Reproducible Propagation of Species-Rich Soil Bacterial Communities Suggests Robust Underlying Deterministic Principles of Community Formation.

Microbiomes are typically characterized by high species diversity but it is poorly understood how such system-level complexity can be generated and propagated. Here, we used soil microcosms as a model to study development of bacterial communities as a function of their starting complexity and environmental boundary conditions. Despite inherent stochastic variation in manipulating species-rich communities, both laboratory-mixed medium complexity (21 soil bacterial isolates in equal proportions) and high-diversity natural top-soil communities followed highly reproducible succession paths, maintaining 16S rRNA gene amplicon signatures prominent for known soil communities in general. Development trajectories and compositional states were different for communities propagated in soil microcosms than in liquid suspension. Compositional states were maintained over multiple renewed growth cycles but could be diverged by short-term pollutant exposure. The different but robust trajectories demonstrated that deterministic taxa-inherent characteristics underlie reproducible development and self-organized complexity of soil microbiomes within their environmental boundary conditions. Our findings also have direct implications for potential strategies to achieve controlled restoration of desertified land. IMPORTANCE There is now a great awareness of the high diversity of most environmental ("free-living") and host-associated microbiomes, but exactly how diverse microbial communities form and maintain is still highly debated. A variety of theories have been put forward, but testing them has been problematic because most studies have been based on synthetic communities that fail to accurately mimic the natural composition (i.e., the species used are typically not found together in the same environment), the diversity (usually too low to be representative), or the environmental system itself (using designs with single carbon sources or solely mixed liquid cultures). In this study, we show how species-diverse soil bacterial communities can reproducibly be generated, propagated, and maintained, either from individual isolates (21 soil bacterial strains) or from natural microbial mixtures washed from top-soil. The high replicate consistency we achieve both in terms of species compositions and developmental trajectories demonstrates the strong inherent deterministic factors driving community formation from their species composition. Generating complex soil microbiomes may provide ways for restoration of damaged soils that are prevalent on our planet

Simulation of a quantum phase transition of polaritons with trapped ions

We present a novel system for the simulation of quantum phase transitions of collective internal qubit and phononic states with a linear crystal of trapped ions. The laser-ion interaction creates an energy gap in the excitation spectrum, which induces an effective phonon-phonon repulsion and a Jaynes-Cummings-Hubbard interaction. This system shows features equivalent to phase transitions of polaritons in coupled cavity arrays. Trapped ions allow for easy tunabilty of the hopping frequency by adjusting the axial trapping frequency, and the phonon-phonon repulsion via the laser detuning and intensity. We propose an experimental protocol to access all observables of the system, which allows one to obtain signatures of the quantum phase transitions even with a small number of ions.Comment: 4 pages, 3 figure

An Introductory Guide to Aligning Networks Using SANA, the Simulated Annealing Network Aligner.

Sequence alignment has had an enormous impact on our understanding of biology, evolution, and disease. The alignment of biological networks holds similar promise. Biological networks generally model interactions between biomolecules such as proteins, genes, metabolites, or mRNAs. There is strong evidence that the network topology-the "structure" of the network-is correlated with the functions performed, so that network topology can be used to help predict or understand function. However, unlike sequence comparison and alignment-which is an essentially solved problem-network comparison and alignment is an NP-complete problem for which heuristic algorithms must be used.Here we introduce SANA, the Simulated Annealing Network Aligner. SANA is one of many algorithms proposed for the arena of biological network alignment. In the context of global network alignment, SANA stands out for its speed, memory efficiency, ease-of-use, and flexibility in the arena of producing alignments between two or more networks. SANA produces better alignments in minutes on a laptop than most other algorithms can produce in hours or days of CPU time on large server-class machines. We walk the user through how to use SANA for several types of biomolecular networks

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

Revisiting Date and Party Hubs: Novel Approaches to Role Assignment in Protein Interaction Networks

The idea of 'date' and 'party' hubs has been influential in the study of protein-protein interaction networks. Date hubs display low co-expression with their partners, whilst party hubs have high co-expression. It was proposed that party hubs are local coordinators whereas date hubs are global connectors. Here we show that the reported importance of date hubs to network connectivity can in fact be attributed to a tiny subset of them. Crucially, these few, extremely central, hubs do not display particularly low expression correlation, undermining the idea of a link between this quantity and hub function. The date/party distinction was originally motivated by an approximately bimodal distribution of hub co-expression; we show that this feature is not always robust to methodological changes. Additionally, topological properties of hubs do not in general correlate with co-expression. Thus, we suggest that a date/party dichotomy is not meaningful and it might be more useful to conceive of roles for protein-protein interactions rather than individual proteins. We find significant correlations between interaction centrality and the functional similarity of the interacting proteins.Comment: 27 pages, 5 main figures, 4 supplementary figure

Fifteen years SIB Swiss Institute of Bioinformatics: life science databases, tools and support.

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) was created in 1998 as an institution to foster excellence in bioinformatics. It is renowned worldwide for its databases and software tools, such as UniProtKB/Swiss-Prot, PROSITE, SWISS-MODEL, STRING, etc, that are all accessible on ExPASy.org, SIB's Bioinformatics Resource Portal. This article provides an overview of the scientific and training resources SIB has consistently been offering to the life science community for more than 15 years

Rich-Club Phenomenon in the Interactome of P. falciparum—Artifact or Signature of a Parasitic Life Style?

