Restoration of plant–pollinator interaction networks via species translocation

The recent decline in pollinator biodiversity, notably in the case of wild bee populations, puts both wild and agricultural ecosystems at risk of ecological community collapse. This has triggered calls for further study of these mutualistic communities in order to more effectively inform restoration of disturbed plant–pollinator communities. Here, we use a dynamic network model to test a variety of translocation strategies for restoring a community after it experiences the loss of some of its species. We consider the reintroduction of extirpated species, both immediately after the original loss and after the community has reequilibrated, as well as the introduction of other native species that were originally absent from the community. We find that reintroducing multiple highly interacting generalist species best restores species richness for lightly disturbed communities. However, for communities that experience significant losses in biodiversity, introducing generalist species that are not originally present in the community may most effectively restore species richness, although in these cases the resultant community often shares few species with the original community. We also demonstrate that the translocation of a single species has a minimal impact on both species richness and the frequency of community collapse. These results have important implications for restoration practices in the face of varying degrees of community perturbations, the refinement of which is crucial for community management.     

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.     

Novel tumor-targeted RGD peptide–camptothecin conjugates: Synthesis and biological evaluation

Five RGD peptide–camptothecin (CPT) conjugates were designed and synthesized with the purpose to improve the therapeutic index of this antitumoral drug family. New RGD cyclopeptides were selected on the basis of their high affinity to α_v integrin receptors overexpressed by tumor cells and their metabolic stability. The conjugates can be divided in two groups: in the first the peptide was attached to the drug through an amide bond, in the second through a hydrazone bond. The main difference between the two spacers lies in their acid stability. Affinity to the receptors was maintained for all conjugates and their internalization into tumor cells was demonstrated. The first group conjugates showed lower in vitro and in vivo activity than the parent drug, probably due to the excessive stability of the amide bond, even inside the tumor cells. Conversely, the hydrazone conjugates exhibited in vitro tumor cell inhibition similar to the parent drug, indicating high conversion in the culture medium and/or inside the cells, but their poor solubility hampered in vivo experiments. On the basis of these results, information was acquired for additional development of derivatives with different linkers and better solubility for in vivo evaluation.     

Evolutionarily conserved motifs and modules in mitochondrial protein–protein interaction networks

Advances in organelle interactomics have led to new insights into organelle functions. In this study, we considered the common mitochondrial PIN of four evolutionarily distant eukaryotic species, namely Homo sapiens, Mus musculus, Drosophila melanogaster and Caenorhabditis elegans. By comparative interactomics analysis of mitochondrial PINs in these organisms, five conserved modules were identified. Modules comprise the main mitochondrial tasks, including proteins involved in translation process, mitochondrial import inner membrane proteins, TCA cycle enzymes, mitochondrial electron transport chain, and metabolic enzymes. Furthermore, we reemphasize that subgraphs of network, i.e., motifs and themes, may represent evolutionarily conserved topological units which are biologically significant.     

Advances in the study of protein–DNA interaction

Protein-DNA interaction plays an important role in many biological processes. The classical methods and the novel technologies advanced have been developed for the interaction of protein-DNA. Recent developments of these methods and research achievements have been reviewed in this paper.     

Protein–protein interaction networks studies and importance of 3D structure knowledge

Protein–protein interaction networks (PPINs) are a powerful tool to study biological processes in living cells. In this review, we present the progress of PPIN studies from abstract to more detailed representations. We will focus on 3D interactome networks, which offer detailed information at the atomic level. This information can be exploited in understanding not only the underlying cellular mechanisms, but also how human variants and disease-causing mutations affect protein functions and complexes’ stability. Recent studies have used structural information on PPINs to also understand the molecular mechanisms of binding partner selection. We will address the challenges in generating 3D PPINs due to the restricted number of solved protein structures. Finally, some of the current use of 3D PPINs will be discussed, highlighting their contribution to the studies in genotype–phenotype relationships and in the optimization of targeted studies to design novel chemical compounds for medical treatments.     

