EcologicalNetworks.jl: analysing ecological networks of species interactions

Networks are a convenient way to represent many interactions among ecological entities. The analysis of ecological networks is challenging for two reasons. First, there is a plethora of measures that can be applied (and some of them measure the same property). Second, the implementation of these measures is sometimes difficult. We present ’EcologicalNetworks.jl’, a package for the ‘Julia’ programming language. Using a layered system of types to represent several types of ecological networks, this packages offers a solid library of basic functions which can be chained together to perform the most common analyses of ecological networks.     

水稻条纹花叶病毒对介体电光叶蝉生长繁殖及取食行为的影响

【目的】由电光叶蝉Recilia dorsalis传播的水稻条纹花叶病毒(rice stripe mosaic virus,RSMV)目前在我国华南稻区大面积发生并对水稻生产造成严重危害。本研究旨在明确RSMV对介体电光叶蝉生长繁殖及取食行为的影响。【方法】通过生物学实验测定RSMV侵染后电光叶蝉的生长和生命表参数;利用刺吸电位(electrical penetration graph,EPG)技术比较携带和未携带RSMV的电光叶蝉成虫在健康水稻上的取食行为差异;采用Y型嗅觉仪测定电光叶蝉成虫对感染和未感染RSMV水稻的寄主选择倾向性。【结果】与无毒电光叶蝉相比,携带RSMV的电光叶蝉若虫发育历期延长,而若虫存活率、成虫羽化率、雌虫繁殖力和卵孵化率下降。无毒电光叶蝉成虫倾向于选择取食RSMV侵染的水稻,而带毒电光叶蝉成虫倾向于选择取食健康水稻。与无毒电光叶蝉相比,带毒电光叶蝉成虫取食健康水稻所产生的刺探波、障碍波和唾液分泌波次数和持续时间均显著增加,被动取食波和休息波次数减少但时间均延长。【结论】与无毒电光叶蝉相比,感染RSMV使带毒电光叶蝉若虫发育历期延长且不利于其种群的繁殖。RSMV通过调控介体电光叶蝉成虫的取食和寄主选择行为而有利于自身在寄主水稻间的传播。     

基于图论的景观图表达、分析及应用

景观结构和空间格局一直是景观生态学的核心问题,图论的应用为景观格局的分析提供了一种研究框架,基于图论的景观图逐渐被应用于生物多样性保护的连通性建模和景观规划的决策支持研究,景观图的表达、分析和应用已成为保护生物学和景观生态学研究的热点。本文首先介绍了景观图的图论基础,在Scopus数据库的基础上,检索了1993—2019年在标题、摘要和关键词中出现 “landscape graph”、“connectivity”和“network”词汇的257篇已发表的期刊论文。从年发文量、来源期刊、研究区域、研究机构、景观类型等方面分析了该领域的研究进展和发展趋势。分析发现,2017年之前,发表的期刊论文数量整体呈上升趋势,2017年之后年发文量逐年下降;主要研究力量集中在美国、西班牙、法国、加拿大和中国,发文量占到86.8%。大部分研究成果发表在“Landscape Ecology”、“Landscape and Urban Planning”和“Biological Conservation”期刊上。在研究内容上,景观图表达主要包括点的定义、边的度量和景观的模拟3方面,景观图分析研究包括分析指数、景观图划分两方面。我们重点关注了景观图在生物多样性保护、景观(生态网络)规划和管理、景观影响评价等科学与实践中的应用。基于图论的景观图通过帮助理解景观连通性变化、动物行为和栖息地保护,影响着保护科学和规划实践者。图论对保护科学和规划的影响来自于丰富的理论基础和成熟的研究方法,基于图论的景观图为景观结构和格局的生态学理解提供了跳板,并将继续成为全球研究人员和实践者的重要工具。     

Efficient mapping of ligand migration channel networks in dynamic proteins

For many proteins such as myoglobin, the binding site lies in the interior, and there is no obvious route from the exterior to the binding site in the average structure. Although computer simulations for a limited number of proteins have found some transiently open channels, it is not clear if there exist more channels elsewhere or how the channels are regulated. A systematic approach that can map out the whole ligand migration channel network is lacking. Ligand migration in a dynamic protein resembles closely a well-studied problem in robotics, namely, the navigation of a mobile robot in a dynamic environment. In this work, we present a novel robotic motion planning inspired approach that can map the ligand migration channel network in a dynamic protein. The method combines an efficient spatial mapping of protein inner space with a temporal exploration of protein structural heterogeneity, which is represented by a structure ensemble. The spatial mapping of each conformation in the ensemble produces a partial map of protein inner cavities and their inter-connectivity. These maps are then merged to form a super map that contains all the channels that open dynamically. Results on the pathways in myoglobin for gaseous ligands demonstrate the efficiency of our approach in mapping the ligand migration channel networks. The results, obtained in a significantly less amount of time than trajectory-based approaches, are in agreement with previous simulation results. Additionally, the method clearly illustrates how and what conformational changes open or close a channel.     

