The nucleoporin Nup98 associates with the intranuclear filamentous protein network of TPR

The Nup98 gene codes for several alternatively spliced protein precursors. Two in vitro translated and autoproteolytically cleaved precursors yielded heterodimers of Nup98-6kDa peptide and Nup98-Nup96. TPR (translocated promoter region) is a protein that forms filamentous structures extending from nuclear pore complexes (NPCs) to intranuclear sites. We found that in vitro translated TPR bound to in vitro translated Nup98 and, via Nup98, to Nup96. Double-immunofluorescence microscopy with antibodies to TPR and Nup98 showed colocalization. In confocal sections the nucleolus itself was only weakly stained but there was intensive perinucleolar staining. Striking spike-like structures emanated from this perinucleolar ring and attenuated into thinner structures as they extended to the nuclear periphery. This characteristic staining pattern of the TPR network was considerably enhanced when a myc-tagged pyruvate kinase-6kDa fusion protein was overexpressed in HeLa cells. Double-immunoelectron microscopy of these cells using anti-myc and anti-TPR antibodies and secondary gold-coupled antibodies yielded row-like arrangements of gold particles. Taken together, the immunolocalization data support previous electron microscopical data, suggesting that TPR forms filaments that extend from the NPC to the nucleolus. We discuss the possible implications of the association of Nup98 with this intranuclear TPR network for an intranuclear phase of transport.

Gene Promoter Evolution Targets the Center of the Human Protein Interaction Network

Assessing the contribution of promoters and coding sequences to gene evolution is an important step toward discovering the major genetic determinants of human evolution. Many specific examples have revealed the evolutionary importance of cis-regulatory regions. However, the relative contribution of regulatory and coding regions to the evolutionary process and whether systemic factors differentially influence their evolution remains unclear. To address these questions, we carried out an analysis at the genome scale to identify signatures of positive selection in human proximal promoters. Next, we examined whether genes with positively selected promoters (Prom+ genes) show systemic differences with respect to a set of genes with positively selected protein-coding regions (Cod+ genes). We found that the number of genes in each set was not significantly different (8.1% and 8.5%, respectively). Furthermore, a functional analysis showed that, in both cases, positive selection affects almost all biological processes and only a few genes of each group are located in enriched categories, indicating that promoters and coding regions are not evolutionarily specialized with respect to gene function. On the other hand, we show that the topology of the human protein network has a different influence on the molecular evolution of proximal promoters and coding regions. Notably, Prom+ genes have an unexpectedly high centrality when compared with a reference distribution (P = 0.008, for Eigenvalue centrality). Moreover, the frequency of Prom+ genes increases from the periphery to the center of the protein network (P = 0.02, for the logistic regression coefficient). This means that gene centrality does not constrain the evolution of proximal promoters, unlike the case with coding regions, and further indicates that the evolution of proximal promoters is more efficient in the center of the protein network than in the periphery. These results show that proximal promoters have had a systemic contribution to human evolution by increasing the participation of central genes in the evolutionary process.

Topological aspects of the human protein interaction network

It is currently widely accepted that the understanding of complex cell functions depends on an integrated network theoretical approach and not on an isolated view of the different molecular agents. Aim of this thesis was the examination of topological properties that mirror known biological aspects by depicting the human protein network with methods from graph- and network theory. The presented network is a partial human interactome of 9222 proteins and 36324 interactions, consisting of single interactions reliably extracted from peer-reviewed scientific publications. In general, one can focus on intra- or intermodular characteristics, where a functional module is defined as "a discrete entity whose function is separable from those of other modules". It is found that the presented human network is also scale-free and hierarchically organised, as shown for yeast networks before. The interactome also exhibits proteins with high betweenness and low connectivity which are biologically analyzed and interpreted here as shuttling proteins between organelles (e.g. ER to Golgi, internal ER protein translocation, peroxisomal import, nuclear pores import/export) for the first time. As an optimisation for finding proteins that connect modules, a new method is developed here based on proteins located between highly clustered regions, rather than regarding highly connected regions. As a proof of principle, the Mediator complex is found in first place, the prime example for a connector complex. Focusing on intramodular aspects, the measurement of k-clique communities discriminates overlapping modules very well. Twenty of the largest identified modules are analysed in detail and annotated to known biological structures (e.g. proteasome, the NFκB-, TGF-β complex). Additionally, two large and highly interconnected modules for signal transducer and transcription factor proteins are revealed, separated by known shuttling proteins. These proteins yield also the highest number of redundant shortcuts (by calculating the skeleton), exhibit the highest numbers of interactions and might constitute highly interconnected but spatially separated rich-clubs either for signal transduction or for transcription factors. This design principle allows manifold regulatory events for signal transduction and enables a high diversity of transcription events in the nucleus by a limited set of proteins. Altogether, biological aspects are mirrored by pure topological features, leading to a new view and to new methods that assist the annotation of proteins to biological functions, structures and subcellular localisations. As the human protein network is one of the most complex networks at all, these results will be fruitful for other fields of network theory and will help understanding complex network functions in general.

