TREEasy: An automated workflow to infer gene trees,species trees,and phylogenetic networks from multilocus data

Multilocus genomic data sets can be used to infer a rich set of information about the evolutionary history of a lineage, including gene trees, species trees, and phylogenetic networks. However, user‐friendly tools to run such integrated analyses are lacking, and workflows often require tedious reformatting and handling time to shepherd data through a series of individual programs. Here, we present a tool written in Python—TREEasy—that performs automated sequence alignment (with MAFFT), gene tree inference (with IQ‐Tree), species inference from concatenated data (with IQ‐Tree and RaxML‐NG), species tree inference from gene trees (with ASTRAL, MP‐EST, and STELLS2), and phylogenetic network inference (with SNaQ and PhyloNet). The tool only requires FASTA files and nine parameters as inputs. The tool can be run as command line or through a Graphical User Interface (GUI). As examples, we reproduced a recent analysis of staghorn coral evolution, and performed a new analysis on the evolution of the “WGD clade” of yeast. The latter revealed novel patterns that were not identified by previous analyses. TREEasy represents a reliable and simple tool to accelerate research in systematic biology ( https://github.com/MaoYafei/TREEasy ).     

The pulse of the tree is under genetic control: eucalyptus as a case study

The pulse of the tree (diurnal cycle of stem radius fluctuations) has been widely studied as a way of analyzing tree responses to the environment, including the phenotypic plasticity of tree–water relationships in particular. However, the genetic basis of this daily phenotype and its interplay with the environment remain largely unexplored. We characterized the genetic and environmental determinants of this response, by monitoring daily stem radius fluctuation (dSRF) on 210 trees from a Eucalyptus urophylla × E. grandis full‐sib family over 2 years. The dSRF signal was broken down into hydraulic capacitance, assessed as the daily amplitude of shrinkage (DA), and net growth, estimated as the change in maximum radius between two consecutive days (ΔR). The environmental determinants of these two traits were clearly different: DA was positively correlated with atmospheric variables relating to water demand, while ΔR was associated with soil water content. The heritability for these two traits ranged from low to moderate over time, revealing a time‐dependent or environment‐dependent complex genetic determinism. We identified 686 and 384 daily quantitative trait loci (QTL) representing 32 and 31 QTL regions for DA and ΔR, respectively. The identification of gene networks underlying the 27 major genomics regions for both traits generated additional hypotheses concerning the biological mechanisms involved in response to water demand and supply. This study highlights that environmentally induced changes in daily stem radius fluctuation are genetically controlled in trees and suggests that these daily responses integrated over time shape the genetic architecture of mature traits.     

玉米转基因技术专利权人合作态势分析

转基因玉米是最重要的转基因主粮作物之一,其转基因技术具有一定的代表性。为了更好地了解和掌握玉米转基因技术领域研发主体合作情况,文章构建了基于专利权人合作网络的目标技术领域专利权人合作态势分析框架,并基于社会网络分析方法与技术,以世界范围内的转基因玉米领域的重要专利权人为分析对象,构建合作网络、分析整体合作特征、挖掘合作子网、探测领域内重要专利权人,从而从宏观、中观和微观三个层面客观展现玉米转基因技术领域专利权人合作态势,为科技战略规划提供一定的决策支撑。     

基于综合评价法的洞庭湖区绿地生态网络构建

绿地生态网络对于改善区域生态空间破碎化、生物多样性降低、生态系统服务供需不平衡及保障区域生态安全具有重要意义。本研究以洞庭湖区为例,在3S技术支持下,从生态系统服务功能、潜在生物多样性、形态空间格局的角度综合评价和识别生态源地及计算栅格单元的基本生态阻力;利用夜间灯光指数修正基本生态阻力;运用最小累积阻力模型识别生态廊道;构建加权评价模型,对源地的聚合度、离散度及廊道的生态连接贡献度进行评价;利用结构特征指数对综合网络、“源-汇”潜在网络及规划网络进行对比分析和评价。结果表明: 源地空间分布不均衡,林地、灌丛与水域三者面积之和占源地总面积的95.9%,位于研究区中部的洞庭湖湿地生态风险较高;源地离生态网络系统中心位置越近及到其他源地的平均最小累积阻力越小,聚合及离散的优势越强;高生态质量源地周围的中、高生态质量源地分布越密集,其聚合度、离散度越高;廊道离高生态质量的源地越近,表现为生态连接贡献度越大;林地、灌丛,尤其是河道在自然生态系统与人类社会系统之间起着非常重要的生态连接作用;“源-汇”规划绿道对“源-汇”潜在生态廊道形成了良好补充,与“源-汇”潜在网络相比,综合网络的α、β、γ、ρ指数分别提高123.1%、25.8%、26.2%、74.6%;与“源-汇”规划网络相比,α、β、γ、ρ指数分别提高了190.0%、31.1%、32.5%、114.6%。本研究结果能为洞庭湖区绿地生态网络构建、国土空间规划提供参考。     

