The hierarchical organization of natural protein interaction networks confers self-organization properties on pseudocells

BACKGROUND: Cell organization is governed and maintained via specific interactions among its constituent macromolecules. Comparison of the experimentally determined protein interaction networks in different model organisms has revealed little conservation of the specific edges linking ortholog proteins. Nevertheless, some topological characteristics of the graphs representing the networks - namely non-random degree distribution and high clustering coefficient - are shared by networks of distantly related organisms. Here we investigate the role of the topological features of the protein interaction network in promoting cell organization. METHODS: We have used a stochastic model, dubbed ProtNet representing a computer stylized cell to answer questions about the dynamic consequences of the topological properties of the static graphs representing protein interaction networks. RESULTS: By using a novel metrics of cell organization, we show that natural networks, differently from random networks, can promote cell self-organization. Furthermore the ensemble of protein complexes that forms in pseudocells, which self-organize according to the interaction rules of natural networks, are more robust to perturbations. CONCLUSIONS: The analysis of the dynamic properties of networks with a variety of topological characteristics lead us to conclude that self organization is a consequence of the high clustering coefficient, whereas the scale free degree distribution has little influence on this property

The tapeworm interactome: inferring confidence scored protein-protein interactions from the proteome of Hymenolepis microstoma

BACKGROUND: Reference genome and transcriptome assemblies of helminths have reached a level of completion whereby secondary analyses that rely on accurate gene estimation or syntenic relationships can be now conducted with a high level of confidence. Recent public release of the v.3 assembly of the mouse bile-duct tapeworm, Hymenolepis microstoma, provides chromosome-level characterisation of the genome and a stabilised set of protein coding gene models underpinned by bioinformatic and empirical data. However, interactome data have not been produced. Conserved protein-protein interactions in other organisms, termed interologs, can be used to transfer interactions between species, allowing systems-level analysis in non-model organisms. RESULTS: Here, we describe a probabilistic, integrated network of interologs for the H. microstoma proteome, based on conserved protein interactions found in eukaryote model species. Almost a third of the 10,139 gene models in the v.3 assembly could be assigned interaction data and assessment of the resulting network indicates that topologically-important proteins are related to essential cellular pathways, and that the network clusters into biologically meaningful components. Moreover, network parameters are similar to those of single-species interaction networks that we constructed in the same way for S. cerevisiae, C. elegans and H. sapiens, demonstrating that information-rich, system-level analyses can be conducted even on species separated by a large phylogenetic distance from the major model organisms from which most protein interaction evidence is based. Using the interolog network, we then focused on sub-networks of interactions assigned to discrete suites of genes of interest, including signalling components and transcription factors, germline multipotency genes, and genes differentially-expressed between larval and adult worms. Results show not only an expected bias toward highly-conserved proteins, such as components of intracellular signal transduction, but in some cases predicted interactions with transcription factors that aid in identifying their target genes. CONCLUSIONS: With key helminth genomes now complete, systems-level analyses can provide an important predictive framework to guide basic and applied research on helminths and will become increasingly informative as new protein-protein interaction data accumulate

Compact Integration of Multi-Network Topology for Functional Analysis of Genes

The topological landscape of molecular or functional interaction networks provides a rich source of information for inferring functional patterns of genes or proteins. However, a pressing yet-unsolved challenge is how to combine multiple heterogeneous networks, each having different connectivity patterns, to achieve more accurate inference. Here, we describe the Mashup framework for scalable and robust network integration. In Mashup, the diffusion in each network is first analyzed to characterize the topological context of each node. Next, the high-dimensional topological patterns in individual networks are canonically represented using low-dimensional vectors, one per gene or protein. These vectors can then be plugged into off-the-shelf machine learning methods to derive functional insights about genes or proteins. We present tools based on Mashup that achieve state-of-the-art performance in three diverse functional inference tasks: protein function prediction, gene ontology reconstruction, and genetic interaction prediction. Mashup enables deeper insights into the struct ure of rapidly accumulating and diverse biological network data and can be broadly applied to other network science domains. Keywords: interactome analysis; network integration; heterogeneous networks; dimensionality reduction; network diffusion; gene function prediction; genetic interaction prediction; gene ontology reconstruction; drug response predictionNational Institutes of Health (U.S.) (Grant R01GM081871

Systematic assessment of protein interaction data using graph topology approaches

Ph.DDOCTOR OF PHILOSOPH

Heat shock partially dissociates the overlapping modules of the yeast protein-protein interaction network: a systems level model of adaptation

