基于网络药理学挖掘巴西人参的核心功效＊

目的 基于网络药理学方法挖掘巴西人参核心药效，结合动物实验验证其抗疲劳作用。方法 通过文献检索方法收集巴西人参的化学成分，运用Discovery studio软件筛选类药性成分，通过Swiss target Prediction数据库对类药性成分预测分析关键靶点，并对预测靶点进行蛋白质相互作用关系分析，采用Cytoscape 3.6.1软件中的MCODE插件筛选出PPI网络中的核心靶点，通过DAVID数据库对核心靶点进行生物信息学分析。将小鼠随机分为空白对照组、阳性对照组、巴西人参高、低剂量组，各组小鼠给药15天后进行转棒实验及负重游泳实验，检测小鼠血清乳酸、肝糖原含量，考察各组小鼠抗疲劳能力。结果 网络药理学结果提示巴西人参主要有调节甾体类激素以及物质代谢的作用；巴西人参高剂量组能够显著延长小鼠的转棒疲劳时间以及力竭游泳时间（P < 0.05或P < 0.01）；巴西人参高、低剂量组均能够显著降低血清中乳酸堆积、增加肝糖原的含量（P < 0.05或P < 0.01）。结论 巴西人参具有多成分、多靶点、多途径的作用特点，其核心功效可能为抗疲劳以及增强性功能作用；动物实验结果表明巴西人参具有良好抗疲劳效果，其机制可能为促进机体内乳酸的转化，增加体内储能物质即肝糖原的储备。     

Network vaccinology

The structure and function of the immune system is governed by complex networks of interactions between cells and molecular components. Vaccination perturbs these networks, triggering specific pathways to induce cellular and humoral immunity. Systems vaccinology studies have generated vast data sets describing the genes related to vaccination, motivating the use of new approaches to identify patterns within the data. Here, we describe a framework called Network Vaccinology to explore the structure and function of biological networks responsible for vaccine-induced immunity. We demonstrate how the principles of graph theory can be used to identify modules of genes, proteins, and metabolites that are associated with innate and adaptive immune responses. Network vaccinology can be used to assess specific and shared molecular mechanisms of different types of vaccines, adjuvants, and routes of administration to direct rational design of the next generation of vaccines.     

基于网络药理学与细胞实验的三白汤治疗皮肤色素沉着机制探究与初证＊

目的 探究并验证三白汤治疗皮肤色素沉着的作用机制。方法 通过TCMSP、TCMID以及BATMAN-TCM数据库收集三白汤化学成分和作用靶点，通过检索GeneCards、DisGeNET数据库获得皮肤色素沉着的疾病靶点。取两靶点集的交集，进而保留相关化学成分，应用Cytoscape建立化学成分-潜在靶点网络。将交集靶点导入STRING数据库，得到交集靶点的相互作用网络。采用CytoHubba、DAVID和Kobas对交集靶点的相互作用网络进行拓扑分析、GO和KEGG富集分析。利用AutoDock 4.2对关键靶点进行分子对接研究。基于B16-F10黑色素瘤细胞，构建抗黑色素模型，验证分子对接结果。结果 筛选获得三白汤活性成分29个、潜在作用靶点23个。拓扑分析得到酪氨酸酶、雌激素受体1、孕激素受体等8个关键靶点，主要参与涉及男性性腺发育、对雌二醇的反应等GO富集分析所示的生命过程。KEGG通路分析得到123条主要信号通路，主要涉及黑色素合成、雌激素信号通路、酪氨酸代谢等。分子对接结果显示，三白汤化学成分与潜在靶点具有较好的结合活性。细胞实验表明，三白汤中代表性成分的抗黑色素活性顺序与分子对接结果一致。结论 三白汤通过其中芍药内酯苷、β-谷甾醇、α-香树精等有效成分，作用于连环蛋白β1（catenin beta 1，CTNNB1）、酪氨酸酶（tyrosinase，TYR）等靶点，调节黑色素合成、酪氨酸代谢等信号通路，最终减少黑色素合成以治疗色素沉着。     