西双版纳国家级自然保护区蚂蚁-树互作网络空间变异

网络分析(network analysis)可以同时分析群落中的物种多样性和种间关系, 为了解生态群落的稳定性机制提供了新的分析思路和方法。本研究从西双版纳国家级自然保护区的纳板河、勐仑和勐腊(补蚌)三个地点采集了树栖性蚂蚁及树木的种类和数量数据, 对蚂蚁-树组成的二分网络进行了分析, 探讨了3个采样点物种的多样性、网络指标以及群落指标之间的关系。我们采用零模型的方法比较了3个样点的标准化网络参数差异。结果表明: 蚂蚁和树木的物种数以及树的异质性指数(Shannon-Wiener多样性指数、Simpson多样性指数)都呈现出勐仑 > 纳板河 > 补蚌的趋势。树木-蚂蚁的灭绝曲线系数大小关系同样为勐仑 > 纳板河 > 补蚌, 灭绝曲线与树的物种数及异质性指数大小趋势一致, 而与蚂蚁的异质性指数并不吻合。根据Z值的绝对值来看, 网络参数(加权嵌套性、平均连接数、特化水平、模块性、连接度)与群落参数(灭绝曲线系数、生态位重叠)的大小趋势相同, 表现出勐仑 > 纳板河 > 补蚌的趋势。综上所述, 蚂蚁-树互作网络的稳定性(灭绝曲线系数)主要由树的数量和异质性指数决定。网络的加权嵌套性和网络中节点的平均连接数也能促进群落的稳定性。而在一个特化的(数值越大表示专性互作越多)和模块化(具有较多密切互作的节点单元)的网络中, 当低营养级物种灭绝时高营养级物种数量将迅速减少。     

基于生态系统服务供需的雄安新区生态网络构建与优化

城市生态网络构建是城市生态系统服务有效发挥作用的保障,构建完善的生态网络对于城市生态格局的优化具有重要的意义。结合雄安新区总体规划,通过雄安新区生态系统服务供给、需求两个层面识别生态源地。基于源地-缓冲区-廊道-节点框架,构建新区生态网络。其中,基于生态源地与城镇用地驱动因子,运用最小累积阻力方法得到累积阻力差,构建新区三生空间布局。根据成本距离分析和路径分析,结合雄安新区规划绿带分布,生成生态廊道,并在廊道与廊道交汇点、重要生态功能与脆弱的关键点以及道路轨道交汇点识别生态节点。得出：（1）新区生态源地主要位于白洋淀、公园绿地与其他绿地,分为水域生态源和林地生态源两类生态源地,面积总共728 km²。城镇源地主要位于新区东部容城县与雄县城区以及一些零星的农村居民点区域,面积为166 km²。（2）在新区三生空间布局下,加强生态廊道的构建,提升生态源地之间、生态源地与城镇源地之间的连通性,新区生态廊道主要依赖河流廊道和林地廊道两种类型。（3）在新区未来生态网络的建设中,需要重点关注河流廊道交汇点、河流廊道与城区的交汇处、交通道路与生态用地交汇处等的生态节点的建设及其生态功能的提升。     

