Interactome modeling

Coevolution of robustness, epistasis, and recombination favors asexual reproduction

The prevalence of sexual reproduction remains one of the most perplexing phenomena in evolutionary biology. The deterministic mutation hypothesis postulates that sexual reproduction will be advantageous under synergistic epistasis, a condition in which mutations cause a greater reduction in fitness when combined than would be expected from their individual effects. The inverse condition, antagonistic epistasis, correspondingly is predicted to favor asexual reproduction. To assess this hypothesis, we introduce a finite population evolutionary process that combines a recombination modifier formalism with a gene-regulatory network model. We demonstrate that when reproductive mode and epistasis are allowed to coevolve, asexual reproduction outcompetes sexual reproduction. In addition, no correlation is found between the level of synergistic epistasis and the fixation time of the asexual mode. However, a significant correlation is found between the level of antagonistic epistasis and asexual mode fixation time. This asymmetry can be explained by the greater reduction in fitness imposed by sexual reproduction as compared with asexual reproduction. Our findings present evidence and suggest plausible explanations that challenge both the deterministic mutation hypothesis and recent arguments asserting the importance of emergent synergistic epistasis in the maintenance of sexual reproduction.

The human disease network

A network of disorders and disease genes linked by known disorder-gene associations offers a platform to explore in a single graph-theoretic framework all known phenotype and disease gene associations, indicating the common genetic origin of many diseases. Genes associated with similar disorders show both higher likelihood of physical interactions between their products and higher expression profiling similarity for their transcripts, supporting the existence of distinct disease-specific functional modules. We find that essential human genes are likely to encode hub proteins and are expressed widely in most tissues. This suggests that disease genes also would play a central role in the human interactome. In contrast, we find that the vast majority of disease genes are nonessential and show no tendency to encode hub proteins, and their expression pattern indicates that they are localized in the functional periphery of the network. A selection-based model explains the observed difference between essential and disease genes and also suggests that diseases caused by somatic mutations should not be peripheral, a prediction we confirm for cancer genes.

Advanced glycation end-product-induced mitogenesis is dependent on Janus kinase 2-induced heat shock protein 70 in normal rat kidney interstitial fibroblast cells

Kidney interstitial fibroblast proliferation is important in the pathogenesis of diabetic renal fibrosis. In this regard, advanced glycation end-product (AGE)-induced proliferation in normal rat kidney interstitial fibroblast (NRK-49F) cells is dependent on the Janus kinase 2 (JAK2) signal transducers and activators of transcription (STAT) pathway. Heat shock protein (Hsp) is a molecular target of JAK/STAT. Thus, the role of Hsp70 in AGE-induced mitogenesis in NRK-49F cells was studied. The AGE dose (100-200 microg/mL) and time (16-72 h) dependently increased Hsp70 protein expression. AGE-induced Hsp70 was attenuated by AG-490 (a JAK2 inhibitor) and N-acetylcysteine. AGE also increased tyrosine phosphorylation of Hsp70, cyclin E, and cyclin D1 (to a lesser extent) while increasing Hsp70 protein interactions with STAT1, STAT3, STAT5b, cyclin D1, and cyclin E. AGE-induced tyrosine phosphorylation of Hsp70 and cyclin E (but not cyclin D1) was attenuated by AG-490. AGE-induced mitogenesis, cyclin D1, and cyclin E were attenuated by Hsp70 antisense oligodeoxynucleotide and 2-aminopurine (an Hsp70 inhibitor). AGE-induced Hsp70 and mitogenesis were also attenuated by N-acetylcysteine. It was concluded that AGE-induced Hsp70 protein expression and tyrosine phosphorylation are dependent on JAK2 in NRK-49F cells. AGE increased protein-protein interactions among Hsp70, STAT1, STAT3, STAT5b, cyclin D1, and cyclin E. Moreover, AGE-induced mitogenesis is dependent on Hsp70 and oxidative stress.

