Meeting report from the fourth meeting of the Computational Modeling in Biology Network (COMBINE)

This report summarizes the topics and activities of the fourth edition of the annual COMBINE meeting, held in Paris during September 16-20 2013,

ENFIN--A European network for integrative systems biology.

Integration of biological data of various types and the development of adapted bioinformatics tools represent critical objectives to enable research at the systems level. The European Network of Excellence ENFIN is engaged in developing an adapted infrastructure to connect databases, and platforms to enable both the generation of new bioinformatics tools and the experimental validation of computational predictions. With the aim of bridging the gap existing between standard wet laboratories and bioinformatics, the ENFIN Network runs integrative research projects to bring the latest computational techniques to bear directly on questions dedicated to systems biology in the wet laboratory environment. The Network maintains internally close collaboration between experimental and computational research, enabling a permanent cycling of experimental validation and improvement of computational prediction methods. The computational work includes the development of a database infrastructure (EnCORE), bioinformatics analysis methods and a novel platform for protein function analysis FuncNet.

WormBase: network access to the genome and biology of Caenorhabditis elegans

WormBase (http://www.wormbase.org) is a web-based resource for the Caenorhabditis elegans genome and its biology. It builds upon the existing ACeDB database of the C.elegans genome by providing data curation services, a significantly expanded range of subject areas and a user-friendly front end.

Cluster analysis of the p53 genetic regulatory network: topology and biology

We describe a network module detection approach which combines a rapid and robust clustering algorithm with an objective measure of the coherence of the modules identified. The approach is applied to the network of genetic regulatory interactions surrounding the tumor suppressor gene p53. This algorithm identifies ten clusters in the p53 network, which are visually coherent and biologically plausible.

AtPIN: Arabidopsis thaliana Protein Interaction Network

Background: Protein-protein interactions (PPIs) constitute one of the most crucial conditions to sustain life in living organisms. To study PPI in Arabidopsis thaliana we have developed AtPIN, a database and web interface for searching and building interaction networks based on publicly available protein-protein interaction datasets. Description: All interactions were divided into experimentally demonstrated or predicted. The PPIs in the AtPIN database present a cellular compartment classification (C(3)) which divides the PPI into 4 classes according to its interaction evidence and subcellular localization. It has been shown in the literature that a pair of genuine interacting proteins are generally expected to have a common cellular role and proteins that have common interaction partners have a high chance of sharing a common function. In AtPIN, due to its integrative profile, the reliability index for a reported PPI can be postulated in terms of the proportion of interaction partners that two proteins have in common. For this, we implement the Functional Similarity Weight (FSW) calculation for all first level interactions present in AtPIN database. In order to identify target proteins of cytosolic glutamyl-tRNA synthetase (Cyt-gluRS) (AT5G26710) we combined two approaches, AtPIN search and yeast two-hybrid screening. Interestingly, the proteins glutamine synthetase (AT5G35630), a disease resistance protein (AT3G50950) and a zinc finger protein (AT5G24930), which has been predicted as target proteins for Cyt-gluRS by AtPIN, were also detected in the experimental screening. Conclusions: AtPIN is a friendly and easy-to-use tool that aggregates information on Arabidopsis thaliana PPIs, ontology, and sub-cellular localization, and might be a useful and reliable strategy to map protein-protein interactions in Arabidopsis. AtPIN can be accessed at http://bioinfo.esalq.usp.br/atpin.

Finding Quasi-Optimal Network Topologies for Information Transmission in Active Networks

This work clarifies the relation between network circuit (topology) and behaviour (information transmission and synchronization) in active networks, e.g. neural networks. As an application, we show how one can find network topologies that are able to transmit a large amount of information, possess a large number of communication channels, and are robust under large variations of the network coupling configuration. This theoretical approach is general and does not depend on the particular dynamic of the elements forming the network, since the network topology can be determined by finding a Laplacian matrix (the matrix that describes the connections and the coupling strengths among the elements) whose eigenvalues satisfy some special conditions. To illustrate our ideas and theoretical approaches, we use neural networks of electrically connected chaotic Hindmarsh-Rose neurons.

Automatic Network Fingerprinting through Single-Node Motifs

Complex networks have been characterised by their specific connectivity patterns (network motifs), but their building blocks can also be identified and described by node-motifs-a combination of local network features. One technique to identify single node-motifs has been presented by Costa et al. (L. D. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser, Europhys. Lett., 87, 1, 2009). Here, we first suggest improvements to the method including how its parameters can be determined automatically. Such automatic routines make high-throughput studies of many networks feasible. Second, the new routines are validated in different network-series. Third, we provide an example of how the method can be used to analyse network time-series. In conclusion, we provide a robust method for systematically discovering and classifying characteristic nodes of a network. In contrast to classical motif analysis, our approach can identify individual components (here: nodes) that are specific to a network. Such special nodes, as hubs before, might be found to play critical roles in real-world networks.