甘草抗动脉粥样硬化机制的网络药理学研究

利用网络药理学方法探讨甘草在抗动脉粥样硬化中的分子机制。本研究利用中医药系统药理学数据库和分析平台(traditional Chinese medicine systems pharmacology database and analysis platform,TCMSP)分析甘草中的有效活性成分,并获得有效成分的作用靶点。通过Cytoscape软件构建可视化靶点互相作用网络,对网络中的关键靶点进行基因本体(GO)富集分析和KEGG通路富集分析。结果显示甘草中40种有效活性成分的预测靶点共97个,47个靶点与动脉粥样硬化(AS)相关,其中18个是血管保护药物和脂质修饰药物的作用靶点,表明甘草可作为调控AS发展的药物。基于97个预测靶点的GO富集分析,发现甘草可参与多种生物学过程,尤其是应对外源性刺激,以及参与细胞凋亡等过程。通过构建甘草靶点与AS疾病靶点相互作用网络(PPI),确定了AKT1、MAPK3、MAPK1、JUN和CASP3等关键靶点,并对关键靶点进行KEGG富集分析,结果表明甘草主要影响调控细胞增殖、生存以及凋亡的细胞信号转导相关通路,并激活先天免疫相关信号通路,调节炎性细胞因子释放,从而发挥抗动脉粥样硬化作用。甘草具有多成分、多靶点、多途径的作用特点,主要通过PI3K-AKT信号途径、MAPK信号途径、NOD样受体信号通路调控细胞增殖和凋亡,同时发挥免疫调控作用,从而影响动脉粥样硬化的发展,由此可见,甘草可作为动脉粥样硬化疾病治疗的候选中草药。     

Trophic niche similarities of sympatric Turdus thrushes determined by fecal contents,stable isotopes,and bipartite network approaches

An ecological niche has been defined as an n‐dimensional hypervolume formed by conditions and resources that species need to survive, grow, and reproduce. In practice, such niche dimensions are measurable and describe how species share resources, which has been thought to be a crucial mechanism for coexistence and a major driver of broad biodiversity patterns. Here, we investigate resource partitioning and trophic interactions of three sympatric, phylogenetically related and morphologically similar species of thrushes (Turdus spp.). Based on one year of data collected in southern Brazil, we investigated niche partitioning using three approaches: diet and trophic niche assessed by fecal analysis, diet and niche estimated by stable isotopes in blood and mixing models, and bipartite network analysis derived from direct diet and mixing model outputs. Approaches revealed that the three sympatric thrushes are generalists that feed on similar diets, demonstrating high niche overlap. Fruits from C3 plants were one of the most important food items in their networks, with wide links connecting the three thrush species. Turdus amaurochalinus and T. albicollis had the greatest trophic and isotopic niche overlap, with 90% and 20% overlap, respectively. There was partitioning of key resources between these two species, with a shared preference for fig tree fruits—Ficus cestrifolia (T. amaurochalinus PSIRI% = 11.3 and T. albicollis = 11.5), which was not present in the diet of T. rufiventris. Results added a new approach to the network analysis based on values from the stable isotope mixing models, allowing comparisons between traditional dietary analysis and diet inferred by isotopic mixing models, which reflects food items effectively assimilated in consumer tissues. Both are visualized in bipartite networks and show food‐consumers link strengths. This approach could be useful to other studies using stable isotopes coupled to network analysis, particularly useful in sympatric species with similar niches.     

Robustness of a meta-network to alternative habitat loss scenarios

Studying how habitat loss affects the tolerance of ecological networks to species extinction (i.e. their robustness) is key for our understanding of the influence of human activities on natural ecosystems. With networks typically occurring as local interaction networks interconnected in space (a meta-network), we may ask how the loss of specific habitat fragments affects the overall robustness of the meta-network. To address this question, for an empirical meta-network of plants, herbivores and natural enemies we simulated the removal of habitat fragments in increasing and decreasing order of area, age and connectivity for plant extinction and the secondary extinction of herbivores, natural enemies and their interactions. Meta-network robustness was characterized as the area under the curve of remnant species or interactions at the end of a fragment removal sequence. To pinpoint the effects of fragment area, age and connectivity, respectively, we compared the observed robustness for each removal scenario against that of a random sequence. The meta-network was more robust to the loss of old (i.e. long-fragmented), large, connected fragments than of young (i.e. recently fragmented), small, isolated fragments. Thus, young, small, isolated fragments may be particularly important to the conservation of species and interactions, while contrary to our expectations larger, more connected fragments contribute little to meta-network robustness. Our findings highlight the importance of young, small, isolated fragments as sources of species and interactions unique to the regional level. These effects may largely result from an unpaid extinction debt, whereby younger fragments are likely to lose species over time. Yet, there may also be more long-lasting effects from cultivated lands (e.g. water, fertilizers and restricted cattle grazing) and network complexity in small, isolated fragments. Such fragments may sustain important biological diversity in fragmented landscapes, but maintaining their conservation value may depend on adequate restoration strategies.     

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.