Promiscuous Binding of Ligands by β-Lactoglobulin Involves Hydrophobic Interactions and Plasticity

Bovine beta-lactoglobulin (betaLG) binds a variety of hydrophobic ligands, though precisely how is not clear. To understand the structural basis of this promiscuous binding, we studied the interaction of betaLG with palmitic acid (PA) using heteronuclear NMR spectroscopy. The titration was monitored using tryptophan fluorescence and a HSQC spectrum confirmed a 1:1 stoichiometry for the PA-betaLG complex. Upon the binding of PA, signal disappearances and large changes in chemical shifts were observed for the residues located at the entrance and bottom of the cavity, respectively. This observation indicates that the lower region makes a rigid connection with PA whereas the entrance is more flexible. The result is in contrast to the binding of PA to intestinal fatty acid-binding protein, another member of the calycin superfamily, in which structural consolidation occurs upon ligand binding. On the other hand, the ability of betaLG to accommodate various hydrophobic ligands resembles that of GroEL, in which a large hydrophobic cavity and flexible binding site confer the ability to bind various hydrophobic substrates. Considering these observations, it is suggested that, in addition to the presence of the hydrophobic cavity, the plasticity of the entrance region makes possible the binding of hydrophobic ligands of various shapes. Thus, in contrast to the specific binding seen for many enzymes, betaLG provides an example of binding with low specificity but high affinity, which may play an important role in protein-ligand and protein-protein networks.

Integrative analysis of a cancer somatic mutome

BACKGROUND

The consecutive acquisition of genetic alterations characterizes neoplastic processes. As a consequence of these alterations, molecular interactions are reprogrammed in the context of highly connected and regulated cellular networks. The recent identification of the collection of somatically mutated genes in breast tumors (breast cancer somatic "mutome") allows the comprehensive study of its function and organization in complex networks.

RESULTS

We analyzed functional genomic data (loss of heterozygosity, copy number variation and gene expression in breast tumors) and protein binary interactions from public repositories to identify potential novel components of neoplastic processes, the functional relationships between them, and to examine their coordinated function in breast cancer pathogenesis. This analysis identified candidate tumor suppressors and oncogenes, and new genes whose expression level predicts survival rate in breast cancer patients. Mutome network modeling using different types of pathological and healthy functional relationships unveils functional modules significantly enriched in genes or proteins (genes/proteins) with related biological process Gene Ontology terms and containing known breast cancer-related genes/proteins.

CONCLUSION

This study presents a comprehensive analysis of the breast somatic mutome, highlighting those genes with a higher probability of playing a determinant role in tumorigenesis and better defining molecular interactions related to the neoplastic process.

From protein-protein complexes to interactomics

Protein-protein interactions (or PPIs) are key elements for the normal functioning of a living cell. A large description of the protein interactomics field is given in this review where different aspects will be discussed. We first give an introduction of the different large scale experimental approaches from yeast two-hybrid to mass spectrometry used to discover PPIs and build protein interaction maps. Single PPI validation techniques such as co-immunoprecipitation or fluorescence methods are then presented as they are more and more integrated in global PPI discovery strategy. Data from different experimental sets are compared and an assessment of the different large scale technologies is presented. Bioinformatics tools can also predict with a good accuracy PPIs in silico, PPIs databases are now numerous and topological analysis has led to interesting insights into the nature of network connection. Finally, PPI, as an association of two proteins, has been structurally characterized for many protein complexes and is largely discussed throughout existing examples. The results obtained so far already provide the biologist with a large set of structured data from which knowledge on pathways and associated protein function can be extracted.

Computational prediction of cancer-gene function

Most cancer genes remain functionally uncharacterized in the physiological context of disease development. High-throughput molecular profiling and interaction studies are increasingly being used to identify clusters of functionally linked gene products related to neoplastic cell processes. However, in vivo determination of cancer-gene function is laborious and inefficient, so accurately predicting cancer-gene function is a significant challenge for oncologists and computational biologists alike. How can modern computational and statistical methods be used to reliably deduce the function(s) of poorly characterized cancer genes from the newly available genomic and proteomic datasets? We explore plausible solutions to this important challenge.

