Using real-world data to predict health outcomes—The prediction design: Application and sample size planning

The gold standard for investigating the efficacy of a new therapy is a (pragmatic) randomized controlled trial (RCT). This approach is costly, time-consuming, and not always practicable. At the same time, huge quantities of available patient-level control condition data in analyzable format of (former) RCTs or real-world data (RWD) are neglected. Therefore, alternative study designs are desirable. The design presented here consists of setting up a prediction model for determining treatment effects under the control condition for future patients. When a new treatment is intended to be tested against a control treatment, a single-arm trial for the new therapy is conducted. The treatment effect is then evaluated by comparing the outcomes of the single-arm trial against the predicted outcomes under the control condition. While there are obvious advantages of this design compared to classical RCTs (increased efficiency, lower cost, alleviating participants’ fear of being on control treatment), there are several sources of bias. Our aim is to investigate whether and how such a design—the prediction design—may be used to provide information on treatment effects by leveraging external data sources. For this purpose, we investigated under what assumptions linear prediction models could be used to predict the counterfactual of patients precisely enough to construct a test and an appropriate sample size formula for evaluating the average treatment effect in the population of a new study. A user-friendly R Shiny application (available at: https://web.imbi.uni-heidelberg.de/PredictionDesignR/ ) facilitates the application of the proposed methods, while a real-world application example illustrates them.     

代谢工程改造大肠杆菌一碳模块高效合成L-甲硫氨酸

L-甲硫氨酸又名L-蛋氨酸,是人体必需8种氨基酸之一,在饲料、医药、食品领域具有重要应用。以实验室前期构建的M2(Escherichia coli W3110?IJAHFEBC/PAM)为出发菌株,以模块化代谢工程策略构建了一株L-甲硫氨酸高产菌株。首先通过过表达亚甲基四氢叶酸还原酶(methylenetetrahydrofolate reductase,MetF)和筛选不同来源的丝氨酸羟甲基转移酶(hydroxymethyltransferase,GlyA),增强了一碳模块甲基供体的生成,优化了一碳模块。随后针对一碳模块的前体供应,过表达了胱醚裂解酶(cysteamine lyase,MalY)和半胱氨酸内运基因(fliY),有效地提高了L-高半胱氨酸和L-半胱氨酸的供应。最终摇瓶发酵L-甲硫氨酸的产量由2.8 g/L提高至4.05 g/L,5 L发酵罐中达到18.26 g/L。研究结果表明,一碳模块对L-甲硫氨酸的生物合成具有十分重要的影响,在细胞内通过优化一碳模块,可以实现L-甲硫氨酸的高效生物合成。本研究为进一步提高微生物发酵生产L-甲硫氨酸的水平奠定了基础。     

非洲猪瘟病毒I226R蛋白抑制cGAS-STING通路介导的天然免疫应答

本研究旨在探究非洲猪瘟病毒(African swine fever virus, ASFV) I226R蛋白(I226R protein, pI226R)抑制cGAS-STING信号通路的作用机制。利用双荧光素酶报告系统和实时荧光定量PCR (real-time quantitative PCR, qPCR)证明pI226R显著抑制cGAS-STING通路介导的I型干扰素及干扰素刺激相关基因的产生。免疫共沉淀及激光共聚焦显微镜试验发现pI226R与cGAS蛋白相互作用。免疫印迹分析证明pI226R通过自噬-溶酶体途径促进cGAS蛋白的降解。同时,pI226R阻碍了cGAS与E3泛素连接酶三基序蛋白56 (tripartite motif protein 56, TRIM56)的结合,导致cGAS的单泛素化减弱,从而抑制了cGAS的活化和cGAS-STING通路的激活。总之,本研究证明ASFV pI226R通过拮抗cGAS进而抑制宿主的抗病毒天然免疫反应,进一步增加了对研究ASFV免疫逃逸机制的理解,为疫苗的研发提供了理论基础。     

