Inferring network interactions within a cell

The continuing growth in high-throughput data acquisition has led to a proliferation of network models to represent and analyse biological systems. These networks involve distinct interaction types detected by a combination of methods, ranging from directly observed physical interactions based in biochemistry to interactions inferred from phenotype measurements, genomic expression and comparative genomics. The discovery of interactions increasingly requires a blend of experimental and computational methods. Considering yeast as a model system, recent analytical methods are reviewed here and specific aims are proposed to improve network interaction inference and facilitate predictive biological modelling.     

Preferential duplication in the sparse part of yeast protein interaction network

Gene duplication is an important mechanism driving the evolution of biomolecular network. Thus, it is expected that there should be a strong relationship between a gene's duplicability and the interactions of its protein product with other proteins in the network. We studied this question in the context of the protein interaction network (PIN) of Saccharomyces cerevisiae. We found that duplicates have, on average, significantly lower clustering coefficient (CC) than singletons, and the proportion of duplicates (PD) decreases steadily with CC. Furthermore, using functional annotation data, we observed a strong negative correlation between PD and the mean CC for functional categories. By partitioning the network into modules and assigning each protein a modularity measure Q(n), we found that CC of a protein is a reflection of its modularity. Moreover, the core components of complexes identified in a recent high-throughput experiment, characterized by high CC, have lower PD than that of the attachments. Subsequently, 2 types of hub were identified by their degree, CC and Q(n). Although PD of intramodular hubs is much less than the network average, PD of intermodular hubs is comparable to, or even higher than, the network average. Our results suggest that high CC, and thus high modularity, pose strong evolutionary constraints on gene duplicability, and gene duplication prefers to happen in the sparse part of PINs.     

Inferring species roles in metacommunity structure from species co-occurrence networks

A long-standing question in community ecology is what determines the identity of species that coexist across local communities or metacommunity assembly. To shed light upon this question, we used a network approach to analyse the drivers of species co-occurrence patterns. In particular, we focus on the potential roles of body size and trophic status as determinants of metacommunity cohesion because of their link to resource use and dispersal ability. Small-sized individuals at low-trophic levels, and with limited dispersal potential, are expected to form highly linked subgroups, whereas large-size individuals at higher trophic positions, and with good dispersal potential, will foster the spatial coupling of subgroups and the cohesion of the whole metacommunity. By using modularity analysis, we identified six modules of species with similar responses to ecological conditions and high co-occurrence across local communities. Most species either co-occur with species from a single module or are connectors of the whole network. Among the latter are carnivorous species of intermediate body size, which by virtue of their high incidence provide connectivity to otherwise isolated communities playing the role of spatial couplers. Our study also demonstrates that the incorporation of network tools to the analysis of metacommunity ecology can help unveil the mechanisms underlying patterns and processes in metacommunity assembly.     

铁镉互作对水稻脂质过氧化及抗氧化酶活性的影响

利用营养液培养方法,以‘沈农265’为供试品种,研究不同Fe(0、0.1、0.25、0.5 mmol Fe²⁺·L^-1 )、Cd(0、0.1、1.0 μmol Cd²⁺·L^-1)处理对水稻植株体内脂质过氧化及抗氧化酶活性的影响.结果表明： 单独供应Fe显著降低了水稻地上部和根系生物量,同时供应Cd后生物量不再下降;单独供应Cd降低了根系中丙二醛(MDA)和可溶性蛋白含量,而同时供应Fe时这种降低作用消失.Fe处理降低了水稻地上部和根系Cd含量,Cd处理也降低了Fe含量,两者表现出明显的相互抑制作用.高Cd(1.0 μmol·L^-1)和Fe互作,增加了水稻根系中MDA和可溶性蛋白含量,降低了超氧化物岐化酶(SOD)和过氧化氢酶(CAT)活性.表明在低Cd环境中为水稻提供一定数量的外源Fe能降低植株Cd含量;但高Cd胁迫将降低水稻对Fe的吸收,并导致植株体内产生脂质过氧化.     

