Resulting Interaction Networks Research Articles

From kill the winner to eliminate the winner in open phage-bacteria systems.

Phages and bacteria manage to coexist and sustain ecosystems with a high diversity of strains, despite limited resources and heavy predation. This diversity can be explained by the “kill the winner” model where virulent phages predominantly prey on fast-growing bacteria and thereby suppress the competitive exclusion of slower-growing bacteria. Here we computationally investigate the robustness of these systems against invasions, where new phages or bacteria may interact with more than one of the resident strains. The resulting interaction networks were found to self-organize into a network with strongly interacting specialized predator-prey pairs, resembling that of the “kill the winner” model. Furthermore, the “kill the winner” dynamics is enforced with the occasional elimination of even the fastest-growing bacteria strains due to a phage infecting the fast and slow growers. The frequency of slower-growing strains was increased with the introduction of even a few non-diagonal interactions. Hence, phages capable of infecting multiple hosts play significant roles both in the evolution of the ecosystem by eliminating the winner and in supporting diversity by allowing slow growers to coexist with faster growers.

Butterfly–parasitoid–hostplant interactions in Western Palaearctic Hesperiidae: a DNA barcoding reference library

AbstractThe study of ecological interactions between plants, phytophagous insects and their natural enemies is an essential but challenging component for understanding ecosystem dynamics. Molecular methods such as DNA barcoding can help elucidate these interactions. In this study, we employed DNA barcoding to establish hostplant and parasitoid interactions with hesperiid butterflies, using a complete reference library for Hesperiidae of continental Europe and north-western Africa (53 species, 100% of those recorded) based on 2934 sequences from 38 countries. A total of 233 hostplant and parasitoid interactions are presented, some recovered by DNA barcoding larval remains or parasitoid cocoons. Combining DNA barcode results with other lines of evidence allowed 94% species-level identification for Hesperiidae, but success was lower for parasitoids, in part due to unresolved taxonomy. Potential cases of cryptic diversity, both in Hesperiidae and Microgastrinae, are discussed. We briefly analyse the resulting interaction networks. Future DNA barcoding initiatives in this region should focus attention on north-western Africa and on parasitoids, because in these cases barcode reference libraries and taxonomy are less well developed.

Effects and limitations of a nucleobase-driven backmapping procedure for nucleic acids using steered molecular dynamics

Coarse-grained models can be of great help to address the problem of structure prediction in nucleic acids. On one hand they can make the prediction more efficient, while on the other hand they can also help to identify the essential degrees of freedom and interactions for the description of a number of structures. With the aim to provide an all-atom representation in an explicit solvent to the predictions of our SPlit and conQueR (SPQR) coarse-grained model of RNA, we recently introduced a backmapping procedure which enforces the predicted structure into an atomistic one by means of steered molecular dynamics. These simulations minimize the ERMSD, a particular metric which deals exclusively with the relative arrangement of nucleobases, between the atomistic representation and the target structure. In this paper, we explore the effects of this approach on the resulting interaction networks and backbone conformations by applying it on a set of fragments using as a target their native structure. We find that the geometry of the target structures can be reliably recovered, with limitations in the regions with unpaired bases such as bulges. In addition, we observe that the folding pathway can also change depending on the parameters used in the definition of the ERMSD and the use of other metrics such as the RMSD.

Connection adaption for control of networked mobile chaotic agents

In this paper, we propose a strategy for the control of mobile chaotic oscillators by adaptively rewiring connections between nearby agents with local information. In contrast to the dominant adaptive control schemes where coupling strength is adjusted continuously according to the states of the oscillators, our method does not request adaption of coupling strength. As the resulting interaction structure generated by this proposed strategy is strongly related to unidirectional chains, by investigating synchronization property of unidirectional chains, we reveal that there exists a certain coupling range in which the agents could be controlled regardless of the length of the chain. This feature enables the adaptive strategy to control the mobile oscillators regardless of their moving speed. Compared with existing adaptive control strategies for networked mobile agents, our proposed strategy is simpler for implementation where the resulting interaction networks are kept unweighted at all time.

Redes de interacciones y uso de recursos florales por abejas macho de orquídeas (Hymenoptera: Apidae: Euglossini) en un bosque primario de la región del Chocó (Colombia).

Orchid bees are important keystone pollinators from the Neotropics. With the aim to study the relationships between orchid bees and their nectar and aromatic host species, we made systematic samplings of males across two conservation areas in the biogeographic Choc6 Region of Colombia. We used chemical baits to collect 352 male bees during five months. The pollen attached to their bodies was extracted for palynological identification and to estimate interaction networks. The euglossine community consisted of at least 22 species including Eg. maculilabris, Eg. orellana, Eg. championi and Eg. ignita. The male bees were associated with 84 plants but depended on a small group of them (Peperomia spp. and Anthurium spp, as well as species of Solanaceae, Ericaceae and Malpighiaceae) which were widely distributed across the altitudinal gradient, and were available through the year. The resulting interaction networks revealed a typical nested pattern usually found in plant-pollinator interactions, with several rare bee and plant species interaction with a small group of generalist bees and plant species. Albeit, we found variation within networks related to species composition. Such variation may be a consequence of specific differences in plant flowering phenology.