Predicting protein-protein interactions from protein domains using a set cover approach

One goal of contemporary proteome research is the elucidation of cellular protein interactions. Based on currently available protein-protein interaction and domain data, we introduce a novel method, Maximum Specificity Set Cover (MSSC), for the prediction of protein-protein interactions. In our approach, we map the relationship between interactions of proteins and their corresponding domain architectures to a generalized weighted set cover problem. The application of a greedy algorithm provides sets of domain interactions which explain the presence of protein interactions to the largest degree of specificity. Utilizing domain and protein interaction data of S. cerevisiae, MSSC enables prediction of previously unknown protein interactions, links that are well supported by a high tendency of coexpression and functional homogeneity of the corresponding proteins. Focusing on concrete examples, we show that MSSC reliably predicts protein interactions in well-studied molecular systems, such as the 26S proteasome and RNA polymerase II of S. cerevisiae. We also show that the quality of the predictions is comparable to the Maximum Likelihood Estimation while MSSC is faster. This new algorithm and all data sets used are accessible through a Web portal at http://ppi.cse.nd.edu.

A systems biology analysis of the Drosophila phagosome

Phagocytes have a critical function in remodelling tissues during embryogenesis and thereafter are central effectors of immune defence. During phagocytosis, particles are internalized into 'phagosomes', organelles from which immune processes such as microbial destruction and antigen presentation are initiated. Certain pathogens have evolved mechanisms to evade the immune system and persist undetected within phagocytes, and it is therefore evident that a detailed knowledge of this process is essential to an understanding of many aspects of innate and adaptive immunity. However, despite the crucial role of phagosomes in immunity, their components and organization are not fully defined. Here we present a systems biology analysis of phagosomes isolated from cells derived from the genetically tractable model organism Drosophila melanogaster and address the complex dynamic interactions between proteins within this organelle and their involvement in particle engulfment. Proteomic analysis identified 617 proteins potentially associated with Drosophila phagosomes; these were organized by protein-protein interactions to generate the 'phagosome interactome', a detailed protein-protein interaction network of this subcellular compartment. These networks predicted both the architecture of the phagosome and putative biomodules. The contribution of each protein and complex to bacterial internalization was tested by RNA-mediated interference and identified known components of the phagocytic machinery. In addition, the prediction and validation of regulators of phagocytosis such as the 'exocyst', a macromolecular complex required for exocytosis but not previously implicated in phagocytosis, validates this strategy. In generating this 'systems-based model', we show the power of applying this approach to the study of complex cellular processes and organelles and expect that this detailed model of the phagosome will provide a new framework for studying host-pathogen interactions and innate immunity.

CellCircuits: a database of protein network models

CellCircuits () is an open-access database of molecular network models, designed to bridge the gap between databases of individual pairwise molecular interactions and databases of validated pathways. CellCircuits captures the output from an increasing number of approaches that screen molecular interaction networks to identify functional subnetworks, based on their correspondence with expression or phenotypic data, their internal structure or their conservation across species. This initial release catalogs 2019 computationally derived models drawn from 11 journal articles and spanning five organisms (yeast, worm, fly, Plasmodium falciparum and human). Models are available either as images or in machine-readable formats and can be queried by the names of proteins they contain or by their enriched biological functions. We envision CellCircuits as a clearinghouse in which theorists may distribute or revise models in need of validation and experimentalists may search for models or specific hypotheses relevant to their interests. We demonstrate how such a repository of network models is a novel systems biology resource by performing several meta-analyses not currently possible with existing databases.

The retinoblastoma tumor-suppressor gene, the exception that proves the rule

The retinoblastoma tumor-suppressor gene (Rb1) is centrally important in cancer research. Mutational inactivation of Rb1 causes the pediatric cancer retinoblastoma, while deregulation of the pathway in which it functions is common in most types of human cancer. The Rb1-encoded protein (pRb) is well known as a general cell cycle regulator, and this activity is critical for pRb-mediated tumor suppression. The main focus of this review, however, is on more recent evidence demonstrating the existence of additional, cell type-specific pRb functions in cellular differentiation and survival. These additional functions are relevant to carcinogenesis suggesting that the net effect of Rb1 loss on the behavior of resulting tumors is highly dependent on biological context. The molecular mechanisms underlying pRb functions are based on the cellular proteins it interacts with and the functional consequences of those interactions. Better insight into pRb-mediated tumor suppression and clinical exploitation of pRb as a therapeutic target will require a global view of the complex, interdependent network of pocket protein complexes that function simultaneously within given tissues.

