27 research outputs found
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
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
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