Background: Our previous study described the important regulatory roles of microRNAs (miRNAs) in ischemic stroke. However, the functional significance of long non coding RNA (lncRNAs) in ischemic stroke was largely unknown. This study was aimed to identify the lncRNA profiling and elucidate the regulatory mechanisms in pathophysiology of stroke. Materials and methods: Herein, we used RNA sequencing to sequence the RNA from blood of three ischemic stroke patients and three normal controls. Differential expression analysis was performed to identify differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs). After further correlation and co-expression analysis, the corresponding co-expression networks and miRNA-lncRNA-mRNA interaction network were then constructed. The expression of DElncRNAs and DEmRNAs was verified in Gene Expression Omnibus. Results: RNA sequencing and following bioinformatics analysis led to a total of 61 DElncRNAs (14 up-regulated and 47 down-regulated) and 673 DEmRNAs (432 up-regulated and 241 down-regulated). LOC105372881 and LOC101929707 were the most highly increased and decreased lncRNAs in ischemic stroke. LncRNA-mRNA co-expression networks were constructed according to 3008 positively co-expressed and 607 negatively co-expressed lncRNA-mRNA pairs. The DElncRNAs may play roles in the pathways of Glycolysis/Gluconeogenesis, Arrhythmogenic right ventricular cardiomyopathy, Adherens junction, Lysosome and Hematopoietic cell lineage by regulating their co-expressed mRNAs. Combined with previous data, a miRNA-lncRNA-mRNA interaction network for ischemic stroke was constructed. Based on GSE22255, the expression of six DElncRNAs (CEBPA-AS1, LINC00884, HCG27, MATN1-AS1, HCG26 and LINC01184) and eleven DEmRNAs (TREML4, AHSP, PI3, TESC, ANXA3, OAS1, OAS2, IFI6, ISG15, IFI44L and LY6E) was similar to our sequencing data. Conclusions: Our study was the first time to identify blood lncRNAs in human ischemic stroke by RNA-seq. Our findings might be the foundation for understand the potential roles of lncRNAs in ischemic stroke.

Stroke (ST), endangering human health due to its high incidence and high mortality, is a global public health problem. There is increasing evidence that there is a link between the gut microbiota (GM) and neuropsychiatric diseases. We aimed to find the GM of ST, post-ST cognitive impairment (PSCI), and post-ST affective disorder (PSTD). GM composition was analyzed, followed by GM identification. Alpha diversity estimation showed microbiota diversity in ST patients. Beta diversity analysis showed that the bacterial community structure segregated differently between different groups. At the genus level, ST patients had a significantly higher proportion of Enterococcus and lower content of Bacteroides, Escherichia-Shigella, and Megamonas. PSCI patients had a significantly higher content of Enterococcus, Bacteroides, and Escherichia-Shigella and a lower proportion of Faecalibacterium compared with patients with ST. Patients with PSTD had a significantly higher content of Bacteroides and Escherichia-Shigella and lower content of Enterococcus and Faecalibacterium. Parabacteroides and Lachnospiraceae were associated with Montreal cognitive assessment score of ST patients. Our study indicated that the characteristic GM, especially Bacteroidetes, could be used as clinical biomarkers of PSCI and PSTD.

The brain continually reorganizes its functional network to adapt to post-stroke functional impairments. Previous studies using static modularity analysis have presented global-level behavior patterns of this network reorganization. However, it is far from understood how the brain reconfigures its functional network dynamically following a stroke. This study collected resting-state functional MRI data from 15 stroke patients, with mild (n = 6) and severe (n = 9) two subgroups based on their clinical symptoms. Additionally, 15 age-matched healthy subjects were considered as controls. By applying a multilayer temporal network method, a dynamic modular structure was recognized based on a time-resolved function network. The dynamic network measurements (recruitment, integration, and flexibility) were calculated to characterize the dynamic reconfiguration of post-stroke brain functional networks, hence, revealing the neural functional rebuilding process. It was found from this investigation that severe patients tended to have reduced recruitment and increased between-network integration, while mild patients exhibited low network flexibility and less network integration. It's also noted that previous studies using static methods could not reveal this severity-dependent alteration in network interaction. Clinically, the obtained knowledge of the diverse patterns of dynamic adjustment in brain functional networks observed from the brain neuronal images could help understand the underlying mechanism of the motor, speech, and cognitive functional impairments caused by stroke attacks. The present method not only could be used to evaluate patients' current brain status but also has the potential to provide insights into prognosis analysis and prediction.

Introduction: Ischemic stroke (IS) causes severe neurological impairments and physical disabilities and has a high economic burden. Our study aims to identify the key genes and upstream regulators in IS by integrated microarray analysis.Methods: An integrated analysis of microarray studies of IS was performed to identify the differentially expressed genes (DEGs) in IS compared to normal control. Based on these DEGs, we performed the functional annotation and transcriptional regulatory network constructions. Quantitative real‐time polymerase chain reaction (qRT‐PCR) was performed to verify the expression of DEGs.Results: From two Gene Expression Omnibus datasets obtained, we obtained 1526 DEGs (534 up‐regulated and 992 down‐regulated genes) between IS and normal control. The results of functional annotation showed that Oxidative phosphorylation and Alzheimer's disease were significantly enriched pathways in IS. Top four transcription factors (TFs) with the most downstream genes including PAX4, POU2F1, ELK1, and NKX2‐5. The expression of six genes (ID3, ICAM2, DCTPP1, ANTXR2, DUSP1, and RGS2) was detected by qRT‐PCR. Except for DUSP1 and RGS2, the other four genes in qRT‐PCR played the same pattern with that in our integrated analysis.Conclusions: The dysregulation of these six genes may involve with the process of ischemic stroke (IS). Four TFs (PAX4, POU2F1, ELK1 and NKX2‐5) were concluded to play a role in IS. Our finding provided clues for exploring mechanism and developing novel diagnostic and therapeutic strategies for IS.