Functional organization and its implication in evolution of the human protein-protein interaction network

Towards a Dynamic Interaction Network of Life to unify and expand the evolutionary theory

The classic Darwinian theory and the Synthetic evolutionary theory and their linear models, while invaluable to study the origins and evolution of species, are not primarily designed to model the evolution of organisations, typically that of ecosystems, nor that of processes. How could evolutionary theory better explain the evolution of biological complexity and diversity? Inclusive network-based analyses of dynamic systems could retrace interactions between (related or unrelated) components. This theoretical shift from a Tree of Life to a Dynamic Interaction Network of Life, which is supported by diverse molecular, cellular, microbiological, organismal, ecological and evolutionary studies, would further unify evolutionary biology.

Identifying protein complexes based on brainstorming strategy

Protein complexes comprising of interacting proteins in protein-protein interaction network (PPI network) play a central role in driving biological processes within cells. Recently, more and more swarm intelligence based algorithms to detect protein complexes have been emerging, which have become the research hotspot in proteomics field. In this paper, we propose a novel algorithm for identifying protein complexes based on brainstorming strategy (IPC-BSS), which is integrated into the main idea of swarm intelligence optimization and the improved K-means algorithm. Distance between the nodes in PPI network is defined by combining the network topology and gene ontology (GO) information. Inspired by human brainstorming process, IPC-BSS algorithm firstly selects the clustering center nodes, and then they are separately consolidated with the other nodes with short distance to form initial clusters. Finally, we put forward two ways of updating the initial clusters to search optimal results. Experimental results show that our IPC-BSS algorithm outperforms the other classic algorithms on yeast and human PPI networks, and it obtains many predicted protein complexes with biological significance.

Genome-wide DNA methylome variation in two genetically distinct chicken lines using MethylC-seq

Background

DNA cytosine methylation is an important epigenetic modification that has significant effects on a variety of biological processes in animals. Avian species hold a crucial position in evolutionary history. In this study, we used whole-genome bisulfite sequencing (MethylC-seq) to generate single base methylation profiles of lungs in two genetically distinct and highly inbred chicken lines (Fayoumi and Leghorn) that differ in genetic resistance to multiple pathogens, and we explored the potential regulatory role of DNA methylation associated with immune response differences between the two chicken lines.

Methods

The MethylC-seq was used to generate single base DNA methylation profiles of Fayoumi and Leghorn birds. In addition, transcriptome profiling using RNA–seq from the same chickens and tissues were obtained to interrogate how DNA methylation regulates gene transcription on a genome-wide scale.

Results

The general DNA methylation pattern across different regions of genes was conserved compared to other species except for hyper-methylation of repeat elements, which was not observed in chicken. The methylation level of miRNA and pseudogene promoters was high, which indicates that silencing of these genes may be partially due to promoter hyper-methylation. Interestingly, the promoter regions of more recently evolved genes tended to be more highly methylated, whereas the gene body regions of evolutionarily conserved genes were more highly methylated than those of more recently evolved genes. Immune-related GO (Gene Ontology) terms were significantly enriched from genes within the differentially methylated regions (DMR) between Fayoumi and Leghorn, which implicates DNA methylation as one of the regulatory mechanisms modulating immune response differences between these lines.

Conclusions

This study establishes a single-base resolution DNA methylation profile of chicken lung and suggests a regulatory role of DNA methylation in controlling gene expression and maintaining genome transcription stability. Furthermore, profiling the DNA methylomes of two genetic lines that differ in disease resistance provides a unique opportunity to investigate the potential role of DNA methylation in host disease resistance. Our study provides a foundation for future studies on epigenetic modulation of host immune response to pathogens in chickens.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2098-8) contains supplementary material, which is available to authorized users.

Dissecting the human protein-protein interaction network via phylogenetic decomposition

The protein-protein interaction (PPI) network offers a conceptual framework for better understanding the functional organization of the proteome. However, the intricacy of network complexity complicates comprehensive analysis. Here, we adopted a phylogenic grouping method combined with force-directed graph simulation to decompose the human PPI network in a multi-dimensional manner. This network model enabled us to associate the network topological properties with evolutionary and biological implications. First, we found that ancient proteins occupy the core of the network, whereas young proteins tend to reside on the periphery. Second, the presence of age homophily suggests a possible selection pressure may have acted on the duplication and divergence process during the PPI network evolution. Lastly, functional analysis revealed that each age group possesses high specificity of enriched biological processes and pathway engagements, which could correspond to their evolutionary roles in eukaryotic cells. More interestingly, the network landscape closely coincides with the subcellular localization of proteins. Together, these findings suggest the potential of using conceptual frameworks to mimic the true functional organization in a living cell.

Atopic dermatitis-associated protein interaction network lead to new insights in chronic sulfur mustard skin lesion mechanisms

Chronic sulfur mustard skin lesions (CSMSLs) are the most common complications of sulfur mustard exposure; however, its mechanism is not completely understood.According to clinical signs, there are similarities between CSMSL and atopic dermatitis (AD). In this study, proteomic results of AD were reviewed and the AD-associated protein-protein interaction network (PIN) was analyzed. According to centrality measurements, 16 proteins were designated as pivotal elements in AD mechanisms. Interestingly, most of these proteins had been reported in some sulfur mustard-related studies in late and acute phases separately. Based on the gene enrichment analysis, aging, cell response to stress, cancer, Toll- and NOD-like receptor and apoptosis signaling pathways have the greatest impact on the disease. By the analysis of directed protein interaction networks, it is concluded that TNF, IL-6, AKT1, NOS3 and CDKN1A are the most important proteins. It is possible that these proteins play role in the shared complications of AD and CSMSL including xerosis and itching.

BioPortal as a Dataset of Linked Biomedical Ontologies and Terminologies in RDF

BioPortal is a repository of biomedical ontologies-the largest such repository, with more than 300 ontologies to date. This set includes ontologies that were developed in OWL, OBO and other formats, as well as a large number of medical terminologies that the US National Library of Medicine distributes in its own proprietary format. We have published the RDF version of all these ontologies at http://sparql.bioontology.org. This dataset contains 190M triples, representing both metadata and content for the 300 ontologies. We use the metadata that the ontology authors provide and simple RDFS reasoning in order to provide dataset users with uniform access to key properties of the ontologies, such as lexical properties for the class names and provenance data. The dataset also contains 9.8M cross-ontology mappings of different types, generated both manually and automatically, which come with their own metadata.