On the functional and structural characterization of hubs in protein–protein interaction networks

A number of interesting issues have been addressed on biological networks about their global and local properties. The connection between the topological properties of proteins in Protein–Protein Interaction (PPI) networks and their biological relevance has been investigated focusing on hubs, i.e. proteins with a large number of interacting partners. We will survey the literature trying to answer the following questions: Do hub proteins have special biological properties? Do they tend to be more essential than non-hub proteins? Are they more evolutionarily conserved? Do they play a central role in modular organization of the protein interaction network? Are there structural properties that characterize hub proteins?     

Why nestedness in mutualistic networks?

We investigate the relationship between the nested organization of mutualistic systems and their robustness against the extinction of species. We establish that a nested pattern of contacts is the best possible one as far as robustness is concerned, but only when the least linked species have the greater probability of becoming extinct. We introduce a coefficient that provides a quantitative measure of the robustness of a mutualistic system.     

How do oncoprotein mutations rewire protein–protein interaction networks?

The acquisition of mutations that activate oncogenes or inactivate tumor suppressors is a primary feature of most cancers. Mutations that directly alter protein sequence and structure drive the development of tumors through aberrant expression and modification of proteins, in many cases directly impacting components of signal transduction pathways and cellular architecture. Cancer-associated mutations may have direct or indirect effects on proteins and their interactions and while the effects of mutations on signaling pathways have been widely studied, how mutations alter underlying protein–protein interaction networks is much less well understood. Systematic mapping of oncoprotein protein interactions using proteomics techniques as well as computational network analyses is revealing how oncoprotein mutations perturb protein–protein interaction networks and drive the cancer phenotype.     

Why do protein architectures have boltzmann-like statistics?

A theoretical study has shown that the occurrence of various structural elements in stable folds of random copolymers is exponentially dependent on the own energy of the element. A similar occurrence-on-energy dependence is observed in globular proteins¹ from the level of amino acid conformations to the level of overall architectures. Thus, the structural features stabilized by many random sequences are typical of globular proteins while the features rarely observed in proteins are those which are stabilized by only a minor part of the random sequences. © 1995 Wiley-Liss, Inc.     

How to identify essential genes from molecular networks?

Background  

The prediction of essential genes from molecular networks is a way to test the understanding of essentiality in the context of what is known about the network. However, the current knowledge on molecular network structures is incomplete yet, and consequently the strategies aimed to predict essential genes are prone to uncertain predictions. We propose that simultaneously evaluating different network structures and different algorithms representing gene essentiality (centrality measures) may identify essential genes in networks in a reliable fashion.     

Adhesion of cells to protein carpets: do cells' feet have to be black?

In most physiological situations, cell contact with a substratum is mediated by proteins of extracellular matrix. Therefore, an increasing number of cell-substratum adhesion studies employ substrata covered with one or more proteins of extracellular matrix. To visualize the most adhesive cell structures, focal contacts and focal adhesions, the interference reflection microscopy has been widely used. It has been generally accepted that these strongly adhesive structures can be seen as black streaks in interference reflection microscopy. Calculations are presented herein, which although simplified, suggest that when cells are plated on protein-covered substrata, their focal contacts may not always appear black in interference reflection microscopy.     

Why do traditional dispersion indices used for analysis of spatial distribution of plants tend to become obsolete?

It is common to characterize the spatial distribution of plant patterns as random, aggregate, or uniform. In this context, a major challenge for the researcher is the choice of the method to identify the spatial pattern correctly as well as the factors related to it. The vast literature on the subject is not recent, especially regarding the dispersion indices. The aim of this review was to conduct a critical and temporal analysis of these dispersion indices and test their effectiveness in determining the spatial distribution of Paepalanthus chiquitensis Herzog (Eriocaulaceae). This species is a meaningful model due to its occurrence in specific sites. The Lexis, Charlier, dispersion, relative variance, aggregation, Green, inverse of k of the negative binomial, Morisita, and standardized Morisita indices were limited to indicating that the individuals of the species are aggregate and did not provide information on neither spatial dimension (scale) where the aggregation occurs, nor the factors related to this aggregation. Although they have distinct magnitudes, the algebraic expressions of dispersion, relative variance, aggregation, Green, inverse of k, Morisita, and standardized Morisita indices exhibited a close relationship with each other and little progress from their precursors Lexis and Charlier. By disregarding the possibility of spatial dependence, these indices make it impossible to generate important hypotheses for the investigation of factors related to spatial structure. Therefore, they became obsolete and are falling into disuse. It should be noted that these measurements accomplished their role and contributed to science in times of limited technologies for spatial data.     

Why Do We Expect Carotenoids to be Antioxidants in vivo?

The antioxidant properties of β-carotene, in addition to its proposed immunomodulatory effects, have often been cited as the factors underlying its role in preventing disease initiation and propagation, yet the strongest evidence for diet and cancer prevention is based on fruit and vegetable intake and not β-carotene or other dietary carotenoids, per se. In the light of the outcome of the ATBC trial, the Physicians Health Study and the premature termination of the CARET study, this review addresses the issue of the antioxidant properties of the carotenoids and poses the questions: do dietary carotenes and xanthophylls have a clear role in disease prevention and are their antioxidant properties relevant to this role? What. do we know about their mechanisms of action in vitro as free radical scavengers?     

Complex networks: two ways to be robust?