Protein–protein interaction networks improve the proteomics data interpretation in induced apoptosis

Evaluation of: Deighton RF, Kerr LE, Short DM et al. Network generation enhances interpretation of proteomics data from induced apoptosis. Proteomics DOI: 10.1002/pmic.200900112 (2010) (Epub ahead of print).

The huge ongoing improvements in proteomics technologies, including the development of high-throughput mass spectrometry, are resulting in ever increasing information on protein behavior during cellular processes. The exponential accumulation of proteomics data has the promise to advance biomedical sciences by shedding light on the most important events that regulate mammalian cells under normal and pathophysiological conditions. This may provide practical insights that will impact medical practice and therapy, and may permit the development of a new generation of personalized therapeutics. Proteomics, as a powerful tool, creates numerous opportunities as well as challenges. At the different stages, data interpretation requires proteomics analysis, various tools to help deal with large proteomics data banks and the extraction of more functional information. Network analysis tools facilitate proteomics data interpretation and predict protein functions, functional interactions and in silica identification of intracellular pathways. The work reported by Deighton and colleagues illustrates an example of improving proteomics data interpretation by network generation. The authors used ingenuity pathway analysis to generate a protein network predicting direct and indirect interaction between 13 proteins found to be affected by staurosporine treatment. Importantly, the authors highlight the caution required when interpreting the results from a small number of proteins analyzed using network analysis tools.     

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?     

Potential for climate effects on the size-structure of host–parasitoid indirect interaction networks

Communities of insect herbivores are thought to be structured mainly by indirect processes mediated by shared natural enemies, such as apparent competition. In host–parasitoid interaction networks, overlap in natural enemy communities between any pair of host species depends on the realized niches of parasitoids, which ultimately depend on the foraging decisions of individuals. Optimal foraging theory predicts that egg-limited parasitoid females should reject small hosts in favour of future opportunities to oviposit in larger hosts, while time-limited parasitoids are expected to optimize oviposition rate regardless of host size. The degree to which parasitoids are time- or egg-limited depends in part on weather conditions, as this determines the proportion of an individual''s lifespan that is available to foraging. Using a 10-year time series of monthly quantitative host–parasitoid webs, we present evidence for host-size-based electivity and sex allocation in the common secondary parasitoid Asaphes vulgaris. We argue that this electivity leads to body-size-dependent asymmetry in apparent competition among hosts and we discuss how changing weather patterns, as a result of climate change, may impact foraging behaviour and thereby the size-structure and dynamics of host–parasitoid indirect interaction networks.     

181 Integrative analysis of gene expression and protein–protein interaction networks in Glioblastoma

Glioblastoma multiforme (GBM) is the most malignant of all the brain tumors with very low median survival time of one year, as per Central Brain Tumor Registry of the USA, 2001. Efforts are ongoing to understand this disease pathogenesis in complete details. Global gene expression changes in GBM pathogenesis have been studied by several groups using microarray technology (e.g. Carro et al., 2010). One of the many approaches to ‘understand the control mechanisms underlying the observed changes in the activity of a biological process’ (Cline et al., 2007) is integration of gene expression and protein–protein interactions (PPI) datasets. Among several examples, aberrant activation of Wnt/β-catenin signaling pathway as well as sonic hedgehog (SHH) signaling pathway is reported in GBMs (Klaus & Birchmeier, 2008). Further, these two pathways are also involved in proliferation and clonogenicity of glioma cancer stem cells (Li et al., 2009), which are thought to play a role in glioma initiation, proliferation, and invasion, and are one of the important points of intervention. Hedgehog–Gli1 signaling is also found to regulate the expression of stemness genes. In this paper, analyses of the relationship between the significant differential expression of these and other genes and the connectivity as well as topological features of a PPI network would be discussed. This way, genes potentially overlooked when relying solely on expression profiles may be identified which can be biologically relevant as possible drug target/s or disease biomarker/s.     