Lower airway bacterial microbiome may influence recurrence after resection of early-stage non–small cell lung cancer

ObjectiveThe lower airway bacterial microbiome influences carcinogenesis and response to immunotherapy in non–small cell lung cancer (NSCLC). We investigated the association of this microbiome with recurrence in early NSCLC.MethodsMicrobiomes of presurgery bronchoalveolar lavage (BAL) and saliva, and resected stage I NSCLC tumor and adjacent lung tissues of 48 patients were examined by 16S gene sequencing. Tumor gene expression was measured by RNA sequencing.ResultsSpatial relationships of the different biospecimen types was reflected in their microbiomes, with microbiomes of BAL intermediate to those of saliva and lung tissue. BAL and saliva microbiomes were less dissimilar in patients with high α-amylase levels in BAL, indicating oral aspiration as a source of lower airway microbiota. BAL microbiomes of patients with recurrence within 32 months of surgery differed from those without recurrence during ≥32 months of follow-up (n = 18 each), despite no difference for age, sex, smoking history, and tumor histology and grade. The recurrence-associated BAL microbiome signature was present in 16 of the 18 recurrence cases but in only two of the others. Signature presence was associated with shorter recurrence-free survival (log-rank test P < .001; hazard ratio = 14.5), and greater expression in tumors of genes for cell proliferation and epithelial mesenchymal transition. Immune cellular composition of the tumor microenvironment was not different between patients with and without the signature.ConclusionsPresurgery composition of lower airway microbiome may be associated with recurrence of early NSCLC. This association may reflect an influence of the microbiome on tumor biology.     

Modèle en réseau et troubles mentaux : application et intérêts dans la dépression post-AVC

In contrast to the classic models in psychopathology, the network model considers that the temporal interactions between symptoms are the causes of their occurrence. This model could also be particularly suitable for understanding the processes involved in post-stroke depression. The aim of this paper is to perform a network analysis in order to describe the temporal dynamic of the links existing between depression symptoms during the acute phase after stroke. Twenty-five patients (64% male, mean age 58.1 ± 14.9 years old) hospitalized for a minor stroke (no neurocognitive or motor impairment) were involved in an Ecological Momentary Assessment methodology-based study. They used a smartphone application in order to complete four brief questionnaires each day during the week after hospital discharge. The questionnaire included 7-point Likert scales to measure the severity of the following depressive symptoms: sadness, anhedonia, fatigue, diminished concentration ability, negative thoughts on oneself, pessimism. We used Multilevel Vector Autoregressive analysis to describe the temporal links between those symptoms. We used the software R 3.6.0 with the mlVAR package. The p-value was set at .05. The results show two independent symptoms networks. The first one involves the anhedonia, fatigue, negative thoughts on oneself and sadness. It shows that: anhedonia predicts the activation of later fatigue (β = 0.135, P = 0.037) and later negative thoughts (β = 0.152, P = 0.019); negative thoughts predict later negative thoughts (β = 0.143, P = 0.028) and later sadness (β = 0.171, P = 0.021); fatigue predicts later fatigue (β = 0.261, P < 0.000). Pessimism and diminished concentration ability compose the second network, and the results show that pessimism predicts later pessimism (β = 0.215, P = 0.012) and later diminished concentration ability (β = 0.178, P = 0.045). On the one hand, anhedonia thus plays an important role in the initial and progressive activation of the other symptoms of its network. On the other hand, the cognitive symptoms (negative thoughts and pessimism) cause the deterioration of the mood and the deficit of attentional abilities. Using behavioral and cognitive strategies to support patients after hospital discharge would reduce the risk of depressive complications after a stroke. This study provides convincing empirical elements for the interest of the network model for research in psychopathology and the clinical implications and perspectives allowed by network analysis.