Haplotype distribution of five nuclear genes based on network genealogies and Bayesian inference indicates that Trypanosoma cruzi hybrid strains are polyphyletic

Chagas disease is still a major public health problem in Latin America. Its causative agent, Trypanosoma cruzi, can be typed into three major groups, T. cruzi I, T. cruzi II and hybrids. These groups each have specific genetic characteristics and epidemiological distributions. Several highly virulent strains are found in the hybrid group; their origin is still a matter of debate. The null hypothesis is that the hybrids are of polyphyletic origin, evolving independently from various hybridization events. The alternative hypothesis is that all extant hybrid strains originated from a single hybridization event. We sequenced both alleles of genes encoding EF-1 alpha, actin and SSU rDNA of 26 T. cruzi strains and DHFR-TS and TR of 12 strains. This information was used for network genealogy analysis and Bayesian phylogenies. We found T. cruzi I and T. cruzi II to be monophyletic and that all hybrids had different combinations of T. cruzi I and T. cruzi II haplotypes plus hybrid-specific haplotypes. Bootstrap values (networks) and posterior probabilities (Bayesian phylogenies) of clades supporting the monophyly of hybrids were far below the 95% confidence interval, indicating that the hybrid group is polyphyletic. We hypothesize that T. cruzi I and T. cruzi II are two different species and that the hybrids are extant representatives of independent events of genome hybridization, which sporadically have sufficient fitness to impact on the epidemiology of Chagas disease.

Evidence for a network transcriptional control of promiscuous gene expression in medullary thymic epithelial cells

The expression of peripheral tissue antigens (PTAs) in the thymus by medullary thymic epithelial cells (mTECs) is essential for the central self-tolerance in the generation of the T cell repertoire. Due to heterogeneity of autoantigen representation, this phenomenon has been termed promiscuous gene expression (PGE), in which the autoimmune regulator (Aire) gene plays a key role as a transcription factor in part of these genes. Here we used a microarray strategy to access PGE in cultured murine CD80(+) 3.10 mTEC line. Hierarchical clustering of the data allowed observation that PTA genes were differentially expressed being possible to found their respective induced or repressed mRNAs. To further investigate the control of PGE, we tested the hypothesis that genes involved in this phenomenon might also be modulated by transcriptional network. We then reconstructed such network based on the microarray expression data, featuring the guanylate cyclase 2d (Gucy2d) gene as a main node. In such condition, we established 167 positive and negative interactions with downstream PTA genes. Silencing Aire by RNA interference, Gucy2d while down regulated established a larger number (355) of interactions with PTA genes. T- and G-boxes corresponding to AIRE protein binding sites located upstream to ATG codon of Gucy2d supports this effect. These findings provide evidence that Aire plays a role in association with Gucy2d, which is connected to Several PTA genes and establishes a cascade-like transcriptional control of promiscuous gene expression in mTEC cells. (C) 2009 Elsevier Ltd. All rights reserved.

The GRIP domain is a specific targeting sequence for a population of trans-Golgi network derived tubulo-vesicular carriers

Vesicular carriers for intracellular transport associate with unique sets of accessory molecules that dictate budding and docking on specific membrane domains. Although many of these accessory molecules are peripheral membrane proteins, in most cases the targeting sequences responsible for their membrane recruitment have yet to be identified. We have previously defined a novel Golgi targeting domain (GRIP) shared by a family of coiled-coil peripheral membrane Golgi proteins implicated in membrane trafficking. We show here that the docking site for the GRIP motif of p230 is a specific domain of Golgi. membranes. By immunoelectron microscopy of HeLa cells stably expressing a green fluorescent protein (GFP)-p230(GRIP) fusion protein, we show binding specifically to a subset of membranes of the trans-Golgi network (TGN). Real-time imaging of live HeLa cells revealed that the GFP-p230(GRIP) was associated with highly dynamic tubular extensions of the TGN, which have the appearance and behaviour of transport carriers. To further define the nature of the GRIP membrane binding site, in vitro budding assays were performed using purified rat liver Golgi membranes and cytosol from GFP-p230(GRIP) transfected cells. Analysis of Golgi-derived vesicles by sucrose gradient fractionation demonstrated that GFP-p230(GRIP) binds to a specific population of vesicles distinct from those labelled for beta -COP or gamma -adaptin. The GFP-p230(GRIP) fusion protein is recruited to the same vesicle population as full-length p230, demonstrating that the GRIP domain is solely proficient as a targeting signal for membrane binding of the native molecule. Therefore, p230 GRIP is a targeting signal for recruitment to a highly selective membrane attachment site on a specific population of trans-Golgi network tubulovesicular carriers.

