Heparin-Binding Protein Promotes Acute Lung Injury in Sepsis Mice by Blocking the Aryl Hydrocarbon Receptor Signaling Pathway.

Purpose: This study aimed to explore the therapeutic effect and potential mechanism of heparin-binding protein (HBP) reduction on sepsis-related acute lung injury. Methods: We utilized a murine model of sepsis-induced by intraperitoneal injection of lipopolysaccharides (LPS) in C57BL/6J mice divided into four groups: Control, LPS, Anti-HBP, and ceftriaxone (CEF). Following sepsis induction, Anti-HBP or CEF treatments were administered, and survival rates were monitored for 48 h. We then used reverse-transcription quantitative PCR to analyze the expression levels of HBP in lung tissues, immunohistochemistry for protein localization, and Western blotting for protein quantification. Pulmonary inflammation was assessed using enzyme-linked immunosorbent assays of proinflammatory cytokines (tumor necrosis factor-α, interleukin [IL]-1ß, IL-6, and interferon-γ). The activation state of the aryl hydrocarbon receptor (AhR) signaling pathway was determined via Western blotting, evaluating both cytoplasmic and nuclear localization of AhR and the expression of cytochrome P450 1A1 protein by its target gene. Results: Anti-HBP specifically reduced HBP levels. The survival rate of mice in the Anti-HBP and CEF groups was much higher than that in the LPS group. The severity of lung injury and pulmonary inflammatory response in the Anti-HBP and CEF groups was significantly lower than that in the LPS group. AhR signaling pathway activation was observed in the Anti-HBP and CEF groups. Additionally, there was no significant difference in the above indices between the Anti-HBP and CEF groups. Conclusion: HBP downregulation in lung tissues significantly improved LPS-induced lung injury and the pulmonary inflammatory response, thereby prolonging the survival of sepsis mice, suggesting activation of the AhR signaling pathway. Moreover, the effect of lowering the HBP level was equivalent to that of the classical antibiotic CEF. Trial Registration: Not applicable.

Predicting protein secondary structure by a support vector machine based on a new coding scheme.

Protein structure prediction is one of the most important problems in modern computational biology. Protein secondary structure prediction is a key step in prediction of protein tertiary structure. There have emerged many methods based on machine learning techniques, such as neural networks (NN) and support vector machine (SVM) etc., to focus on the prediction of the secondary structures. In this paper, a new method was proposed based on SVM. Different from the existing methods, this method takes into account of the physical-chemical properties and structure properties of amino acids. When tested on the most popular dataset CB513, it achieved a Q(3) accuracy of 0.7844, which illustrates that it is one of the top range methods for protein of secondary structure prediction.