Network analysis became a powerful tool in recent years. Heat shock is a well-characterized model of cellular dynamics. S. cerevisiae is an appropriate model organism, since both its protein-protein interaction network (interactome) and stress response at the gene expression level have been well characterized. However, the analysis of the reorganization of the yeast interactome during stress has not been investigated yet. We calculated the changes of the interaction-weights of the yeast interactome from the changes of mRNA expression levels upon heat shock. The major finding of our study is that heat shock induced a significant decrease in both the overlaps and connections of yeast interactome modules. In agreement with this the weighted diameter of the yeast interactome had a 4.9-fold increase in heat shock. Several key proteins of the heat shock response became centers of heat shock-induced local communities, as well as bridges providing a residual connection of modules after heat shock. The observed changes resemble to a "stratus-cumulus" type transition of the interactome structure, since the unstressed yeast interactome had a globally connected organization, similar to that of stratus clouds, whereas the heat shocked interactome had a multifocal organization, similar to that of cumulus clouds. Our results showed that heat shock induces a partial disintegration of the global organization of the yeast interactome. This change may be rather general occurring in many types of stresses. Moreover, other complex systems, such as single proteins, social networks and ecosystems may also decrease their inter-modular links, thus develop more compact modules, and display a partial disintegration of their global structure in the initial phase of crisis. Thus, our work may provide a model of a general, system-level adaptation mechanism to environmental changes.Comment: 24 pages, 6 figures, 2 tables, 70 references + 22 pages 8 figures, 4 tables and 8 references in the enclosed Supplemen

Double specific betweenness variants for cross disease network analysis

Tese de mestrado em Bioinformática e Biologia Computacional, Universidade de Lisboa, Faculdade de Ciências, 2020Genes associados a doenças (GAD) tendem em agrupar-se em redes celulares definindo módulos na rede. As observações indicam que estes módulos têm grande interação de proteína-proteína e partilham caminhos comuns. É importante identificar estes módulos de doenças, uma vez que poderá ajudar a desvendar os mecanismos moleculares que fazem parte da doença, mas também descobrir novos genes alvos para fármacos. Contudo, achar estes módulos não é fácil, uma vez que o conhecimento atual está em constante progressão, o que leva a redes de doenças incompletas. Medidas da teoria dos grafos podem caracterizar quantitativamente e comparar redes, os seus vértices ou grupos de vértices. Utilizando estas medidas, alguns métodos foram desenvolvidos de modo a melhor identificar os módulos de doenças nas redes. Um desses métodos é o Double Specific-Betweenness (S2B), que prevê GADs partilhados com base nos genes que são achados frequentemente, e, especifica mente, nos caminhos mais curtos entre os módulos das duas doenças relacionadas. O S2B prevê genes comuns entre doenças na sobreposição de ambos os módulos de doenças numa rede não dirigida usando GAD conhecidos. Com isto, o método prevê que vértices presentes na rede têm mais probabilidade de fazer parte de ambos os módulos. Este método difere de outros já existentes, porque prevê vértices que estão associados simultaneamente a duas doenças, enquanto os outros métodos focam-se apenas em priorização de proteínas, apenas para uma doença. A utilização de duas doenças simultaneamente é uma vantagem, uma vez que é possível inferir mecanismos subjacentes a comorbidades em doenças ou identificar vértices, e/ou caminhos que não estavam previamente associados a comorbidade, dando assim a possibilidade de tratar a doença ou sintoma em ambas em vez de atuar numa só. Neste trabalho desenvolvemos variantes mais flexíveis do S2B para redes não dirigidas e dirigidas. Estas variantes foram comparadas em três tipos de redes distintas e foram avaliados em termos de robustez e estabilidade. Os resultados obtidos mostram que o S2B, em conjunto com as variantes que usam passeios aleatórios (SRWR e SLB) são os métodos com melhor performance. Estes métodos têm uma melhor performance na rede de sinalização não dirigida. Os resultados também demonstram que os métodos são robustos a mudanças iniciais nas sementes (variação aleatória de sementes ou no número de sementes usadas). O rácio do módulo de doença completo para o número de sementes de input está correlacionado com a performance do método. Resumindo, este trabalho providencia um guia compreensivo para a aplicação de métodos de previsão da sobreposição com a máxima performance.Disease associate genes (DGs) tend to cluster in cellular networks defining network modules. It has been observed that these modules have many protein-protein interactions and share common pathways. It is important to identify these disease modules, since it can help to unveil the molecular mechanisms that contribute to disease, as well as discovering new candidate drug target genes. However, finding these modules is not an easy task, and, as the current knowledge is still expanding, most known disease network modules are incomplete. Graph theory measures can quantitatively characterize and compare networks, their nodes or group of nodes. Using these measures, some methods were developed to better identify network disease modules. One method is the Double Specific-Betweenness (S2B), that predicts shared DGs based on the genes that are found frequently and specifically in shortest paths between two related disease modules. S2B predicts cross-disease genes in the overlap of both disease modules in an undirected graph using known DGs. With this, the method predicts the nodes present in the graph that are most likely to be part of both disease modules. This method differs from previously existing methods because it predicts nodes that are simultaneously associated with two diseases, while other existing methods focus in protein prioritization only for one disease. Using two diseases at the same time is an advantage, since we can infer new mechanisms underlying comorbid diseases or identify nodes and/or pathways that were not assigned to a comorbidity before, giving the possibility to treat the disease or symptom for both of them instead of a single one. In this work we developed more more flexible S2B variants for undirected and directed networks. These variants were compared in three distinct network types and were evaluated regarding robustness and stability. The obtained results show that S2B, together with variants using random walks (SRWR and SLB) are the best performing methods. These methods perform better with an undirected signalling network. Also, the results show that the methods are robust to changes in initial seeds (random variation of the seeds or the number of seeds used), and to module connectivity. The ratio of complete module size to the number of input seeds is correlated with method performance. In summary, this work provides a comprehensive guide for the application of module overlap prediction methods with maximal performance