A two-step drug repositioning method based on a protein-protein interaction network of genes shared by two diseases and the similarity of drugs

The present study proposed a two-step drug repositioning method based on a protein-protein interaction (PPI) network of twodiseases and the similarity of the drugs prescribed for one of the two. In the proposed method, first, lists of disease related geneswere obtained from a meta-database called Genotator. Then genes shared by a pair of diseases were sought. At the first step of themethod, if a drug having its target(s) in the PPI network, the drug was deemed a repositioning candidate. Because targets of manydrugs are still unknown, the similarities between the prescribed drugs for a specific disease were used to infer repositioningcandidates at the second step. As a first attempt, we applied the proposed method to four different types of diseases: hypertension,diabetes mellitus, Crohn disease, and autism. Some repositioning candidates were found both at the first and second steps.     

Antagonism and bistability in protein interaction networks

A protein interaction network (PIN) is a set of proteins that modulate one another's activities by regulated synthesis and degradation, by reversible binding to form complexes, and by catalytic reactions (e.g., phosphorylation and dephosphorylation). Most PINs are so complex that their dynamical characteristics cannot be deduced accurately by intuitive reasoning alone. To predict the properties of such networks, many research groups have turned to mathematical models (differential equations based on standard biochemical rate laws, e.g., mass-action, Michaelis-Menten, Hill). When using Michaelis-Menten rate expressions to model PINs, care must be exercised to avoid making inconsistent assumptions about enzyme-substrate complexes. We show that an appealingly simple model of a PIN that functions as a bistable switch is compromised by neglecting enzyme-substrate intermediates. When the neglected intermediates are put back into the model, bistability of the switch is lost. The theory of chemical reaction networks predicts that bistability can be recovered by adding specific reaction channels to the molecular mechanism. We explore two very different routes to recover bistability. In both cases, we show how to convert the original 'phenomenological' model into a consistent set of mass-action rate laws that retains the desired bistability properties. Once an equivalent model is formulated in terms of elementary chemical reactions, it can be simulated accurately either by deterministic differential equations or by Gillespie's stochastic simulation algorithm.     

Prioritization of orphan disease-causing genes using topological feature and GO similarity between proteins in interaction networks

Identification of disease-causing genes among a large number of candidates is a fundamental challenge in human disease studies. However, it is still time-consuming and laborious to determine the real disease-causing genes by biological experiments. With the advances of the high-throughput techniques, a large number of protein-protein interactions have been produced. Therefore, to address this issue, several methods based on protein interaction network have been proposed. In this paper, we propose a shortest path-based algorithm, named SPranker, to prioritize disease-causing genes in protein interaction networks. Considering the fact that diseases with similar phenotypes are generally caused by functionally related genes, we further propose an improved algorithm SPGOranker by integrating the semantic similarity of GO annotations. SPGOranker not only considers the topological similarity between protein pairs in a protein interaction network but also takes their functional similarity into account. The proposed algorithms SPranker and SPGOranker were applied to 1598 known orphan disease-causing genes from 172 orphan diseases and compared with three state-of-the-art approaches, ICN, VS and RWR. The experimental results show that SPranker and SPGOranker outperform ICN, VS, and RWR for the prioritization of orphan disease-causing genes. Importantly, for the case study of severe combined immunodeficiency, SPranker and SPGOranker predict several novel causal genes.     

种间互作网络的结构、生态系统功能及稳定性机制研究

生态群落中不同物种间发生多样化的相互作用, 形成了复杂的种间互作网络。复杂生态网络的结构如何影响群落的生态系统功能及稳定性是群落生态学的核心问题之一。种间互作直接影响到物质和能量在生态系统不同组分之间的流动和循环以及群落构建过程, 使得网络结构与生态系统功能和群落稳定性密切相关。在群落及生态系统水平上开展种间互作网络研究将为群落的构建机制、生物多样性维持、生态系统稳定性、物种协同进化和性状分化等领域提供新的视野。当前生物多样性及生态系统功能受到全球变化的极大影响, 研究种间互作网络的拓扑结构、构建机制、稳定性和生态功能也可为生物多样性的保护和管理提供依据。该文从网络结构、构建机制、网络结构和稳定性关系、种间互作对生态系统功能的影响等4个方面综述当前种间网络研究进展, 并提出在今后的研究中利用机器学习和多层网络等来探究环境变化对种间互作网络结构和功能的影响, 并实现理论和实证研究的有效整合。     