Network-guided analysis of genes with altered somatic copy number and gene expression reveals pathways commonly perturbed in metastatic melanoma.

Cancer genomes frequently contain somatic copy number alterations (SCNA) that can significantly perturb the expression level of affected genes and thus disrupt pathways controlling normal growth. In melanoma, many studies have focussed on the copy number and gene expression levels of the BRAF, PTEN and MITF genes, but little has been done to identify new genes using these parameters at the genome-wide scale. Using karyotyping, SNP and CGH arrays, and RNA-seq, we have identified SCNA affecting gene expression ('SCNA-genes') in seven human metastatic melanoma cell lines. We showed that the combination of these techniques is useful to identify candidate genes potentially involved in tumorigenesis. Since few of these alterations were recurrent across our samples, we used a protein network-guided approach to determine whether any pathways were enriched in SCNA-genes in one or more samples. From this unbiased genome-wide analysis, we identified 28 significantly enriched pathway modules. Comparison with two large, independent melanoma SCNA datasets showed less than 10% overlap at the individual gene level, but network-guided analysis revealed 66% shared pathways, including all but three of the pathways identified in our data. Frequently altered pathways included WNT, cadherin signalling, angiogenesis and melanogenesis. Additionally, our results emphasize the potential of the EPHA3 and FRS2 gene products, involved in angiogenesis and migration, as possible therapeutic targets in melanoma. Our study demonstrates the utility of network-guided approaches, for both large and small datasets, to identify pathways recurrently perturbed in cancer.

Gene Ontology Function prediction in Mollicutes using Protein-Protein Association Networks

Abstract Background: Many complex systems can be represented and analysed as networks. The recent availability of large-scale datasets, has made it possible to elucidate some of the organisational principles and rules that govern their function, robustness and evolution. However, one of the main limitations in using protein-protein interactions for function prediction is the availability of interaction data, especially for Mollicutes. If we could harness predicted interactions, such as those from a Protein-Protein Association Networks (PPAN), combining several protein-protein network function-inference methods with semantic similarity calculations, the use of protein-protein interactions for functional inference in this species would become more potentially useful. Results: In this work we show that using PPAN data combined with other approximations, such as functional module detection, orthology exploitation methods and Gene Ontology (GO)-based information measures helps to predict protein function in Mycoplasma genitalium. Conclusions: To our knowledge, the proposed method is the first that combines functional module detection among species, exploiting an orthology procedure and using information theory-based GO semantic similarity in PPAN of the Mycoplasma species. The results of an evaluation show a higher recall than previously reported methods that focused on only one organism network.

Assembly rules for protein networks derived from phylogenetic-statistical analysis of whole genomes

Background: We report an analysis of a protein network of functionally linked proteins, identified from a phylogenetic statistical analysis of complete eukaryotic genomes. Phylogenetic methods identify pairs of proteins that co-evolve on a phylogenetic tree, and have been shown to have a high probability of correctly identifying known functional links. Results: The eukaryotic correlated evolution network we derive displays the familiar power law scaling of connectivity. We introduce the use of explicit phylogenetic methods to reconstruct the ancestral presence or absence of proteins at the interior nodes of a phylogeny of eukaryote species. We find that the connectivity distribution of proteins at the point they arise on the tree and join the network follows a power law, as does the connectivity distribution of proteins at the time they are lost from the network. Proteins resident in the network acquire connections over time, but we find no evidence that 'preferential attachment' - the phenomenon of newly acquired connections in the network being more likely to be made to proteins with large numbers of connections - influences the network structure. We derive a 'variable rate of attachment' model in which proteins vary in their propensity to form network interactions independently of how many connections they have or of the total number of connections in the network, and show how this model can produce apparent power-law scaling without preferential attachment. Conclusion: A few simple rules can explain the topological structure and evolutionary changes to protein-interaction networks: most change is concentrated in satellite proteins of low connectivity and small phenotypic effect, and proteins differ in their propensity to form attachments. Given these rules of assembly, power law scaled networks naturally emerge from simple principles of selection, yielding protein interaction networks that retain a high-degree of robustness on short time scales and evolvability on longer evolutionary time scales.

