The evolutionary dynamics of protein-protein interaction networks inferred from the reconstruction of ancient networks

Cellular functions are based on the complex interplay of proteins, therefore the structure and dynamics of these protein-protein interaction (PPI) networks are the key to the functional understanding of cells. In the last years, large-scale PPI networks of several model organisms were investigated. Methodological improvements now allow the analysis of PPI networks of multiple organisms simultaneously as well as the direct modeling of ancestral networks. This provides the opportunity to challenge existing assumptions on network evolution. We utilized present-day PPI networks from integrated datasets of seven model organisms and developed a theoretical and bioinformatic framework for studying the evolutionary dynamics of PPI networks. A novel filtering approach using percolation analysis was developed to remove low confidence interactions based on topological constraints. We then reconstructed the ancient PPI networks of different ancestors, for which the ancestral proteomes, as well as the ancestral interactions, were inferred. Ancestral proteins were reconstructed using orthologous groups on different evolutionary levels. A stochastic approach, using the duplication-divergence model, was developed for estimating the probabilities of ancient interactions from today's PPI networks. The growth rates for nodes, edges, sizes and modularities of the networks indicate multiplicative growth and are consistent with the results from independent static analysis. Our results support the duplication-divergence model of evolution and indicate fractality and multiplicative growth as general properties of the PPI network structure and dynamics

Deep metagenome and metatranscriptome analyses of microbial communities affiliated with an industrial biogas fermenter, a cow rumen, and elephant feces reveal major differences in carbohydrate hydrolysis strategies

Additional file 4. Compressed rar file containing the bins generated from the biogas fermenter metagenome, part 4 of 4

The Iceman's Last Meal Consisted of Fat, Wild Meat, and Cereals

The history of humankind is marked by the constant adoption of new dietary habits affecting human physiology, metabolism, and even the development of nutrition-related disorders. Despite clear archaeological evidence for the shift from hunter-gatherer lifestyle to agriculture in Neolithic Europe [1], very little information exists on the daily dietary habits of our ancestors. By undertaking a complementary -omics approach combined with microscopy, we analyzed the stomach content of the Iceman, a 5,300-yearold European glacier mummy [2, 3]. He seems to have had a remarkably high proportion of fat in his diet, supplemented with fresh or dried wild meat, cereals, and traces of toxic bracken. Our multipronged approach provides unprecedented analytical depth, deciphering the nutritional habit, meal composition, and food-processing methods of this Copper Age individual

Critical Assessment of Metagenome Interpretation:A benchmark of metagenomics software

International audienceIn metagenome analysis, computational methods for assembly, taxonomic profilingand binning are key components facilitating downstream biological datainterpretation. However, a lack of consensus about benchmarking datasets andevaluation metrics complicates proper performance assessment. The CriticalAssessment of Metagenome Interpretation (CAMI) challenge has engaged the globaldeveloper community to benchmark their programs on datasets of unprecedentedcomplexity and realism. Benchmark metagenomes were generated from newlysequenced ~700 microorganisms and ~600 novel viruses and plasmids, includinggenomes with varying degrees of relatedness to each other and to publicly availableones and representing common experimental setups. Across all datasets, assemblyand genome binning programs performed well for species represented by individualgenomes, while performance was substantially affected by the presence of relatedstrains. Taxonomic profiling and binning programs were proficient at high taxonomicranks, with a notable performance decrease below the family level. Parametersettings substantially impacted performances, underscoring the importance ofprogram reproducibility. While highlighting current challenges in computationalmetagenomics, the CAMI results provide a roadmap for software selection to answerspecific research questions

Scaling exponents (, , ) for the different species.

<p>According to the values of the scaling exponents, the seven species listed are grouped into two categories: scale-free fractal networks and exponential (non-scale-free) fractal networks. The scale-free networks have a power-law degree distribution with exponent , and the non-scale-free fractal networks have an exponential degree distribution with . Notice that none of the networks are small-world. Instead, they are characterized by fractal/modular structures.</p

Scaling exponents, growth rates and their relationships.

<p>Scaling exponents (, , ), growth rates (, , ) and their relationships derived from the dynamic analysis (The growth rates of <i>E. coli</i> do not have uncertainties because there are only two time levels). Here we selected the three largest networks (<i>E. coli</i>, <i>S. cerevisiae</i>, and <i>H. sapiens</i>) and one sample (<i>M. musculus</i>) representing the smaller networks.</p

Fitting parameters in the duplication-divergence model for all organisms.

<p> and are time-independent and describe the probability that an interaction is retained after a duplication and the probability that an interaction is created de novo, respectively. The fraction of interacting pairs in the ancestral network at time is represented by . There are in total nine ancestral time levels for the organisms investigated: the ancestral primates (prNOG), the ancestral rodents (roNOG), the ancestral mammals (maNOG), the ancestral vertebrates (veNOG), the ancestral insects (inNOG), the ancestral animals (meNOG), the ancestral fungi (fuNOG), the ancestral eukaryotes (KOG/euNOG), and the LUCA (COG/NOG). Existing time levels are specific for every species depending on its lineage.</p

An example of the reconstruction process of the <i>S. cerevisiae</i> ancestral networks.

<p>(A) Illustration of the network reconstruction process. A subset of the empirical PPI network of <i>S. cerevisiae</i> is shown. The phylogenetic trees demonstrate how the proteins are grouped into COGs at different evolutionary levels. This information is used to identify the ancestral nodes. Note C2(COG0515) comprises other proteins which are not shown here. (B) The interaction between each pair of COGs is assigned a probability based on the duplication-divergence model. (C) The fractal dimension versus the cutoff for the ancestral prokaryote network of yeast. By increasing , approaches to the value of the present-day network (dashed line). We choose cutoff so that the ancestral network has the some fractal dimension as the present-day network. For , remains (approximately) as a constant.</p