Identification of significant pathways in gastric cancer based on protein-protein interaction networks and cluster analysis

Gastric cancer is one of the most common and lethal cancers worldwide. However, despite its clinical importance, the regulatory mechanisms involved in the aggressiveness of this cancer are still poorly understood. A better understanding of the biology, genetics and molecular mechanisms of gastric cancer would be useful in developing novel targeted approaches for treating this disease. In this study we used protein-protein interaction networks and cluster analysis to comprehensively investigate the cellular pathways involved in gastric cancer. A primary immunodeficiency pathway, focal adhesion, ECM-receptor interactions and the metabolism of xenobiotics by cytochrome P450 were identified as four important pathways associated with the progression of gastric cancer. The genes in these pathways, e.g., ZAP70, IGLL1, CD79A, COL6A3, COL3A1, COL1A1, CYP2C18 and CYP2C9, may be considered as potential therapeutic targets for gastric cancer.     

基于蛋白质互作网络的COVID-19对肺动脉高压的影响机制

通过构建肺动脉高压差异基因和冠状病毒侵入人体后免疫反应相关基因的互作网络,探索COVID-19对肺动脉高压的影响机制。首先通过Meta分析挖掘肺动脉高压相关差异表达基因;其次通过SARS-CoV侵染人体后的基因表达数据,挖掘主要功能通路;最后构建肺动脉高压差异表达基因和冠状病毒主要功能通路基因的互作网络,挖掘网络的显著功能模块。发现肺动脉高压与血管平滑肌细胞、成纤细胞、T/B细胞免疫过程、转录调节因子通路、Toll样信号通路等密切相关,互作网络发现ITGAM、HBB、VCAM1、IL1R2等基因是COVID-19感染肺动脉高压患者的重要调节基因。通过肺动脉高压与冠状病毒感染机体后蛋白质互作网络探索了COVID-19对肺动脉高压的影响机制,为肺动脉高压感染COVID-19的研究及治疗提供了新思路。     

垂叶榕榕小蜂群落及种间互作网络季节动态

群落中的物种相互作用构成了复杂的生态网络。有关物种的数量和组成的季节性动态变化已有较多的研究, 但是对于生态网络的动态变化知之甚少。揭示生态网络的动态变化对于理解群落的稳定性以及群落的动态变化过程和机理具有重要意义。本研究以垂叶榕(Ficus benjamina)榕小蜂群落为研究对象, 分别在西双版纳的干季和雨季采集了榕小蜂的种类和数量信息。比较了两个季节榕小蜂群落的动态变化以及共存网络的参数(例如网路直径、连接数、嵌套性和群落矩阵温度)变化。结果显示: 雨季榕果内传粉榕小蜂Eupristina koningsbergeri所占比例高于干季, 传粉榕小蜂的种群数量也高于干季, 而在干季非传粉榕小蜂的种类增加(干季15种小蜂, 雨季14种)。从榕树-传粉榕小蜂互利共生系统的适合度来看, 干季非传粉小蜂的增加对传粉榕小蜂和榕树的适合度是不利的。在干季, 共存网络物种间的连接数(干季0.95, 雨季0.47)多于雨季, 群落矩阵温度(干季23.24, 雨季2.64)也显著高于雨季。表明干季榕小蜂群落组成及种间关系较雨季更为复杂而多样, 高的矩阵温度暗示群落受到的干扰更大。     

Climatic similarity and biological exchange in the worldwide airline transportation network

