Autophagic Network Analysis of the Dual Effect of Sevoflurane on Neurons Associated with GABARAPL1 and 2

Update on the Mechanism and Treatment of Sevoflurane-Induced Postoperative Cognitive Dysfunction

Sevoflurane is one of the most widely used anesthetics for the induction and maintenance of general anesthesia in surgical patients. Sevoflurane treatment may increase the incidence of postoperative cognitive dysfunction (POCD), and patients with POCD exhibit lower cognitive abilities than before the operation. POCD affects the lives of patients and places an additional burden on patients and their families. Understanding the mechanism of sevoflurane-induced POCD may improve prevention and treatment of POCD. In this paper, we review the diagnosis of POCD, introduce animal models of POCD in clinical research, analyze the possible mechanisms of sevoflurane-induced POCD, and summarize advances in treatment for this condition.

Analysis of the Efficacy and Mechanism of Action of Xuebijing Injection on ARDS Using Meta-Analysis and Network Pharmacology

Objective

Acute respiratory distress syndrome (ARDS) is defined as the acute onset of noncardiogenic edema and subsequent gas-exchange impairment due to a severe inflammatory process known as cytokine storm. Xuebijing injection (hereinafter referred to as Xuebijing) is a patent drug that was used to treat ARDS or severe pneumonia (SP) in China. However, its efficacy and mechanism of actions remain unclear. In this study, we used meta-analysis and network pharmacology to assess these traits of Xuebijing.

Methods

We searched PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), and Wanfang databases for randomized controlled trials (RCTs) that evaluated Xuebijing therapy for ARDS or SP. The outcomes were total mortality, intensive care unit (ICU) stay time, and TNF-α and IL-6 levels. We performed a meta-analysis using RevMan 5.3 software. The putative targets, top 10 proteins, and possible pathway of Xuebinjing on ARDS were analyzed by network pharmacology. TNF-α and IL-6 were further docked with the six main active components of Xuebinjing using AutoDock 4.2.6 and PyMol 1.5.0.3 software.

Results

Fifteen RCTs involving 2778 patients (13 ARDS and 2 SP) were included. Compared with the control, Xuebijing treatment significantly reduced the mortality rate (risk ratio, 0.64 (95% credible interval (CrI), 0.54-0.77)), reduced the ICU stay time (mean difference (MD), -4.51 (95% CrI, -4.97--4.06)), reduced the TNF-α ((MD), -1.23 (95% CrI, -1.38--1.08)) and IL-6 ((MD), -1.15 (95% CrI, -1.52--0.78)) levels. The 56 putative targets, top 10 proteins (MAPK1 (mitogen-activated protein kinase 1), MAPK8 (mitogen-activated protein kinase 8), RELA (transcription factor p65), NFKB1 (nuclear factor NF-kappa-B p105 subunit), JUN (transcription factor AP-1), SRC (proto-oncogene tyrosine-protein kinase), TNF (tumor necrosis factor), HRAS (GTPase HRas), IL6 (interleukin-6), and APP (amyloid-beta A4 protein)), and possible pathways (Ret tyrosine kinase, IL2-mediated signaling events, CD4+/CD8+ T cell-related TCR signaling, p75(NTR)-mediated signaling, CXCR4-mediated signaling events, LPA receptor-mediated events, IL12-mediated signaling events, FAS (CD95) signaling pathway, and immune system) of Xuebinjing's action on ARDS were obtained. The molecular docking results showed that all the six components of Xuebinjing docked with TNF-α, and two components docked with IL-6 got the binding energies lower than -5.

Conclusion

Our results recommended Xuebijing treatment for patients with ARDS. Xuebijing has therapeutic effects on ARDS patients partly by regulating the immune cell/cytokine pathways and thus inhibiting the cytokine storm. TNF-α is the cytokine both directly and indirectly inhibited by Xuebijing, and IL-6 is the cytokine mainly indirectly inhibited by Xuebijing.

Tracing the footsteps of autophagy in computational biology

Autophagy plays a crucial role in maintaining cellular homeostasis through the degradation of unwanted materials like damaged mitochondria and misfolded proteins. However, the contribution of autophagy toward a healthy cell environment is not only limited to the cleaning process. It also assists in protein synthesis when the system lacks the amino acids’ inflow from the extracellular environment due to diet consumptions. Reduction in the autophagy process is associated with diseases like cancer, diabetes, non-alcoholic steatohepatitis, etc., while uncontrolled autophagy may facilitate cell death. We need a better understanding of the autophagy processes and their regulatory mechanisms at various levels (molecules, cells, tissues). This demands a thorough understanding of the system with the help of mathematical and computational tools. The present review illuminates how systems biology approaches are being used for the study of the autophagy process. A comprehensive insight is provided on the application of computational methods involving mathematical modeling and network analysis in the autophagy process. Various mathematical models based on the system of differential equations for studying autophagy are covered here. We have also highlighted the significance of network analysis and machine learning in capturing the core regulatory machinery governing the autophagy process. We explored the available autophagic databases and related resources along with their attributes that are useful in investigating autophagy through computational methods. We conclude the article addressing the potential future perspective in this area, which might provide a more in-depth insight into the dynamics of autophagy.