基于果蝇口腔感染的肠道训练免疫模型研究（英文）

目的 利用果蝇作为遗传工具从个体和分子层面研究果蝇的训练免疫效应,并为后续深入研究其分子机制提供依据。方法 首先构建无菌果蝇模型,在此基础上构建果蝇成虫及跨发育阶段训练免疫模型,用两种革兰氏阴性菌——胡萝卜软腐欧文氏菌（Erwinia carotovora carotovora 15）及铜绿假单胞菌（Pseudomonas aeruginosa）分别经口腔感染果蝇。在第一次感染完全消退后进行再次感染,然后通过比较果蝇在两个感染阶段的存活率和细菌量来衡量训练免疫的潜在效果。通过实时荧光定量PCR检测相应先天免疫相关基因的表达水平,研究革兰氏阴性菌对免疫缺陷（IMD）通路的诱导作用。结果 果蝇成虫及幼虫初次感染均可提高二次感染后的生存率、细菌清除效率及死亡时能承受的最高细菌负荷;二次感染的果蝇中,IMD通路中免疫反应基因的基础表达比未感染的高,这提供了获得感染抗性的分子基础;果蝇的免疫反应主要发生在中肠,二次免疫比初次免疫的效应更迅速且剧烈;二次免疫的果蝇中,肠道干细胞的数量显著多于初次感染。结论 果蝇肠道中强大的训练免疫可由同源或异源革兰氏阴性菌口腔感染引发,且免疫记忆可在整个发育阶段持...     

蛋白质侧链的预测

根据Ｄｅｓｍｅｔ的死端排除定则制作了一种蛋白质侧链观测的快速软件系统,用一批实例结构数据和不同的侧链转子来检验上定则的可行性;并讨论预测的ＲＭＳ与溶剂可及性和结构元素的关系。     

CX3CL1 induces cell migration and invasion through ICAM-1 expression in oral squamous cell carcinoma cells

Human oral squamous cell carcinoma (OSCC) has been associated with a relatively low survival rate over the years and is characterized by a poor prognosis. C-X3-C motif chemokine ligand 1 (CX3CL1) has been involved in advanced migratory cells. Overexpressed CX3CL1 promotes several cellular responses related to cancer metastasis, including cell movement, migration and invasion in tumour cells. However, CX3CL1 controls the migration ability, and its molecular mechanism in OSCC remains unknown. The present study confirmed that CX3CL1 increased cell movement, migration and invasion. The CX3CL1-induced cell motility is upregulated through intercellular adhesion molecule-1 (ICAM-1) expression in OSCC cells. These effects were significantly suppressed when OSCC cells were pre-treated with CX3CR1 monoclonal antibody (mAb) and small-interfering RNA (siRNA). The CX3CL1-CX3CR1 axis activates promoted PLCβ/PKCα/c-Src phosphorylation. Furthermore, CX3CL1 enhanced activator protein-1 (AP-1) activity. The CX3CR1 mAb and PLCβ, PKCα, c-Src inhibitors reduced CX3CL1-induced c-Jun phosphorylation, c-Jun translocation into the nucleus and c-Jun binding to the ICAM-1 promoter. The present results reveal that CX3CL1 induces the migration of OSCC cells by promoting ICAM-1 expression through the CX3CR1 and the PLCβ/PKCα/c-Src signal pathway, suggesting that CX3CL1-CX3CR1-mediated signalling is correlated with tumour motility and appealed to be a precursor for prognosis in human OSCC.     

The Ethanol Extract of Syringa oblata Heartwood,a Mongolian Folk Medicine Containing Major Lignans,Exerts Analgesic and Sedative Effects on Mice

The heartwood of Syringa oblata Lindl. (SO) is one of Mongolian folk medicines to treat insomnia and pain, while its pharmacological evaluation and underlying mechanism remain unclear. In this study, the sedative effect of ethanol extract of SO (ESO) was evaluated with the locomotor activity test and the threshold dose of pentobarbital sodium-induced sleep test in mice, and the hot plate test, acetic acid-induced writhing test, and formalin test in mice were used to evaluate its analgesic effect. The underlying mechanism of ESO analgesia was explored by RT-PCR and western blot analysis, which is associated with the regulation of the NF-κB signaling pathway. Besides, the main constituents of ESO were characterized by LC/MS data analysis and comparison with isolated pure compounds. The current findings brought evidence for clinical application and further pharmacological and phytochemical studies on SO.     

A transformer architecture for retention time prediction in liquid chromatography mass spectrometry-based proteomics

Accurate retention time (RT) prediction is important for spectral library-based analysis in data-independent acquisition mass spectrometry-based proteomics. The deep learning approach has demonstrated superior performance over traditional machine learning methods for this purpose. The transformer architecture is a recent development in deep learning that delivers state-of-the-art performance in many fields such as natural language processing, computer vision, and biology. We assess the performance of the transformer architecture for RT prediction using datasets from five deep learning models Prosit, DeepDIA, AutoRT, DeepPhospho, and AlphaPeptDeep. The experimental results on holdout datasets and independent datasets exhibit state-of-the-art performance of the transformer architecture. The software and evaluation datasets are publicly available for future development in the field.