Exploring mitochondrial system properties of neurodegenerative diseases through interactome mapping

Mitochondria are double membraned, dynamic organelles that are required for a large number of cellular processes, and defects in their function have emerged as causative factors for a growing number of human disorders and are highly associated with cancer, metabolic, and neurodegenerative (ND) diseases. Biochemical and genetic investigations have uncovered small numbers of candidate mitochondrial proteins (MPs) involved in ND disease, but given the diversity of processes affected by MP function and the difficulty of detecting interactions involving these proteins, many more likely remain unknown. However, high-throughput proteomic and genomic approaches developed in genetically tractable model prokaryotes and lower eukaryotes have proven to be effective tools for querying the physical (protein-protein) and functional (gene-gene) relationships between diverse types of proteins, including cytosolic and membrane proteins. In this review, we highlight how experimental and computational approaches developed recently by our group and others can be effectively used towards elucidating the mitochondrial interactome in an unbiased and systematic manner to uncover network-based connections. We discuss how the knowledge from the resulting interaction networks can effectively contribute towards the identification of new mitochondrial disease gene candidates, and thus further clarify the role of mitochondrial biology and the complex etiologies of ND disease. Biochemical and genetic investigations have uncovered small numbers of candidate mitochondrial proteins (MPs) involved in neurodegenerative (ND) diseases, but given the diversity of processes affected by MP function and the difficulty of detecting interactions involving these proteins, many more likely remain unknown. Large-scale proteomic and genomic approaches developed in model prokaryotes and lower eukaryotes have proven to be effective tools for querying the physical (protein-protein) and functional (gene-gene) relationships between diverse types of proteins. Extension of this new framework to the mitochondrial sub-system in human will likewise provide a universally informative systems-level view of the physical and functional landscape for exploring the evolutionary principles underlying mitochondrial function. In this review, we highlight how experimental and computational approaches developed recently by our group and others can be effectively used towards elucidating the mitochondrial interactome in an unbiased and systematic manner to uncover network-based connections. We anticipate that the knowledge from these resulting interaction networks can effectively contribute towards the identification of new mitochondrial disease gene candidates, and thus foster a deeper molecular understanding of mitochondrial biology as well as the etiology of mitochondrial diseases. This article is part of a Special Issue: Can Proteomics Fill the Gap Between Genomics and Phenotypes?

Hierarchical Bayesian models in ecology: Reconstructing species interaction networks from non-homogeneous species abundance data

The relationships among organisms and their surroundings can be of immense complexity. To describe and understand an ecosystem as a tangled bank, multiple ways of interaction and their effects have to be considered, such as predation, competition, mutualism and facilitation. Understanding the resulting interaction networks is a challenge in changing environments, e.g. to predict knock-on effects of invasive species and to understand how climate change impacts biodiversity. The elucidation of complex ecological systems with their interactions will benefit enormously from the development of new machine learning tools that aim to infer the structure of interaction networks from field data. In the present study, we propose a novel Bayesian regression and multiple changepoint model (BRAM) for reconstructing species interaction networks from observed species distributions. The model has been devised to allow robust inference in the presence of spatial autocorrelation and distributional heterogeneity. We have evaluated the model on simulated data that combines a trophic niche model with a stochastic population model on a 2-dimensional lattice, and we have compared the performance of our model with L1-penalized sparse regression (LASSO) and non-linear Bayesian networks with the BDe scoring scheme. In addition, we have applied our method to plant ground coverage data from the western shore of the Outer Hebrides with the objective to infer the ecological interactions.

Structure and Evolution of Streptomyces Interaction Networks in Soil and In Silico

Soil grains harbor an astonishing diversity of Streptomyces strains producing diverse secondary metabolites. However, it is not understood how this genotypic and chemical diversity is ecologically maintained. While secondary metabolites are known to mediate signaling and warfare among strains, no systematic measurement of the resulting interaction networks has been available. We developed a high-throughput platform to measure all pairwise interactions among 64 Streptomyces strains isolated from several individual grains of soil. We acquired more than 10,000 time-lapse movies of colony development of each isolate on media containing compounds produced by each of the other isolates. We observed a rich set of such sender-receiver interactions, including inhibition and promotion of growth and aerial mycelium formation. The probability that two random isolates interact is balanced; it is neither close to zero nor one. The interactions are not random: the distribution of the number of interactions per sender is bimodal and there is enrichment for reciprocity—if strain A inhibits or promotes B, it is likely that B also inhibits or promotes A. Such reciprocity is further enriched in strains derived from the same soil grain, suggesting that it may be a property of coexisting communities. Interactions appear to evolve rapidly: isolates with identical 16S rRNA sequences can have very different interaction patterns. A simple eco-evolutionary model of bacteria interacting through antibiotic production shows how fast evolution of production and resistance can lead to the observed statistical properties of the network. In the model, communities are evolutionarily unstable—they are constantly being invaded by strains with new sets of interactions. This combination of experimental and theoretical observations suggests that diverse Streptomyces communities do not represent a stable ecological state but an intrinsically dynamic eco-evolutionary phenomenon.

