Integrated analysis of ceRNA network and tumor-infiltrating immune cells in esophageal cancer

Background: Esophageal cancer (ESCA) is one of the most commonly diagnosed cancers in the world. Tumor immune microenvironment is closely related to tumor prognosis. The present study aimed at analyzing the competing endogenous RNA (ceRNA) network and tumor-infiltrating immune cells in ESCA.Methods: The expression profiles of mRNAs, lncRNAs, and miRNAs were downloaded from the Cancer Genome Atlas database. A ceRNA network was established based on the differentially expressed RNAs by Cytoscape. CIBERSORT was applied to estimate the proportion of immune cells in ESCA. Prognosis-associated genes and immune cells were applied to establish prognostic models basing on Lasso and multivariate Cox analyses. The survival curves were constructed with Kaplan–Meier method. The predictive efficacy of the prognostic models was evaluated by the receiver operating characteristic (ROC) curves.Results: The differentially expressed mRNAs, lncRNAs, and miRNAs were identified. We constructed the ceRNA network including 23 lncRNAs, 19 miRNAs, and 147 mRNAs. Five key molecules (HMGB3, HOXC8, HSPA1B, KLHL15, and RUNX3) were identified from the ceRNA network and five significant immune cells (plasma cells, T cells follicular helper, monocytes, dendritic cells activated, and neutrophils) were selected via CIBERSORT. The ROC curves based on key genes and significant immune cells all showed good sensitivity (AUC of 3-year survival: 0.739, AUC of 5-year survival: 0.899, AUC of 3-year survival: 0.824, AUC of 5-year survival: 0.876). There was certain correlation between five immune cells and five key molecules.Conclusion: The present study provides an effective bioinformatics basis for exploring the potential biomarkers of ESCA and predicting its prognosis.     

不同身体质量指数人群肠道菌群结构及共存网络解析

[背景] 人体能量稳态失衡表现为体重过轻、超重和肥胖,肠道菌群与人体能量稳态的维持有关,但不同身体质量指数（Body Mass Index,BMI）人群的肠道菌群特征仍需进一步探究。[目的] 基于美国肠道计划公开数据库,解析4类BMI人群肠道菌群的特征,并探究4类BMI人群肠道菌群共存网络特征及差异,为基于肠道菌群来干预肥胖及体重过轻等不健康状态提供新的理论依据。[方法] 从美国肠道计划数据集中筛选具有BMI信息的肠道菌群样本,并根据世界卫生组织规定的BMI划分标准将筛选后的样本分为4类：体重过轻（BMI<18.5 kg/m²）,正常体重（18.5 kg/m²2）,超重（25 kg/m²2）,肥胖（BMI>30 kg/m²）;通过计算和比较肠道菌群的α多样性和β多样性探究4类BMI人群肠道菌群的整体结构特征及差异;通过多元线性回归模型对不同BMI分类与肠道菌群进行相关性分析,并且将地域、年龄、性别因素作为混杂因素加入到模型中进行校正;采用SparCC分别计算4类BMI人群肠道菌群中菌属相关性,并分别构建肠道菌群共存网络。[结果] 经过Wilcoxon秩和检验,发现体重过轻、超重、肥胖人群的肠道菌群α多样性都显著低于正常体重人群;β多样性分析结果表明4类BMI人群肠道菌群的整体结构存在显著差异;4类BMI人群肠道中厚壁菌门（Firmicutes）和拟杆菌门（Bacteroidetes）的相对含量无显著差异;通过MaAsLin分析,并且将地域、年龄、性别因素作为混杂因素加入到模型中进行校正,共得到49个与BMI类型显著相关的物种;4类BMI人群肠道菌群共存网络的拓扑结构具有一定差异,体重过轻和正常体重人群肠道菌群共存网络的复杂度较高,超重和肥胖人群肠道菌群共存网络的复杂度较低。[结论] 4类BMI人群肠道菌群的多样性、整体结构和共存网络间均存在差异。     

织金洞钟乳石沉积物表面可培养细菌多样性和网络分析

【背景】洞穴环境中蕴含丰富而独特的细菌资源,对洞穴环境中细菌的分离培养有助于了解洞穴细菌多样性及细菌资源的挖掘和利用。【目的】利用不同培养基分离细菌,探究钟乳石表面可培养细菌的多样性及其种间互作关系。【方法】通过11种培养基对织金洞内钟乳石沉积物表面的细菌进行分离纯化,并利用16S rRNA基因序列分析初步确定分离菌株的分类地位。在R软件下,通过Bipartite包分析可培养细菌属间的相互作用。【结果】从钟乳石沉积物表面共分离出206株细菌,它们隶属于4门25属45种,香农(Shannon)指数为4.78,辛普森(Simpson)指数为0.95。变形菌门(Proteobacteria)和芽孢杆菌属(Bacillus)分别为样品中可培养细菌的优势门(47.09%)和优势属(29.61%)。无机寡营养培养基有助于洞穴钟乳石沉积物细菌的分离。属水平-采样点网络分析表明,可培养细菌分布具有非随机、显著的嵌套性。芽孢杆菌属(Bacillus)、德沃斯氏菌属(Devosia)、产碱杆菌属(Alcaligenes)、节杆菌属(Arthrobacter)和短杆菌属(Brevibacterium)在细菌群落中存在较多的有效合作值(Effective Partners)和亲密度(Closeness),被其他细菌所依赖程度(Species Strength)较高,是该群落中的重要组成类群。【结论】织金洞内钟乳石沉积物表面存在丰富的细菌资源,分析细菌类群在群落中的作用应结合细菌相对丰度和网络分析。     