Recent studies of biological networks have focused on the distribution of the number of links per node. However, the connectivity distribution does not uncover all the complexity of their topology. Here, we analyse the relation between the connectivity of a species and the average connectivity of its nearest neighbours in three of the most resolved community food webs. We compare the pattern arising with the one recently reported for protein networks and for a simple null model of a random network. Whereas two highly connected nodes are unlikely to be connected between each other in protein networks, the reverse happens in food webs. We discuss this difference in organization in relation to the robustness of biological networks to different types of perturbation.     

Why the move to microfluidics for protein analysis?

There has been a recent trend towards the miniaturization of analytical tools, but what are the advantages of microfluidic devices and when is their use appropriate? Recent advances in the field of micro-analytical systems can be classified according to instrument performance (which refers here to the desired property of the analytical tool of interest) and two important features specifically related to miniaturisation, namely reduction of the sample volume and the time-to-result. Here we discuss the contribution of these different parameters and aim to highlight the factors of choice in the development and use of microfluidic devices dedicated to protein analysis.     

Why do cancer cells produce serum amyloid a acute-phase protein?

The level of acute-phase serum amyloid A (SAA) protein in human blood dramatically grows in cancer, often at its early stage, when acute inflammatory signs are not observed. This fact was registered both by immunochemistry and by proteomics methods in different common cancers, such as lung, ovarian, renal, uterine, and nasopharyngeal cancer and in melanoma. It was proposed that SAA is produced by liver in such cases, as in inflammation, high levels of SAA being a part of nonspecific response to tumor. However, that was not always true, because, in many cancers, the protein of interest is produced directly by cancer cells. What is the biological significance of this observation? What preferences do cancer cells obtain due to SAA overexpression? Recent data on melanoma patients have shown that serum amyloid A is able to stimulate immunosuppressive neutrophils to produce interleukin-10 cytokine that suppressed cell immunity. The ability of cancer cells to produce SAA that is acquired during cancer mutagenesis is likely to enhance their resistance to T-cell immunity due to activation of immunosuppressive granulocytes.     

Why do cladocerans fail to control algal blooms?

Field studies show that even at high nutrient loads phytoplankton may be kept at low levels by filter-feeding zooplankton for a period of weeks (spring clear water phase in lakes) or months (low-stocked fish-ponds). In the absence of planktivorous fish, large-bodied cladocerans effectively control the abundance of algae of a broad size spectrum. Laboratory experiments show that, although difficult to handle and of poor nutritional value, filamentous algae can also be utilized by large-bodiedDaphnia and prevented from population increase, exactly as the principles of the biomanipulation approach would predict. This is not always the case, however. Even when released from predation, large cladocerans often cannot grow and reproduce fast enough to prevent bloom formation. Sometimes, they disappear when the bloom becomes dense, and the biomanipulation approach is not applicable any more. Recent experimental data on four differently-sizedDaphnia species are used in an attempt to (1) explain why cladocerans fail to control filamentous cyanobacteria when filament density is high, and (2) determine the critical filament density at whichDaphnia becomes ineffective. At this critical concentration,Daphnia growth and reproduction is halted, and no positive numerical response to growing phytoplankton standing crop should be expected fromDaphnia population. Bloom formation thus becomes irreversible. The question of what can be done to overcome this bottleneck of the biomanipulation approach may become one of the most challenging questions in plankton ecology in the nearest future.     

How biologically relevant are interaction-based modules in protein networks?

By applying a graph-based algorithm to yeast protein-interaction networks we have extracted modular structures and show that they can be validated using information from the phylogenetic conservation of the network components. We show that the module cores, the parts with the highest intramodular connectivity, are biologically relevant components of the networks. These constituents correlate only weakly with other levels of organization. We also discuss how such structures could be used for finding targets for antimicrobial drugs.     

Why do stigmas move in a flexistylous plant?

Flexistyly is a recently documented stylar polymorphism involving both spatial and temporal segregation of sex roles within hermaphroditic flowers. Using the experimental manipulation of stigma movement in self-compatible Alpinia mutica, we tested the hypothesis that selection for reducing interference between male and female function drives the evolution and/or maintenance of stigma movement. In experimental arrays, anaflexistylous (protogynous) flowers served as pollen donors competing for mating opportunities on cataflexistylous (protandrous) flowers. The pollen donors were either manipulated so their stigmas could not move or were left intact, and their success was determined using allozymes to assess the paternity of recipient seeds. We found that manipulated flowers sired a significantly smaller proportion of seeds, showing that stigma movement in unmanipulated plants increased male fitness. This result was strongest under conditions in which pollen competition was expected to be highest, specifically when pollinators visited multiple donor plants before visiting recipient flowers.     

Why do Luehdorfia butterflies lay eggs in clusters?

Luehdorfia butterflies lay eggs in clusters. Clones of their host plants (Asiasarum and Heterotropa) are distributed pacthily among the understory of deciduous forests. Groups of Luehdorfia larvae often exhaust the clones and may wander over the forest floor seeking new clones. The highest mortality observed is during this wandering period. To elucidate why Luehdorfia butterflies lay eggs in clusters, a simulation experiment was made for hypothetical populations which lay eggs in clusters or singly. Field data on larval mortality, consumption, density of host clones and leaf weight for Luehdorfia japonica were incorporated into the model. The predictions of the simulation were: (1) When the egg density is low, the single egg type could leave many more pupae than the egg clustering type, but when the egg density is high, the former might leave smaller number of pupae than the latter; and (2) There are optimal sizes of egg clusters for different egg densities and the optimal size becomes larger as the egg density increased.