Recent increases in the rates of biological invasion and spread of infectious diseases have been linked to the continued expansion of the worldwide airline transportation network (WAN). Here, the global structure of the WAN is analysed in terms of climatic similarity to illuminate the risk of deliberate or accidental movements of climatically sensitive organisms around the world. From over 44,000 flight routes, we show, for each month of an average year, (i) those scheduled routes that link the most spatially distant but climatically similar airports, (ii) the climatically best-connected airports, and (iii) clusters of airports with similar climatic features. The way in which traffic volumes alter these findings is also examined. Climatic similarity across the WAN is skewed (most geographically close airports are climatically similar) but heavy-tailed (there are considerable numbers of geographically distant but climatically similar airports), with climate similarity highest in the June-August period, matching the annual peak in air traffic. Climatically matched, geographically distant airports form subnetworks within the WAN that change throughout the year. Further, the incorporation of passenger and freight traffic data highlight at greater risk of invasion those airports that are climatically well connected by numerous high capacity routes.     

The impact of life form on the architecture of orchid mycorrhizal networks in tropical forest

Understanding the processes that determine the architecture of interaction networks represents a major challenge in ecology and evolutionary biology. One of the most important interactions involving plants is the interaction between plants and mycorrhizal fungi. While there is a mounting body of research that has studied the architecture of plant–fungus interaction networks, less is known about the potential factors that drive network architecture. In this study, we described the architecture of the network of interactions between mycorrhizal fungi and 44 orchid species that represented different life forms and co‐occurred in tropical forest and assessed the relative importance of ecological, evolutionary and co‐evolutionary mechanisms determining network architecture. We found 87 different fungal operational taxonomic units (OTUs), most of which were members of the Tulasnellaceae. Most orchid species associated with multiple fungi simultaneously, indicating that extreme host selectivity was rare. However, an increasing specificity towards Tulasnellaceae fungal associates from terrestrial to epiphytic and lithophytic orchids was observed. The network of interactions showed an association pattern that was significantly modular (M = 0.7389, M_random = 0.6998) and nested (NODF = 5.53, p < 0.05). Terrestrial orchids had almost no links to modules containing epiphytic or lithophytic orchids, while modules containing epiphytic orchids also contained lithophytic orchids. Within each life form several modules were observed, suggesting that the processes that organize orchid–fungus interactions are independent of life form. The overall phylogenetic signal for both partners in the interaction network was very weak. Overall, these results indicate that tropical orchids associate with a wide number of mycorrhizal fungi and that ecological rather than phylogenetic constraints determine network architecture.     

根据蛋白质互作网络预测乳腺癌相关蛋白质的细致功能

乳腺癌是最为常见的恶性肿瘤之一。已有的关于乳腺癌相关蛋白质的功能注释比较宽泛, 制约了乳腺癌的后续研究工作。对于已知部分功能的乳腺癌相关蛋白质, 提出了一种结合Gene Ontology功能先验知识和蛋白质互作的方法, 通过构建功能特异的局部相互作用网络来预测乳腺癌相关蛋白质的细致功能。结果显示该方法能够以很高的精确率为乳腺癌相关蛋白质预测更为精细的功能。预测的相关蛋白质的功能对于指导实验研究乳腺癌的分子机制具有重要的价值。     

基于微生物互作的共培养分离技术研究进展

自然条件下,微生物以一种复杂的群落形式生活,细胞周围充斥着由相邻细胞产生的各类代谢物,使各细胞间存在多样的互作形式,影响彼此的生长。不同种类的菌株共培养时,营养缺陷型菌株可以利用其他菌株产生的代谢产物进行生长;共培养还可以改变微环境、刺激菌株沉默基因的表达及改变菌株的生存状态。近年来,基于模拟菌株间的互作关系而发展起来的共培养技术逐步应用于未培养微生物的分离工作中,并被认为能有效提高未培养微生物的分离效率。结合已发表的相关文献资料,综合分析潜在共培养的类群多样性以及共培养分离技术的先进性与应用现状等,以期为微生物分离技术的发展及微生物资源的发掘提供参考。     