Reconstructing protein complexes: From proteomics to systems biology

Modern high throughput technologies in biological science often create lists of interesting molecules. The challenge is to reconstruct a descriptive model from these lists that reflects the underlying biological processes as accurately as possible. Once we have such a model or network, what can we learn from it? Specifically, given that we are interested in some biological process associated with the model, what new properties can we predict and subsequently test? Here, we describe, at an introductory level, a range of bioinformatics techniques that can be systematically applied to proteomic datasets. When combined, these methods give us a global overview of the network and the properties of the proteins and their interactions. These properties can then be used to predict functional pathways within the network and to examine substructure. To illustrate the application of these methods, we draw upon our own work concerning a complex of 186 proteins found in neuronal synapses in mammals. The techniques discussed are generally applicable and could be used to examine lists of proteins involved with the biological response to electric or magnetic fields.

The comparative toxicogenomics database: a cross-species resource for building chemical-gene interaction networks

Chemicals in the environment play a critical role in the etiology of many human diseases. Despite their prevalence, the molecular mechanisms of action and the effects of chemicals on susceptibility to disease are not well understood. To promote understanding of these mechanisms, the Comparative Toxicogenomics Database (CTD; http://ctd.mdibl.org/) presents scientifically reviewed and curated information on chemicals, relevant genes and proteins, and their interactions in vertebrates and invertebrates. CTD integrates sequence, reference, species, microarray, and general toxicology information to provide a unique centralized resource for toxicogenomic research. The database also provides visualization capabilities that enable cross-species comparisons of gene and protein sequences. These comparisons will facilitate understanding of structure-function correlations and the genetic basis of susceptibility. Manual curation and integration of cross-species chemical-gene and chemical-protein interactions from the literature are now underway. These data will provide information for building complex interaction networks. New CTD features include (1) cross-species gene, rather than sequence, query and visualization capabilities; (2) integrated cross-links to microarray data from chemicals, genes, and sequences in CTD; (3) a reference set related to chemical-gene and protein interactions identified by an information retrieval system; and (4) a "Chemicals in the News" initiative that provides links from CTD chemicals to environmental health articles from the popular press. Here we describe these new features and our novel cross-species curation of chemical-gene and chemical-protein interactions.

Growing biological networks: Beyond the gene-duplication model

In this paper we propose a generalized growth model for biological interaction networks, including a set of biological features which have been inspired by a long tradition of simulations of immune system and chemical reaction networks. In our models we include characteristics such as the heterogeneity of biological nodes, the existence of natural hubs, the nodes binding by mutual affinity and the significance of type-based networks as compared with instance-based networks. Under these assumptions, we analyse the importance of the nodes concentration with respect to the selection of incoming nodes. We show that networks with fat-tailed degree distribution and highly clustered structure naturally emerge in systems possessing certain properties: new instances need to be produced through an endogenous source and this source needs to provide a positive feedback favouring nodes with high concentration to receive new connections. Furthermore, we show that understanding the concentration dynamics of each node and the consequent correlation between connectivity and concentration is a more adequate way to capture the global properties of type-based biological networks.

Charting protein complexes, signaling pathways, and networks in the immune system

Systematic deciphering of protein-protein interactions has the potential to generate comprehensive and instructive signaling networks and to fuel new therapeutic and diagnostic strategies. Here, we describe how recent advances in high-throughput proteomic technologies, involving biochemical purification methods and mass spectrometry analysis, can be applied systematically to the characterization of protein complexes and the computation of molecular networks. The networks obtained form the basis for further functional analyses, such as knockdown by RNA interference, ultimately leading to the identification of nodes that represent candidate targets for pharmacological exploitation. No individual experimental approach can accurately elucidate all critical modulatory components and biological aspects of a signaling network. Such functionally annotated protein-protein interaction networks, however, represent an ideal platform for the integration of additional datasets. By providing links between molecules, they also provide links to all previous observations associated with these molecules, be they of genetic, pharmacological, or other origin. As exemplified here by the analysis of the tumor necrosis factor (TNF)-alpha/nuclear factor-kappaB (NF-kappaB) signaling pathway, the approach is applicable to any mammalian cellular signaling pathway in the immune system.

