In this study, we integrated the gene expression data of sepsis to reveal more precise genome-wide expression signature to shed light on the pathological mechanism of sepsis. Differentially expressed genes via integrating five microarray datasets from the Gene Expression Omnibus database were obtained. The gene function and involved pathways of differentially expressed genes (DEGs) were detected by GeneCodis3. Transcription factors (TFs) targeting top 20 dysregulated DEGs (including up- and downregulated genes) were found based on the TRANSFAC. A total of 1339 DEGs were detected including 788 upregulated and 551 downregulated genes. These genes were mostly involved in DNA-dependent transcription regulation, blood coagulation, and innate immune response, pathogenic escherichia coli infection, epithelial cell signaling in helicobacter pylori infection, and chemokine signaling pathway. TFs bioinformatic analysis of 20 DEGs generated 374 pairs of TF-target gene involving 47 TFs. At last, we found that five top ten upregulated DEGs (S100A8, S100A9, S100A12, PGLYRP1 and MMP9) and three downregulated DEGs (ZNF84, CYB561A3 and BST1) were under the regulation of three hub TFs of Pax-4, POU2F1, and Nkx2-5. The identified eight DEGs may be regarded as the diagnosis marker and drug target for sepsis.
Let G be a connected graph. A set of vertices [Formula: see text] is called subverted from G if each of the vertices in S and the neighbor of S in G are deleted from G. By G/S we denote the survival subgraph that remains after S is subverted from G. A vertex set S is called a cut-strategy of G if G/S is disconnected, a clique, or ø. The vertex-neighbor-scattering number of G is defined by [Formula: see text], where S is any cut-strategy of G, and ø(G/S) is the number of components of G/S. It is known that this parameter can be used to measure the vulnerability of spy networks and the computing problem of the parameter is NP-complete. In this paper, we discuss the vertex-neighbor-scattering number of bipartite graphs. The NP-completeness of the computing problem of this parameter is proven, and some upper and lower bounds of the parameter are also given.
Betaine is a well-established supplement used in livestock feeding. In our previous study, betaine was shown to result in the redistribution of body fat, a healthier steatosis phenotype, and an increased liver weight and triglyceride storage of the Landes goose liver, which is used for foie-gras production. However, these effects are not found in other species and strains, and the underlying mechanism is unclear. Here, we studied the underpinning molecular mechanisms by developing an in vitro fatty liver cell model using primary Landes goose hepatocytes and a high-glucose culture medium. Oil red-O staining, a mitochondrial membrane potential assay, and a qRT-PCR were used to quantify lipid droplet characteristics, mitochondrial β-oxidation, and fatty acid metabolism-related gene expression, respectively. Our in vitro model successfully simulated steatosis caused by overfeeding. Betaine supplementation resulted in small, well-distributed lipid droplets, consistent with previous experiments in vivo. In addition, mitochondrial membrane potential was restored, and gene expression of fatty acid synthesis genes (e.g., sterol regulatory-element binding protein, diacylglycerol acyltransferase 1 and 2) was lower after betaine supplementation. By contrast, the expression of lipid hydrolysis transfer genes (mitochondrial transfer protein and lipoprotein lipase) was higher. Overall, the results provide a scientific basis and theoretical support for the use of betaine in animal production.
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