Bio::Homology::InterologWalk - A Perl module to build putative protein-protein interaction networks through interolog mapping

BackgroundProtein-protein interaction (PPI) data are widely used to generate network models that aim to describe the relationships between proteins in biological systems. The fidelity and completeness of such networks is primarily limited by the paucity of protein interaction information and by the restriction of most of these data to just a few widely studied experimental organisms. In order to extend the utility of existing PPIs, computational methods can be used that exploit functional conservation between orthologous proteins across taxa to predict putative PPIs or 'interologs'. To date most interolog prediction efforts have been restricted to specific biological domains with fixed underlying data sources and there are no software tools available that provide a generalised framework for 'on-the-fly' interolog prediction.ResultsWe introduce Bio::Homology::InterologWalk, a Perl module to retrieve, prioritise and visualise putative protein-protein interactions through an orthology-walk method. The module uses orthology and experimental interaction data to generate putative PPIs and optionally collates meta-data into an Interaction Prioritisation Index that can be used to help prioritise interologs for further analysis. We show the application of our interolog prediction method to the genomic interactome of the fruit fly, Drosophila melanogaster. We analyse the resulting interaction networks and show that the method proposes new interactome members and interactions that are candidates for future experimental investigation.ConclusionsOur interolog prediction tool employs the Ensembl Perl API and PSICQUIC enabled protein interaction data sources to generate up to date interologs 'on-the-fly'. This represents a significant advance on previous methods for interolog prediction as it allows the use of the latest orthology and protein interaction data for all of the genomes in Ensembl. The module outputs simple text files, making it easy to customise the results by post-processing, allowing the putative PPI datasets to be easily integrated into existing analysis workflows. The Bio::Homology::InterologWalk module, sample scripts and full documentation are freely available from the Comprehensive Perl Archive Network (CPAN) under the GNU Public license.

STRING and STITCH: known and predicted interactions between proteins and chemicals

AbstractInformation on protein-protein and protein-chemical interactions is essential for understanding cellular functions. The STRING and STITCH web resources integrate interaction evidence derived from pathways, automatic literature mining, primary experimental data, and genomic context. The resulting interaction networks cover 1.5 million proteins from 373 organisms and 68,000 chemicals.

Quantification of PDZ Domain Specificity, Prediction of Ligand Affinity and Rational Design of Super-binding Peptides

Transient macromolecular complexes are often formed by protein-protein interaction domains (e.g. PDZ, SH2, SH3, WW) which recognize linear sequence motifs with in vitro affinities typically in the micromolar range. The analysis of the resulting interaction networks requires a quantification of domain specificity and selectivity towards all possible ligands with physiologically relevant affinity. As representative examples, we determined specificity as a function of ligand sequence-dependent affinity contributions by statistical analysis of peptide library screens for the AF6, ERBIN and SNA1 (alpha-1-syntrophin) PDZ domains. For this purpose, the three PDZ domains were first screened for binding with a peptide library comprising 6223 human C termini created by SPOT synthesis. Based on the detected ligand preferences, we designed focused peptide libraries (profile libraries). These libraries were used to quantify the affinity contributions of the four C-terminal ligand residues by means of ANOVA models (analysis of variance) relating the C-terminal ligand sequences to the corresponding dissociation constants. Our models agreed well with experimentally determined dissociation constants and allowed us to design super binding peptides. The latter were shown experimentally to bind to their cognate PDZ domains with the highest affinity. In addition, we determined structure-activity relationships and thereby rationalized the position-specific affinity contributions. Furthermore, we used the statistical models to predict the dissociation constants for the complete ligand sequence space and thus determined the specificity overlap for the three investigated PDZ domains (). Altogether, we present an efficient method for profiling protein-protein interaction domains that provides a biophysical picture of specificity and selectivity. This approach allows the rational design of functional experiments and provides a basis for simulating interaction networks in the field of systems biology.

Comparative analysis of protein interaction networks

Recent advances in proteomics and computational biology have lead to a flood of protein interaction data and resulting interaction networks (e.g. (Gavin et al., 2002)). Here I first analyse the status and quality of parts lists (genes and proteins), then comparatively assess large-scale protein interaction data (von Mering et al., 2002) and finally try to identify biological meaningful units (e.g. pathways, cellular processes) within interaction networks that are derived from the conservation of gene neighborhood (Snel et al., 2002). Possible extensions of gene neighborhood analysis to eukaryotes (von Mering and Bork, 2002) will be discussed.