biotechnology Information in 2000, which has become one of the most comprehensive gene expression data resources available. Based on this database, we performed a comprehensive analysis for gene expression pattern both in the MCAO rat blood and tissues samples that have been widely used as classical models for stroke. 2. Materials and Methods 2.1. Expression Data and Data Preprocessing The mRNA dataset GSE97537 and miRNA dataset GSE97532 [18] were retrieved from the GEO (https://www.ncbi.nlm.nih.gov/geo) database. GSE97537 was generated on GPL1355 [Rat230_2] Affymetrix Rat Genome 230 2.0 Array platform. A total of 7 brain samples from MCAO rats and 5 brain samples from sham-operated rats were included in the GSE97537 dataset. GSE97532 was on the basis of GPL21572 [miRNA-4] Affymetrix Multispecies miRNA-4 Array [ProbeSet ID version] platform. Totally, of 6 blood samples from MCAO rats and 3 blood samples from sham-operated rats were included in the GSE97532 dataset. Expression data of probes were annotated as corresponding gene or miRNA symbols on the basis of GPL1355 or GPL21572 platform, respectively. For one gene or miRNA corresponding to multiprobes, the maximum expression value was considered as the expression value of this gene or miRNA. After that, the expression matrix was constructed. For GSE97537 dataset, there were a total of 31099 probes in each sample (Supplementary Table 1). After preprocessing, 15239 genes were obtained. A total of 1277 miRNAs were included in GSE97532 dataset (Supplementary Table 2). Following preprocessing, 541 miRNAs were retained for further analysis. 2.2. Differential Expression Analysis The differentially expressed mRNAs between 7 MCAO rat brain tissues and 5 sham-operated rat brain tissues, and miRNAs between 6 MCAO rat blood samples and 3 sham-operated rat blood samples were analyzed by Linear Models for Microarray Data (limma) package (version 3.34.7; https://bioconductor.org/packages/release/bioc/html/limma.html), respectively. Then, fold change and corrected value were generated by the limma package [19]. The corrected value ≤ 0.01 and log2|fold change| ≥2 or ≤0.05 were set as thresholds. 2.3. Functional Enrichment Analysis Gene set enrichment analysis (GSEA: http://software.broadinstitute.org/gsea/index.jsp) was performed to explore potential functions associated differentially expressed mRNAs [20]. value = 0.01, value = 0.01, and Jacard >0.375 were set as the cut-off criterion. Gene ontology (GO) database was used to annotate gene functions [21]. GO function terms include molecular function (MF), biological process (BP), and cellular component (CC). Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis [22] was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID; version 6.8; (https://david.ncifcrf.gov/)) [23]. value < 0.05 was considered to be significantly enriched. 2.4. RT-qPCR Assay 14 rats were purchased from Shanghai Experimental Animal Center (Shanghai, China). These rats were randomly separated into MCAO () and sham operation (). The MCAO model was conducted, as previously described [18]. 24 hours after the reperfusion, the rats were anesthetized. The blood and brain tissue samples were collected. Total RNA was extracted using Trizol (solarbio, Beijing, China), which was reverse transcribed into cDNA utilizing reverse transcription kit (YEASEN, Shanghai, China). RT-qPCR was carried out by qPCR kit (YEASEN). The relative expression levels were determined with the 2–ΔΔCt method. Our research was approved by the Ethics Committee of Hebei General Hospital. 2.5. miRNA Target Prediction The targets of differentially expressed miRNAs were predicted using DIANA-microT tools that identify mRNA targets for mouse miRNAs [24]. Furthermore, the targeted mRNAs of the interested miRNA was predicted using TargetScan [25], miRTarBase [26], miRDB [27], miRanda [28], and miRMap [29] databases. The miRNA-mRNA interaction network was visualized using Cytoscape software [30]. 3. Results 3.1. Identification of Differentially Expressed mRNAs between MCAO Rat Brain Tissues and Sham-Operated Rat Brain Tissues The similarity test of the samples was carried out to determine whether there was a difference in similarity between the samples at the expression level. Using the correlation coefficient matrix, we found the differences in transcription levels between 7 MCAO rat brain tissues and 5 sham-operated rat brain tissues (Figure 1). Therefore, further downstream analysis can be performed.