Autism and environmental genomics

Autism spectrum disorders (ASD) are defined by behavior and diagnosed by clinical history and observation but have no biomarkers and are presumably, etiologically and biologically heterogeneous. Given brain abnormalities and high monozygotic concordance, ASDs have been framed as neurobiologically based and highly genetic, which has shaped the research agenda and in particular criteria for choosing candidate ASD genes. Genetic studies to date have not uncovered genes of strong effect, but a move toward "genetic complexity" at the neurobiological level may not suffice, as evidence of systemic abnormalities (e.g. gastrointestinal and immune), increasing rates and less than 100% monozygotic concordance support a more inclusive reframing of autism as a multisystem disorder with genetic influence and environmental contributors. We review this evidence and also use a bioinformatic approach to explore the possibility that "environmentally responsive genes" not specifically associated with the nervous system, but potentially associated with systemic changes in autism, have not hitherto received sufficient attention in autism genetics investigations. We overlapped genes from NIEHS Environmental Genome Project, the Comparative Toxicogenomics Database, and the SeattleSNPs database of genes relevant to the human immune and inflammatory response with linkage regions identified in published autism genome scans. We identified 135 genes in overlap regions, of which 56 had never previously been studied in relation to autism and 47 had functional SNPs (in coding regions). Both our review and the bioinformatics exercise support the expansion of criteria for evaluating the relevance of genes to autism risk to include genes related to systemic impact and environmental responsiveness. This review also suggests the utility of environmental genomic resources in highlighting the potential relevance of particular genes within linkage regions. Environmental responsiveness and systems impacts consistent with system-wide findings in autism are thus supported as important considerations in identifying the numerous and complex modes of gene-environment interaction in autism.

Checking and fixing the cellular nanomachinery: towards medical nanoscopy

Most diseases, regardless of their diverse etiologies, manifest themselves as defects of cellular proteins. Cellular proteins have been recently shown to form specific complexes exerting their functions as if they were nanoscopic machines. Such nanoscopic protein machines cooperate in functional modules, yielding extended, highly compartmentalized networks. The classical resolution limits of fluorescence microscopy have also been recently overcome, opening the nanometer domain to live-cell imaging. Together, progress in functional proteomics and live-cell imaging provide novel possibilities for directly analyzing and modifying nanoscopic protein machines in living cells and tissues.

Stable evolutionary signal in a yeast protein interaction network

Background

The recently emerged protein interaction network paradigm can provide novel and important insights into the innerworkings of a cell. Yet, the heavy burden of both false positive and false negative protein-protein interaction data casts doubt on the broader usefulness of these interaction sets. Approaches focusing on one-protein-at-a-time have been powerfully employed to demonstrate the high degree of conservation of proteins participating in numerous interactions; here, we expand his 'node' focused paradigm to investigate the relative persistence of 'link' based evolutionary signals in a protein interaction network of S. cerevisiae and point out the value of this relatively untapped source of information.

Results

The trend for highly connected proteins to be preferably conserved in evolution is stable, even in the context of tremendous noise in the underlying protein interactions as well as in the assignment of orthology among five higher eukaryotes. We find that local clustering around interactions correlates with preferred evolutionary conservation of the participating proteins; furthermore the correlation between high local clustering and evolutionary conservation is accompanied by a stable elevated degree of coexpression of the interacting proteins. We use this conserved interaction data, combined with P. falciparum /Yeast orthologs, as proof-of-principle that high-order network topology can be used comparatively to deduce local network structure in non-model organisms.

Conclusion

High local clustering is a criterion for the reliability of an interaction and coincides with preferred evolutionary conservation and significant coexpression. These strong and stable correlations indicate that evolutionary units go beyond a single protein to include the interactions among them. In particular, the stability of these signals in the face of extreme noise suggests that empirical protein interaction data can be integrated with orthologous clustering around these protein interactions to reliably infer local network structures in non-model organisms.

Protein linguistics — a grammar for modular protein assembly?

The correspondence between biology and linguistics at the level of sequence and lexical inventories, and of structure and syntax, has fuelled attempts to describe genome structure by the rules of formal linguistics. But how can we define protein linguistic rules? And how could compositional semantics improve our understanding of protein organization and functional plasticity?