Predicting enhancer-promoter interaction from genomic sequence with deep neural networks

Background: In the human genome, distal enhancers are involved in regulating target genes through proximal promoters by forming enhancer-promoter interactions. Although recently developed high-throughput experimental approaches have allowed us to recognize potential enhancer-promoter interactions genome-wide, it is still largely unclear to what extent the sequence-level information encoded in our genome help guide such interactions. Methods: Here we report a new computational method (named “SPEID”) using deep learning models to predict enhancer-promoter interactions based on sequence-based features only, when the locations of putative enhancers and promoters in a particular cell type are given. Results: Our results across six different cell types demonstrate that SPEID is effective in predicting enhancer-promoter interactions as compared to state-of-the-art methods that only use information from a single cell type. As a proof-of-principle, we also applied SPEID to identify somatic non-coding mutations in melanoma samples that may have reduced enhancer-promoter interactions in tumor genomes. Conclusions: This work demonstrates that deep learning models can help reveal that sequence-based features alone are sufficient to reliably predict enhancer-promoter interactions genome-wide.     

Computational analysis of human protein interaction networks

Large amounts of human protein interaction data have been produced by experiments and prediction methods. However, the experimental coverage of the human interactome is still low in contrast to predicted data. To gain insight into the value of publicly available human protein network data, we compared predicted datasets, high-throughput results from yeast two-hybrid screens, and literature-curated protein-protein interactions. This evaluation is not only important for further methodological improvements, but also for increasing the confidence in functional hypotheses derived from predictions. Therefore, we assessed the quality and the potential bias of the different datasets using functional similarity based on the Gene Ontology, structural iPfam domain-domain interactions, likelihood ratios, and topological network parameters. This analysis revealed major differences between predicted datasets, but some of them also scored at least as high as the experimental ones regarding multiple quality measures. Therefore, since only small pair wise overlap between most datasets is observed, they may be combined to enlarge the available human interactome data. For this purpose, we additionally studied the influence of protein length on data quality and the number of disease proteins covered by each dataset. We could further demonstrate that protein interactions predicted by more than one method achieve an elevated reliability.     

Extraction of biological interaction networks from scientific literature

Biology can be regarded as a science of networks: interactions between various biological entities (eg genes, proteins, metabolites) on different levels (eg gene regulation, cell signalling) can be represented as graphs and, thus, analysis of such networks might shed new light on the function of biological systems. Such biological networks can be obtained from different sources. The extraction of networks from text is an important technique that requires the integration of several different computational disciplines. This paper summarises the most important steps in network extraction and reviews common approaches and solutions for the extraction of biological networks from scientific literature.     

Characterization of microRNA-regulated protein-protein interaction network

We have performed topological analysis to elucidate the global correlation between microRNA (miRNA) regulation and protein-protein interaction network in human. The analysis showed that target genes of individual miRNA tend to be hubs and bottlenecks in the network. While proteins directly regulated by miRNA might not form a network module themselves, the miRNA-target genes and their interacting neighbors jointly showed significantly higher modularity. Our findings shed light on how miRNA may regulate the protein interaction network.     

The dissimilarity of species interaction networks

In a context of global changes, and amidst the perpetual modification of community structure undergone by most natural ecosystems, it is more important than ever to understand how species interactions vary through space and time. The integration of biogeography and network theory will yield important results and further our understanding of species interactions. It has, however, been hampered so far by the difficulty to quantify variation among interaction networks. Here, we propose a general framework to study the dissimilarity of species interaction networks over time, space or environments, allowing both the use of quantitative and qualitative data. We decompose network dissimilarity into interactions and species turnover components, so that it is immediately comparable to common measures of β‐diversity. We emphasise that scaling up β‐diversity of community composition to the β‐diversity of interactions requires only a small methodological step, which we foresee will help empiricists adopt this method. We illustrate the framework with a large dataset of hosts and parasites interactions and highlight other possible usages. We discuss a research agenda towards a biogeographical theory of species interactions.