Recent advances have provided a first experimental protein interaction map of the human malaria parasite P. falciparum, which appears to be remotely related to interactomes of other eukaryotes. Here, we present a comparative topological analysis of this experimentally determined web with a network of conserved interactions between proteins in S. cerevisiae, C. elegans and D. melanogaster that have an ortholog in Plasmodium. Focusing on experimental interactions, we find a significant presence of a “rich-club,” a topological characteristic that features an “oligarchy” of highly connected proteins being intertwined with one another. In complete contrast, the network of interologs and particularly the web of evolutionary-conserved interactions in P. falciparum lack this feature. This observation prompts the question of whether this result points to a topological signature of the parasite's biology, since experimentally obtained interactions widely cover parasite-specific functions. Significantly, hub proteins that appear in such an oligarchy revolve around invasion functions, shaping an island of parasite-specific activities in a sea of evolutionary inherited interactions. This presence of a biologically unprecedented network feature in the human malaria parasite might be an artifact of the quality and the methods to obtain interaction data in this organism. Yet, the observation that rich-club proteins have distinctive and statistically significant functions that revolve around parasite-specific activities point to a topological signature of a parasitic life style

The Binary Protein Interactome of Treponema pallidum – The Syphilis Spirochete

Protein interaction networks shed light on the global organization of proteomes but can also place individual proteins into a functional context. If we know the function of bacterial proteins we will be able to understand how these species have adapted to diverse environments including many extreme habitats. Here we present the protein interaction network for the syphilis spirochete Treponema pallidum which encodes 1,039 proteins, 726 (or 70%) of which interact via 3,649 interactions as revealed by systematic yeast two-hybrid screens. A high-confidence subset of 991 interactions links 576 proteins. To derive further biological insights from our data, we constructed an integrated network of proteins involved in DNA metabolism. Combining our data with additional evidences, we provide improved annotations for at least 18 proteins (including TP0004, TP0050, and TP0183 which are suggested to be involved in DNA metabolism). We estimate that this “minimal” bacterium contains on the order of 3,000 protein interactions. Profiles of functional interconnections indicate that bacterial proteins interact more promiscuously than eukaryotic proteins, reflecting the non-compartmentalized structure of the bacterial cell. Using our high-confidence interactions, we also predict 417,329 homologous interactions (“interologs”) for 372 completely sequenced genomes and provide evidence that at least one third of them can be experimentally confirmed

Short clones or long clones? A simulation study on the use of paired reads in metagenomics

<p>Abstract</p> <p>Background</p> <p>Metagenomics is the study of environmental samples using sequencing. Rapid advances in sequencing technology are fueling a vast increase in the number and scope of metagenomics projects. Most metagenome sequencing projects so far have been based on Sanger or Roche-454 sequencing, as only these technologies provide long enough reads, while Illumina sequencing has not been considered suitable for metagenomic studies due to a short read length of only 35 bp. However, now that reads of length 75 bp can be sequenced in pairs, Illumina sequencing has become a viable option for metagenome studies.</p> <p>Results</p> <p>This paper addresses the problem of taxonomical analysis of paired reads. We describe a new feature of our metagenome analysis software MEGAN that allows one to process sequencing reads in pairs and makes assignments of such reads based on the combined bit scores of their matches to reference sequences. Using this new software in a simulation study, we investigate the use of Illumina paired-sequencing in taxonomical analysis and compare the performance of single reads, short clones and long clones. In addition, we also compare against simulated Roche-454 sequencing runs.</p> <p>Conclusion</p> <p>This work shows that paired reads perform better than single reads, as expected, but also, perhaps slightly less obviously, that long clones allow more specific assignments than short ones. A new version of the program MEGAN that explicitly takes paired reads into account is available from our website.</p

Identifying protein complexes directly from high-throughput TAP data with Markov random fields

<p>Abstract</p> <p>Background</p> <p>Predicting protein complexes from experimental data remains a challenge due to limited resolution and stochastic errors of high-throughput methods. Current algorithms to reconstruct the complexes typically rely on a two-step process. First, they construct an interaction graph from the data, predominantly using heuristics, and subsequently cluster its vertices to identify protein complexes.</p> <p>Results</p> <p>We propose a model-based identification of protein complexes directly from the experimental observations. Our model of protein complexes based on Markov random fields explicitly incorporates false negative and false positive errors and exhibits a high robustness to noise. A model-based quality score for the resulting clusters allows us to identify reliable predictions in the complete data set. Comparisons with prior work on reference data sets shows favorable results, particularly for larger unfiltered data sets. Additional information on predictions, including the source code under the GNU Public License can be found at http://algorithmics.molgen.mpg.de/Static/Supplements/ProteinComplexes.</p> <p>Conclusion</p> <p>We can identify complexes in the data obtained from high-throughput experiments without prior elimination of proteins or weak interactions. The few parameters of our model, which does not rely on heuristics, can be estimated using maximum likelihood without a reference data set. This is particularly important for protein complex studies in organisms that do not have an established reference frame of known protein complexes.</p