Shining light on signaling and metabolic networks by genetically encoded biosensors

Fluorescent labels have revolutionized cell biology. Signaling intermediates and metabolites can be measured in real time with subcellular spatial resolution. Most of these sensors are based on fluorescent proteins, and many report fluorescence resonance energy transfer. Because the biosensors are genetically encoded, a toolbox for addressing cell biological questions at the systems level is now available. Fluorescent biosensors are able to determine the localization of proteins and their dynamics, to reveal the cellular and subcellular localization of the respective interactions and activities, and to provide complementary data on the steady state levels of ions, metabolites, and signaling intermediates with high temporal and spatial resolution. They represent the basis for cell-based high-throughput assays that are necessary for a systems perspective on plant cell function.

Agriculture in the developing world: Connecting innovations in plant research to downstream applications

Enhancing agricultural productivity in those areas of the world bypassed by the Green Revolution will require new approaches that provide incentives and funding mechanisms that promote the translation of new innovations in plant science into concrete benefits for poor farmers. Through better dialogue, plant breeders and laboratory scientists from both the public and private-sectors need to find solutions for the key constraints to crop production, many of which center around abiotic and biotic stresses. The revolution in plant genomics has opened up new perspectives and opportunities for plant breeders who can now apply molecular markers to assess and enhance diversity in their germplasm collections, to introgress valuable traits from new sources, and to identify genes that control key traits. Functional genomics is also providing another powerful route to the identification of such genes. The ability to introgress beneficial genes under the control of specific promoters through transgenic approaches is yet one more stepping stone in the path to targeted approaches to crop improvement, and the new sciences have identified a vast array of genes that have exciting potential for crop improvement. For a few crops with viable markets, such as maize and cotton, some of the traits developed by the private sector are already showing benefits for farmers of the developing world, but the public sector will need to develop new skills and overcome a number of hurdles to carry out similar efforts for other crops and traits useful to very poor farmers.

Interactome: gateway into systems biology

Protein-protein interactions are fundamental to all biological processes, and a comprehensive determination of all protein-protein interactions that can take place in an organism provides a framework for understanding biology as an integrated system. The availability of genome-scale sets of cloned open reading frames has facilitated systematic efforts at creating proteome-scale data sets of protein-protein interactions, which are represented as complex networks or 'interactome' maps. Protein-protein interaction mapping projects that follow stringent criteria, coupled with experimental validation in orthogonal systems, provide high-confidence data sets immanently useful for interrogating developmental and disease mechanisms at a system level as well as elucidating individual protein function and interactome network topology. Although far from complete, currently available maps provide insight into how biochemical properties of proteins and protein complexes are integrated into biological systems. Such maps are also a useful resource to predict the function(s) of thousands of genes.

MEKK4 is an effector of the embryonic TRAF4 for JNK activation

TRAF4 has previously been shown to activate JNK through an unknown mechanism. Here, we show that endogenous TRAF4 and MEKK4 associate in both human K562 cells and mouse E10.5 embryos. TRAF4 interacts with the kinase domain of MEKK4. However, this association does not require MEKK4 kinase activity. The interaction of MEKK4 and TRAF4 are further demonstrated by the colocalization of TRAF4 and MEKK4 in cells. Importantly, although TRAF4 has little or no ability to activate JNK independently, coexpression of TRAF4 and MEKK4 results in synergistic activation of JNK that is inhibited by a kinase-inactive mutant of MEKK4, MEKK4K1361R. MEKK4 binds the TRAF domain of TRAF4 and MEKK4/TRAF4 activation of JNK is inhibited by expression of the TRAF domain. Furthermore, TRAF4 stimulates MEKK4 kinase activity by promoting MEKK4 oligomerization and JNK activation can be stimulated by chemical induction of MEKK4 dimerization. The findings identify MEKK4 as the MAPK kinase kinase for TRAF4 regulation of the JNK pathway.

Some protein interaction data do not exhibit power law statistics

It has been claimed that protein-protein interaction (PPI) networks are scale-free, and that identifying high-degree "hub" proteins reveals important features of PPI networks. In this paper, we evaluate the claims that PPI node degree sequences follow a power law, a necessary condition for networks to be scale-free. We provide two PPI network examples which clearly do not have power laws when analyzed correctly, and thus at least these PPI networks are not scale-free. We also show that these PPI networks do appear to have power laws according to methods that have become standard in the existing literature. We explain the source of this error using numerically generated data from analytic formulas, where there are no sampling or noise ambiguities.