The Bio3D packages for structural bioinformatics

Bio3D is a family of R packages for the analysis of biomolecular sequence, structure, and dynamics. Major functionality includes biomolecular database searching and retrieval, sequence and structure conservation analysis, ensemble normal mode analysis, protein structure and correlation network analysis, principal component, and related multivariate analysis methods. Here, we review recent package developments, including a new underlying segregation into separate packages for distinct analysis, and introduce a new method for structure analysis named ensemble difference distance matrix analysis (eDDM). The eDDM approach calculates and compares atomic distance matrices across large sets of homologous atomic structures to help identify the residue wise determinants underlying specific functional processes. An eDDM workflow is detailed along with an example application to a large protein family. As a new member of the Bio3D family, the Bio3D‐eddm package supports both experimental and theoretical simulation‐generated structures, is integrated with other methods for dissecting sequence‐structure–function relationships, and can be used in a highly automated and reproducible manner. Bio3D is distributed as an integrated set of platform independent open source R packages available from: http://thegrantlab.org/bio3d/ .     

北京城市蝴蝶蜜源植物网络特征及重要蜜源植物识别

物种间相互作用网络研究能为物种多样性的保护、城市生态系统稳定性的维持提供指导。基于群落水平的城市蝴蝶蜜源植物互作网络的研究较少,对城市蝴蝶蜜源网络结构缺乏深入认识。研究在国内城市生态系统中构建蝴蝶蜜源网络,并探讨不同类型植物对网络特征的影响。2020年6-9月,在北京26个城市公园中记录访花蝴蝶和蜜源植物物种及互作频次,采用交互多样性（ID）、交互均匀性（IE）、专业化程度（H2''）定量化生态网络结构特征,采用Kruskal-Wallis秩和检验和变差分解分析不同生长型、起源、栽培方式的植物类型对网络结构的影响差异。采用物种的伙伴多样性（PD）和专业化程度（d''）识别重要蜜源植物。研究结果表明：（1）北京城市公园中22种蝴蝶与81种开花植物的交互作用,形成趋于泛化的生态网络结构;（2）不同生长型及不同起源的植物-蝴蝶网络的交互多样性及专业化程度有显著差异,草本及乡土植物对丰富网络中交互多样性和支持专业性更高的蝴蝶物种具重要作用,而植物的栽培方式对蜜源网络结构影响较小;（3）伙伴多样性高且专业化程度高的植物可被视为重要蜜源植物。基于蝴蝶多样性保护的目标,在城市生态系统中,绿色空间应注重构建乡土草本植物群落,优先选择重要蜜源植物。我们的发现印证了蝴蝶-蜜源植物生态网络方法作为联结生态研究和城市绿地实践管理的有效工具,能为城市生态系统中生物多样性保护提供科学策略,具有重要意义。     

基于残差注意力网络模型的浮游植物识别

浮游植物作为水生态系统中最重要的生物组成部分之一,对水环境敏感,在水环境监测中得到了广泛的关注。然而水生环境复杂多样,准确高效地识别浮游植物是监测工作中的一大挑战。当前浮游植物识别方法可分为经典形态学分类、分子标记和人工智能图像识别三类。前两种方法已被广泛采用,但费时费力,不利于监测机构的大规模应用和推广。同样,利用图像进行自动化分类难以在高准确率与高效率上达到平衡。深度学习技术的发展为此提供了新思路。本文提出一种新的深度卷积神经网络RAN-11。该网络以残差注意力网络Attention-56和Attention-92为基础,凭借通道对齐融合主干上的底层特征与顶层特征,通过调整注意力模块和残差快个数以精简结构,并引入了Leaky ReLU激活函数代替ReLU。以太湖11个优势属共计1036张图像为数据来源进行对比验证。除星杆藻外,RAN-11对单一优势属的的查准率都在90%以上,并且有5个优势属达到100%的查准率。RAN-11的识别准确率为95.67%,推理速率为41.5帧/s,不仅比Attention-92（95.19%的准确率,23.6帧/s）更准确,而且比Attention-56（94.71%的准确率,41.2帧/s）更快,真正兼顾了准确率与效率。研究结果表明：（1）RAN-11在查准率、准确率和推理速率上优于原始残差注意力网络,更优于以词包模型为代表的传统图像识别方法;（2）融合多尺度特征、精简网络结构和优化激活函数是提高卷积神经网络性能的有力手段。建立在经典分类基础之上,本文提出新的残差注意力网络来提升浮游植物鉴定技术,并构建出浮游植物自动化识别系统,识别准确率高、易于推广,对于实现水体中浮游植物的自动化监测具有重要意义。