Environmental vibrio phage–bacteria interaction networks reflect the genetic structure of host populations

Phages depend on their bacterial hosts to replicate. The habitat, density and genetic diversity of host populations are therefore key factors in phage ecology, but our ability to explore their biology depends on the isolation of a diverse and representative collection of phages from different sources. Here, we compared two populations of marine bacterial hosts and their phages collected during a time series sampling program in an oyster farm. The population of Vibrio crassostreae, a species associated specifically to oysters, was genetically structured into clades of near clonal strains, leading to the isolation of closely related phages forming large modules in phage–bacterial infection networks. For Vibrio chagasii, which blooms in the water column, a lower number of closely related hosts and a higher diversity of isolated phages resulted in small modules in the phage–bacterial infection network. Over time, phage load was correlated with V. chagasii abundance, indicating a role of host blooms in driving phage abundance. Genetic experiments further demonstrated that these phage blooms can generate epigenetic and genetic variability that can counteract host defence systems. These results highlight the importance of considering both the environmental dynamics and the genetic structure of the host when interpreting phage–bacteria networks.     

Protein–protein interaction networks suggest different targets have different propensities for triggering drug resistance

Emergence of drug resistance is a major problem in the treatment of many diseases including tuberculosis. To tackle the problem from a wholistic perspective, it is essential to understand the molecular mechanisms by which bacteria acquire drug resistance using a systems approach. Availability of genome-scale data of expression profiles under different drug exposed conditions and protein–protein interactions, makes it feasible to reconstruct and analyze systems-level models. A number of proteins involved in different resistance mechanisms, referred to as the resistome are identified from literature. The interaction of the drug directly with the resistome is unable to explain most resistance processes adequately, including that of increased mutations in the target’s binding site. We recently hypothesized that some communication might exist from the drug environment to the resistome to trigger emergence of drug resistance. We report here a network based approach to identify most plausible paths of such communication in Mycobacterium tuberculosis. Networks capturing both structural and functional linkages among various proteins were weighted based on gene expression profiles upon exposure to specific drugs and betweenness centrality of the interactions. Our analysis suggests that different drug targets and hence different drugs could trigger the resistome to different extents and through different routes. The identified paths correlate well with the mechanisms known through experiment. Some examples of the top ranked hubs in multiple drug specific networks are PolA, FadD1, CydA, a monoxygenase and GltS, which could serve as co-targets, that could be inhibited in order to retard resistance related communication in the cell.     

LEVELNET to visualize,explore, and compare protein–protein interaction networks

Physical interactions between proteins are central to all biological processes. Yet, the current knowledge of who interacts with whom in the cell and in what manner relies on partial, noisy, and highly heterogeneous data. Thus, there is a need for methods comprehensively describing and organizing such data. LEVELNET is a versatile and interactive tool for visualizing, exploring, and comparing protein–protein interaction (PPI) networks inferred from different types of evidence. LEVELNET helps to break down the complexity of PPI networks by representing them as multi-layered graphs and by facilitating the direct comparison of their subnetworks toward biological interpretation. It focuses primarily on the protein chains whose 3D structures are available in the Protein Data Bank. We showcase some potential applications, such as investigating the structural evidence supporting PPIs associated to specific biological processes, assessing the co-localization of interaction partners, comparing the PPI networks obtained through computational experiments versus homology transfer, and creating PPI benchmarks with desired properties.     

Core modification of substituted piperidines as Novel inhibitors of HDM2–p53 protein–protein interaction

The discovery of 3,3-disubstituted piperidine 1 as novel p53–HDM2 inhibitors prompted us to implement subsequent SAR follow up directed towards piperidine core modifications. Conformational restrictions and further functionalization of the piperidine core were investigated as a strategy to gain additional interactions with HDM2. Substitutions at positions 4, 5 and 6 of the piperidine ring were explored. Although some substitutions were tolerated, no significant improvement in potency was observed compared to 1. Incorporation of an allyl side chain at position 2 provided a drastic improvement in binding potency.     

Protein–solvent interaction

Protein folding and assembly can be manipulated in in vitro systems by co-solvents at high concentrations. A number of co-solvents that enhance protein stability and assembly have been shown to be excluded from the protein surface. Such co-solvent exclusion has been demonstrated by dialysis experiments and shown to be correlated with their effects on protein stability and assembly.