GLUT4 recycles via a trans-Golgi network (TGN) subdomain enriched in Syntaxins 6 and 16 but not TGN38: Involvement of an acidic targeting motif

Insulin stimulates glucose transport in fat and muscle cells by triggering exocytosis of the glucose transporter GLUT4. To define the intracellular trafficking of GLUT4, we have studied the internalization of an epitope-tagged version of GLUT4 from the cell surface. GLUT4 rapidly traversed the endosomal system en route to a perinuclear location. This perinuclear GLUT4 compartment did not colocalize with endosomal markers (endosomal antigen I protein, transferrin) or TGN38, but showed significant overlap with the TGN target (t)-soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNAREs) Syntaxins 6 and 16. These results were confirmed by vesicle immunoisolation. Consistent with a role for Syntaxins 6 and 16 in GLUT4 trafficking we found that their expression was up-regulated significantly during adipocyte differentiation and insulin stimulated their movement to the cell surface. GLUT4 trafficking between endosomes and trans-Golgi network was regulated via an acidic targeting motif in the carboxy terminus of GLUT4, because a mutant lacking this motif was retained in endosomes. We conclude that GLUT4 is rapidly transported from the cell surface to a subdomain of the trans-Golgi network that is enriched in the t-SNAREs Syntaxins 6 and 16 and that an acidic targeting motif in the C-terminal tail of GLUT4 plays an important role in this process.

GRIP domain-mediated targeting of two new coiled-coil proteins, GCC88 and GCC185, to subcompartments of the trans-Golgi network

The GRIP domain is a targeting sequence found in a family of coiled-coil peripheral Golgi proteins. Previously we demonstrated that the GRIP domain of p230/golgin245 is specifically recruited to tubulovesicular structures of the traps-Golgi network (TGN). Here we have characterized two novel Golgi proteins with functional GRIP domains, designated GCC88 and GCC185. GCC88 cDNA encodes a protein of 88 kDa, and GCC185 cDNA encodes a protein of 185 kDa. Both molecules are brefeldin A-sensitive peripheral membrane proteins and are predicted to have extensive coiled-coil regions with the GRIP domain at the C terminus. By immunofluorescence and immunoelectron microscopy GCC88 and GCC185, and the GRIP protein golgin97, are all localized to the TGN of Hela cells. Overexpression of full-length GCC88 leads to the formation of large electron dense structures that extend from the traps-Golgi. These de novo structures contain GCC88 and co-stain for the TGN markers syntaxin 6 and TGN38 but not for alpha2,6-sialyltransferase, beta-COP, or cis-Golgi GM130. The formation of these abnormal structures requires the N-terminal domain of GCC88. TGN38, which recycles between the TGN and plasma membrane, was transported into and out of the GCC88 decorated structures. These data introduce two new GRIP domain proteins and implicate a role for GCC88 in the organization of a specific TGN subcompartment involved with membrane transport.

GAIP participates in budding of membrane carriers at the trans-Golgi network

Galpha interacting protein (GAIP) is a regulator of G protein signaling protein that associates dynamically with vesicles and has been implicated in membrane trafficking, although its specific role is not yet known. Using an in vitro budding assay, we show that GAIP is recruited to a specific population of trans-Golgi network-derived vesicles and that these are distinct from coatomer or clathrin-coated vesicles. A truncation mutant (NT-GAIP) encoding only the N-terminal half of GAIP is recruited to trans -Golgi network membranes during the formation of vesicle carriers. Overexpression of NT-GAIP induces the formation of long, coated tubules, which are stabilized by microtubules. Results from the budding assay and from imaging in live cells show that these tubules remain attached to the Golgi stack rather than being released as carrier vesicles. NT-GAIP expression blocks membrane budding and results in the accumulation of tubular carrier intermediates. NT-GAIP-decorated tubules are competent to load vesicular stomatitis virus protein G-green fluorescent protein as post-Golgi, exocytic cargo and in cells expressing NT-GAIP there is reduced surface delivery of vesicular stomatitis virus protein G-green fluorescent protein. We conclude that GAIP functions as an essential part of the membrane budding machinery for a subset of post-Golgi exocytic carriers derived from the trans-Golgi network.

Hybrid systems biology: application to Escherichia coli

Dissertation presented to obtain a Master degree in Biotechnology