Iis - Integrated Interactome System: A Web-based Platform For The Annotation, Analysis And Visualization Of Protein-metabolite-gene-drug Interactions By Integrating A Variety Of Data Sources And Tools

Background: High-throughput screening of physical, genetic and chemical-genetic interactions brings important perspectives in the Systems Biology field, as the analysis of these interactions provides new insights into protein/gene function, cellular metabolic variations and the validation of therapeutic targets and drug design. However, such analysis depends on a pipeline connecting different tools that can automatically integrate data from diverse sources and result in a more comprehensive dataset that can be properly interpreted. Results: We describe here the Integrated Interactome System (IIS), an integrative platform with a web-based interface for the annotation, analysis and visualization of the interaction profiles of proteins/genes, metabolites and drugs of interest. IIS works in four connected modules: (i) Submission module, which receives raw data derived from Sanger sequencing (e.g. two-hybrid system); (ii) Search module, which enables the user to search for the processed reads to be assembled into contigs/singlets, or for lists of proteins/genes, metabolites and drugs of interest, and add them to the project; (iii) Annotation module, which assigns annotations from several databases for the contigs/singlets or lists of proteins/genes, generating tables with automatic annotation that can be manually curated; and (iv) Interactome module, which maps the contigs/singlets or the uploaded lists to entries in our integrated database, building networks that gather novel identified interactions, protein and metabolite expression/concentration levels, subcellular localization and computed topological metrics, GO biological processes and KEGG pathways enrichment. This module generates a XGMML file that can be imported into Cytoscape or be visualized directly on the web. Conclusions: We have developed IIS by the integration of diverse databases following the need of appropriate tools for a systematic analysis of physical, genetic and chemical-genetic interactions. IIS was validated with yeast two-hybrid, proteomics and metabolomics datasets, but it is also extendable to other datasets. 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Genome-Scale Networks Link Neurodegenerative Disease Genes to α-Synuclein through Specific Molecular Pathways

Numerous genes and molecular pathways are implicated in neurodegenerative proteinopathies, but their inter-relationships are poorly understood. We systematically mapped molecular pathways underlying the toxicity of alpha-synuclein (α-syn), a protein central to Parkinson's disease. Genome-wide screens in yeast identified 332 genes that impact α-syn toxicity. To “humanize” this molecular network, we developed a computational method, TransposeNet. This integrates a Steiner prize-collecting approach with homology assignment through sequence, structure, and interaction topology. TransposeNet linked α-syn to multiple parkinsonism genes and druggable targets through perturbed protein trafficking and ER quality control as well as mRNA metabolism and translation. A calcium signaling hub linked these processes to perturbed mitochondrial quality control and function, metal ion transport, transcriptional regulation, and signal transduction. Parkinsonism gene interaction profiles spatially opposed in the network (ATP13A2/PARK9 and VPS35/PARK17) were highly distinct, and network relationships for specific genes (LRRK2/PARK8, ATXN2, and EIF4G1/PARK18) were confirmed in patient induced pluripotent stem cell (iPSC)-derived neurons. This cross-species platform connected diverse neurodegenerative genes to proteinopathy through specific mechanisms and may facilitate patient stratification for targeted therapy. Keywords: alpha-synuclein; iPS cell; Parkinson’s disease; stem cell; mRNA translation; RNA-binding protein; LRRK2; VPS35; vesicle trafficking; yeas

Cluster-based assessment of protein-protein interaction confidence

Background: Protein-protein interaction networks are key to a systems-level understanding of cellular biology. However, interaction data can contain a considerable fraction of false positives. Several methods have been proposed to assess the confidence of individual interactions. Most of them require the integration of additional data like protein expression and interaction homology information. While being certainly useful, such additional data are not always available and may introduce additional bias and ambiguity. Results: We propose a novel, network topology based interaction confidence assessment method called CAPPIC (cluster-based assessment of protein-protein interaction confidence). It exploits the network’s inherent modular architecture for assessing the confidence of individual interactions. Our method determines algorithmic parameters intrinsically and does not require any parameter input or reference sets for confidence scoring. Conclusions: On the basis of five yeast and two human physical interactome maps inferred using different techniques, we show that CAPPIC reliably assesses interaction confidence and its performance compares well to other approaches that are also based on network topology. The confidence score correlates with the agreement in localization and biological process annotations of interacting proteins. Moreover, it corroborates experimental evidence of physical interactions. Our method is not limited to physical interactome maps as we exemplify with a large yeast genetic interaction network. An implementation of CAPPIC is available at http://intscore.molgen.mpg.d