Mining for novel tumor suppressor genes using a shortest path approach

Cancer, being among the most serious diseases, causes many deaths every year. Many investigators have devoted themselves to designing effective treatments for this disease. Cancer always involves abnormal cell growth with the potential to invade or spread to other parts of the body. In contrast, tumor suppressor genes (TSGs) act as guardians to prevent a disordered cell cycle and genomic instability in normal cells. Studies on TSGs can assist in the design of effective treatments against cancer. In this study, we propose a computational method to discover potential TSGs. Based on the known TSGs, a number of candidate genes were selected by applying the shortest path approach in a weighted graph that was constructed using protein–protein interaction network. The analysis of selected genes shows that some of them are new TSGs recently reported in the literature, while others may be novel TSGs.     

基于EPG的三种刺吸式害虫在苹果苗上的取食行为比较

【目的】苹果绵蚜Eriosoma lanigerum、绣线菊蚜Aphis citricola和梨网蝽Stephanitis nashi是苹果园的一类重要害虫,它们以刺吸式口器对苹果树造成不同程度的危害。本研究旨在明确这3种刺吸式昆虫在苹果树上取食行为差异。【方法】利用刺吸电位(electrical penetration graph, EPG)技术对苹果绵蚜和绣线菊蚜成虫在苹果苗韧皮部和非韧皮部上的EPG指标,以及苹果绵蚜、绣线菊蚜和梨网蝽成虫苹果苗上的取食行为进行了比较分析,分析了这3种害虫的成虫在苹果苗上取食8 h各种波形平均持续时间的占比。【结果】结果表明,苹果绵蚜和绣线菊蚜成蚜在苹果苗上均产生6种取食波形,即非刺探波(np)、路径波(C)、意外穿刺细胞非主动取食细胞波(pd)、木质部取食波(G)、韧皮部唾液分泌波(E1)和韧皮部取食波(E2);而梨网蝽成虫取食过程中只产生非刺探波(np)、表皮刺穿波(A)、叶肉细胞取食波(Gc)和木质部取食波(E)4种波形。从蚜虫在苹果苗非韧皮部上取食的EPG指标看,苹果绵蚜成蚜pd波平均时间显著高于绣线菊蚜的,而刺探次数、np波总时间和pd波次数均显著低于绣线菊蚜的。从蚜虫在苹果苗韧皮部上取食的EPG指标来看,除第1次出现E2波的时间外,各指标没有显著差异。从3种害虫在苹果苗上取食8 h各波形的占比来看,梨网蝽成虫的np波总时间所占比例最高(53%),其次是绣线菊蚜成虫的(24%),苹果绵蚜成虫的最低(为1%)。同时,苹果绵蚜和绣线菊蚜成虫取食波为E2波,所占总时间比例分别为35%和25%,而梨网蝽的取食波为Gc波(占总时间比例为36%)和E波(占总时间比例为11%)。【结论】本研究阐释了苹果园刺吸式口器害虫的生态位分离和取食行为学机制,为果园刺吸式口器害虫的综合治理提供了理论依据。     

GT-Miner: a graph-theoretic data miner, viewer, and model processor

Inexpensive computational power combined with high-throughput experimental platforms has created a wealth of biological information requiring analytical tools and techniques for interpretation. Graph-theoretic concepts and tools have provided an important foundation for information visualization, integration, and analysis of datasets, but they have often been relegated to background analysis tasks. GT-Miner is designed for visual data analysis and mining operations, interacts with other software, including databases, and works with diverse data types. It facilitates a discovery-oriented approach to data mining wherein exploration of alterations of the data and variations of the visualization is encouraged. The user is presented with a basic iterative process, consisting of loading, visualizing, transforming, and then storing the resultant information. Complex analyses are built-up through repeated iterations and user interactions. The iterative process is optimized by automatic layout following transformations and by maintaining a current selection set of interest for elements modified by the transformations. Multiple visualizations are supported including hierarchical, spring, and force-directed self-organizing layouts. Graphs can be transformed with an extensible set of algorithms or manually with an integral visual editor. GT-Miner is intended to allow easier access to visual data mining for the non-expert.     

Can natural selection favour altruism between species?

Darwin suggested that the discovery of altruism between species would annihilate his theory of natural selection. However, it has not been formally shown whether between‐species altruism can evolve by natural selection, or why this could never happen. Here, we develop a spatial population genetic model of two interacting species, showing that indiscriminate between species helping can be favoured by natural selection. We then ask if this helping behaviour constitutes altruism between species, using a linear‐regression analysis to separate the total action of natural selection into its direct and indirect (kin selected) components. We show that our model can be interpreted in two ways, as either altruism within species, or altruism between species. This ambiguity arises depending on whether or not we treat genes in the other species as predictors of an individual's fitness, which is equivalent to treating these individuals as agents (actors or recipients). Our formal analysis, which focuses upon evolutionary dynamics rather than agents and their agendas, cannot resolve which is the better approach. Nonetheless, because a within‐species altruism interpretation is always possible, our analysis supports Darwin's suggestion that natural selection does not favour traits that provide benefits exclusively to individuals of other species.     

