ObjectiveTo systematically profile and characterize the circular RNA (circRNA) and microRNA (miRNA) expression pattern in the lesion epicenter of spinal tissues after traumatic spinal cord injury (TSCI) and predict the structure and potential functions of the regulatory network.MethodsForty-eight adult male C57BL/6 mice (weighing, 18-22 g) were randomly divided into the TSCI (n=24) and sham (n=24) groups. Mice in the TSCI group underwent T8-10 vertebral laminectomy and Allen’s weight-drop spinal cord injury. Mice in the sham group underwent the same laminectomy without TSCI. The spinal tissues were harvested after 3 days. Some tissues were stained with HE staining to observe the structure. The others were used for sequencing. The RNA-Seq, gene ontology (GO) analysis, and circRNA-miRNA network analyses (TargetScan and miRanda) were used to profile the expression and regulation patterns of network of mice models after TSCI.ResultsHE staining showed the severe damage to the spinal cord in TSCI group compared with sham group. A total of 17 440 circRNAs and 1 228 miRNAs were identified. The host gene of significant differentially expressed circRNA enriched in the cytoplasm, associated with positive regulation of transcription and protein phosphorylation. mmu-miR-21-5p was the most significant differentially expressed miRNA after TSCI, and circRNA6730 was predicted to be its targeted circRNA. Then a potential regulatory circRNA-miRNA network was constructed.ConclusionThe significant differentially expressed circRNAs and miRNAs may play important roles after TSCI. A targeted interaction network with mmu-miR-21-5p at the core of circRNA6730 could provide basis of pathophysiological mechanism, as well as help guide therapeutic strategies for TSCI.
Recent studies showed that certain drugs can change regulatory reaction parameters in gene regulatory networks (GRNs) and therefore restore pathological cells to a normal state. A state control framework for regulating biological networks has been built based on attractors and bifurcation theory to analyze this phenomenon. However, the control signal is self-developed in this framework, of which the parameter perturbation method can only calculate the state transition time of cells with single control variable. Therefore, an optimal control method based on the dynamic optimization algorithms is proposed for complex biological networks modeled by nonlinear ordinary differential equations (ODEs). In this approach, dynamic optimization problems are constructed based on basic characteristics of the biological networks. Furthermore, using an example of a simple low-dimensional three-node GRN and a complex high-dimensional cancer GRN, MATLAB is utilized to calculate optimal control strategies with either single or multiple control variables. This method aims to achieve accurate and rapid state regulation for biological networks, which can provide a reference for experimental researches and medical treatment.
ObjectiveTo explore the mechanism of paucigranulocytic asthma and to find therapeutic target for paucigranulocytic asthma.MethodsGSE143303 data and platform information were downloaded from GEO. Gene Set Enrichment Analysis were performed to construct positive and negative gene-gene interaction network correlation with paucigranulocytic asthma. Differential expression analysis, pathway commonality analysis were performed with R language.ResultsGSE143303 data set contained 47 endobronchial biopsies from adult (16 cases of paucigranulocytic asthma, 13 cases of healthy control). Compared with control group, the paucigranulocytic asthma group had 115 differential genes set (37 positive and 78 negative). The results of pathway commonality analysis showed that the crosslink existed within the negative gene-gene interaction network correlation with paucigranulocytic asthma. Among these, most of the genes belonged to the protein HLA gene family. Differential expression analysis show that HLA-DQB1, HLA-DRB5 were differential genes and TNFRSF13B was significantly downregulated genes in the intersect genes.ConclusionTNFRSF13B, HLA-DQB1, HLA-DRB5 and regulatory networks associated with them are the crucial factors contributing to paucigranulocytic asthma.