Using a genetic network to parameterize a landscape resistance surface for fishers, Martes pennanti

Knowledge of dispersal-related gene flow is important for addressing many basic and applied questions in ecology and evolution. We used landscape genetics to understand the recovery of a recently expanded population of fishers (Martes pennanti) in Ontario, Canada. An important focus of landscape genetics is modelling the effects of landscape features on gene flow. Most often resistance surfaces in landscape genetic studies are built a priori based upon nongenetic field data or expert opinion. The resistance surface that best fits genetic data is then selected and interpreted. Given inherent biases in using expert opinion or movement data to model gene flow, we sought an alternative approach. We used estimates of conditional genetic distance derived from a network of genetic connectivity to parameterize landscape resistance and build a final resistance surface based upon information-theoretic model selection and multi-model averaging. We sampled 657 fishers from 31 landscapes, genotyped them at 16 microsatellite loci, and modelled the effects of snow depth, road density, river density, and coniferous forest on gene flow. Our final model suggested that road density, river density, and snow depth impeded gene flow during the fisher population expansion demonstrating that both human impacts and seasonal habitat variation affect gene flow for fishers. Our approach to building landscape genetic resistance surfaces mitigates many of the problems and caveats associated with using either nongenetic field data or expert opinion to derive resistance surfaces.     

Chimeragenesis of distantly‐related proteins by noncontiguous recombination

We introduce a method for identifying elements of a protein structure that can be shuffled to make chimeric proteins from two or more homologous parents. Formulating recombination as a graph‐partitioning problem allows us to identify noncontiguous segments of the sequence that should be inherited together in the progeny proteins. We demonstrate this noncontiguous recombination approach by constructing a chimera of β‐glucosidases from two different kingdoms of life. Although the protein's alpha–beta barrel fold has no obvious subdomains for recombination, noncontiguous SCHEMA recombination generated a functional chimera that takes approximately half its structure from each parent. The X‐ray crystal structure shows that the structural blocks that make up the chimera maintain the backbone conformations found in their respective parental structures. Although the chimera has lower β‐glucosidase activity than the parent enzymes, the activity was easily recovered by directed evolution. This simple method, which does not rely on detailed atomic models, can be used to design chimeras that take structural, and functional, elements from distantly‐related proteins.     

Annotating Diseases Using Human Phenotype Ontology Improves Prediction of Disease-Associated Long Non-coding RNAs

Recently, many long non-coding RNAs (lncRNAs) have been identified and their biological function has been characterized; however, our understanding of their underlying molecular mechanisms related to disease is still limited. To overcome the limitation in experimentally identifying disease–lncRNA associations, computational methods have been proposed as a powerful tool to predict such associations. These methods are usually based on the similarities between diseases or lncRNAs since it was reported that similar diseases are associated with functionally similar lncRNAs. Therefore, prediction performance is highly dependent on how well the similarities can be captured. Previous studies have calculated the similarity between two diseases by mapping exactly each disease to a single Disease Ontology (DO) term, and then use a semantic similarity measure to calculate the similarity between them. However, the problem of this approach is that a disease can be described by more than one DO terms. Until now, there is no annotation database of DO terms for diseases except for genes. In contrast, Human Phenotype Ontology (HPO) is designed to fully annotate human disease phenotypes. Therefore, in this study, we constructed disease similarity networks/matrices using HPO instead of DO. Then, we used these networks/matrices as inputs of two representative machine learning-based and network-based ranking algorithms, that is, regularized least square and heterogeneous graph-based inference, respectively. The results showed that the prediction performance of the two algorithms on HPO-based is better than that on DO-based networks/matrices. In addition, our method can predict 11 novel cancer-associated lncRNAs, which are supported by literature evidence.     

Probing behaviour of Diuraphis noxia on five cereal species with different hydroxamic acid levels

The feeding behaviour of Diuraphis noxia (Kurdjumov) (Homoptera: Aphididae) was electronically monitored on five cereal species (Triticum aestivum L., T. turgidum L., Secale cereale L. and x Triticosecale Wittmack) containing different hydroxamic acids levels and on Hordeum vulgare L., lacking these compounds, by means of a DC-System. With increasing DIMBOA (2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3-one) levels D. noxia showed less probing particularly before reaching the phloem phase and a lower percentage of aphids achieved sustained phloem ingestion. However, no significant correlation was found between DIMBOA levels and the total time of phloem feeding. These findings indicate that factors from the mesophyll and vascular tissues appear to be involved in the effects of resistance. The underlying mechanism, however, remains unclear. Thus, DIMBOA seems to be only part of the chemical defenses of the cereal plants that might be implicated in D. noxia resistance.