Discovering biogeographic and ecological clusters with a graph theoretic spin on factor analysis

Factor analysis (FA) has the advantage of highlighting each semi‐distinct cluster of samples in a data set with one axis at a time, as opposed to simply arranging samples across axes to represent gradients. However, in the case of presence–absence data it is confounded by absences when gradients are long. No statistical model can cope with this problem because the raw data simply do not present underlying information about the length of such gradients. Here I propose an easy way to tease out this information. It is a simple emendation of FA called stepping down, which involves giving an absence a negative value when the missing species nowhere co‐occurs with the species found in the relevant sample. Specifically, a binary co‐occurrence graph is created, and the magnitude of negative values is made a function of how far the graph must be traversed in order to link the missing species with each species that is present. Simulations show that standard FA yields inferior results to FA based on stepped‐down matrices in terms of mapping clusters into axes one‐by‐one. Standard FA is also uninformative when applied to a global bat inventory data set. Step‐down FA (SDFA) easily flags the main biogeographic groupings. Methods like correspondence analysis, non‐metric multidimensional scaling, and Bayesian latent variable modelling are not commensurate with SDFA because they do not seek to find a one‐to‐one mapping of axes and clusters. Stepping down seems promising as a means of illustrating clusters of samples, especially when there are subtle or complex discontinuities in gradients.     

Good predictors for the fixation probability on complex networks of multi-player games using territorial interactions

In 2012 Broom and Rychtar developed a new framework to consider the evolution of a population over a non-homogeneous underlying structure, where fitness depends upon multiplayer interactions amongst the individuals within the population played in groups of various sizes (including one). This included the independent model, and as a special case the territorial raider model, which has been considered in a series of subsequent papers. Here individuals are based upon the vertex of a graph but move to interact with their neighbours, sometimes meeting in large groups. The most important single property of such populations is the fixation probability, the probability of a single mutant completely replacing the existing population. In a recent paper we considered the fixation probability for the Birth Death Birth (BDB) dynamics for three games, a Public Goods game, the Hawk–Dove game and for fixed fitnesses for a large number of randomly generated graphs, in particular seeing if important underlying graph properties could be used as predictors. We found two good predictors, temperature and mean group size, but some interesting and unusual features for one type of graph, Barabasi–Albert graphs. In this paper we use a regression analysis to investigate (the usual) three alternative evolutionary dynamics (BDD, DBB, DBD) in addition to the original BDB. In particular, we find that the dynamics split into two pairs, BDB/DBD and BDD/DBB, each of which give essentially the same results and found a good fit to the data using a quadratic regression involving the above two variables. Further we find that temperature is the most important predictor for the Hawk–Dove game, whilst for the Public Goods game the group size also plays a key role, and is more important than the temperature for the BDD/DBB dynamics.     

Penetration of faba bean sieve elements by pea aphid does not trigger forisome dispersal

Immediately after their stylets penetrate a phloem sieve element, aphids inject saliva into the sieve element for approximately 30–60 s before they begin to ingest phloem sap. This salivation period is recorded as waveform E1 in electrical penetration graph (EPG) monitoring of aphid feeding behavior. It has been hypothesized that the function of this initial period of phloem salivation is to reverse or prevent plugging of the sieve element by one of the plant's phloem defenses: formation of P‐protein plugs or callose synthesis in the sieve pores that connect adjacent sieve elements. This hypothesis was tested using the pea aphid, Acyrthosiphon pisum (Harris) (Hemiptera: Aphididae), and faba bean, Vicia faba L. (Fabaceae), as a model system, and the results do not support the hypothesis. In legumes, such as faba bean, P‐protein plugs in sieve elements are formed by dispersal of proteinaceous bodies called forisomes. Contrary to the hypothesis, the great majority of sieve element penetrations by pea aphid stylets do not trigger forisome dispersal. Thirteen sieve elements were cryofixed early in phloem phase before the aphids could complete their salivation period and the forisomes were not dispersed in any of the 13 samples. However, in these samples, the aphids completed on average a little over half of their normal E1 salivation period before they were cryofixed. Thus, it is possible that sieve element penetration triggered forisome dispersal in these samples but the abbreviated period of salivation was still sufficient to reverse dispersal. To rule out this possibility, 17 sieve elements were cryofixed during R‐pds, which are an EPG waveform associated with sieve element penetration but without the characteristic E1 salivation that occurs during phloem phase. In 16 of the 17 samples, the forisomes were not dispersed. Thus, faba bean sieve elements usually do not form P‐protein plugs in response to penetration by pea aphid stylets. Consequently, the characteristic E1 salivation that occurs at the start of each phloem phase does not seem to be necessary to prevent a plugging response because penetration of sieve elements during R‐pds does not trigger forisome dispersal despite the absence of E1 salivation. Furthermore, as P‐protein plugs do not normally form in response to sieve element penetration, E1 salivation that occurs at the start of each phloem phase is not a response to development of a P‐protein plug. Thus, the E1 salivation period at the beginning of the phloem phase appears to have function(s) unrelated to phloem sealing.     

Protein-protein interaction network-based detection of functionally similar proteins within species

Although functionally similar proteins across species have been widely studied, functionally similar proteins within species showing low sequence similarity have not been examined in detail. Identification of these proteins is of significant importance for understanding biological functions, evolution of protein families, progression of co-evolution, and convergent evolution and others which cannot be obtained by detection of functionally similar proteins across species. Here, we explored a method of detecting functionally similar proteins within species based on graph theory. After denoting protein-protein interaction networks using graphs, we split the graphs into subgraphs using the 1-hop method. Proteins with functional similarities in a species were detected using a method of modified shortest path to compare these subgraphs and to find the eligible optimal results. Using seven protein-protein interaction networks and this method, some functionally similar proteins with low sequence similarity that cannot detected by sequence alignment were identified. By analyzing the results, we found that, sometimes, it is difficult to separate homologous from convergent evolution. Evaluation of the performance of our method by gene ontology term overlap showed that the precision of our method was excellent.