Resilience of protein-protein interaction networks as determined by their large-scale topological features

The relationship between the structure and function of biological networks constitutes a fundamental issue in systems biology. Particularly, the structure of protein-protein interaction networks is related to important biological functions. In this work, we investigated how such a resilience is determined by the large scale features of the respective networks. Four species are taken into account, namely yeast Saccharomyces cerevisiae, worm Caenorhabditis elegans, fly Drosophila melanogaster and Homo sapiens. We adopted two entropy-related measurements (degree entropy and dynamic entropy) in order to quantify the overall degree of robustness of these networks. We verified that while they exhibit similar structural variations under random node removal, they differ significantly when subjected to intentional attacks (hub removal). As a matter of fact, more complex species tended to exhibit more robust networks. More specifically, we quantified how six important measurements of the networks topology (namely clustering coefficient, average degree of neighbors, average shortest path length, diameter, assortativity coefficient, and slope of the power law degree distribution) correlated with the two entropy measurements. Our results revealed that the fraction of hubs and the average neighbor degree contribute significantly for the resilience of networks. In addition, the topological analysis of the removed hubs indicated that the presence of alternative paths between the proteins connected to hubs tend to reinforce resilience. The performed analysis helps to understand how resilience is underlain in networks and can be applied to the development of protein network models.

Direct Visualization of a Protein Nuclear Architecture

Whether the cell nucleus is organized by an underlying architecture analagous to the cytoskeleton has been a highly contentious issue since the original isolation of a nuclease and salt-resistant nuclear matrix. Despite electron microscopy studies that show that a nuclear architecture can be visualized after fractionation, the necessity to elute chromatin to visualize this structure has hindered general acceptance of a karyoskeleton. Using an analytical electron microscopy method capable of quantitative elemental analysis, electron spectroscopic imaging, we show that the majority of the fine structure within interchromatin regions of the cell nucleus in fixed whole cells is not nucleoprotein. Rather, this fine structure is compositionally similar to known protein-based cellular structures of the cytoplasm. This study is the first demonstration of a protein network in unfractionated and uninfected cells and provides a method for the ultrastructural characterization of the interaction of this protein architecture with chromatin and ribonucleoprotein elements of the cell nucleus.

Dynamic impacts of the inhibition of the molecular chaperone hsp90 on the T-cell proteome have implications for anti-cancer therapy.

The molecular chaperone Hsp90-dependent proteome represents a complex protein network of critical biological and medical relevance. Known to associate with proteins with a broad variety of functions termed clients, Hsp90 maintains key essential and oncogenic signalling pathways. Consequently, Hsp90 inhibitors are being tested as anti-cancer drugs. Using an integrated systematic approach to analyse the effects of Hsp90 inhibition in T-cells, we quantified differential changes in the Hsp90-dependent proteome, Hsp90 interactome, and a selection of the transcriptome. Kinetic behaviours in the Hsp90-dependent proteome were assessed using a novel pulse-chase strategy (Fierro-Monti et al., accompanying article), detecting effects on both protein stability and synthesis. Global and specific dynamic impacts, including proteostatic responses, are due to direct inhibition of Hsp90 as well as indirect effects. As a result, a decrease was detected in most proteins that changed their levels, including known Hsp90 clients. Most likely, consequences of the role of Hsp90 in gene expression determined a global reduction in net de novo protein synthesis. This decrease appeared to be greater in magnitude than a concomitantly observed global increase in protein decay rates. Several novel putative Hsp90 clients were validated, and interestingly, protein families with critical functions, particularly the Hsp90 family and cofactors themselves as well as protein kinases, displayed strongly increased decay rates due to Hsp90 inhibitor treatment. Remarkably, an upsurge in survival pathways, involving molecular chaperones and several oncoproteins, and decreased levels of some tumour suppressors, have implications for anti-cancer therapy with Hsp90 inhibitors. The diversity of global effects may represent a paradigm of mechanisms that are operating to shield cells from proteotoxic stress, by promoting pro-survival and anti-proliferative functions. Data are available via ProteomeXchange with identifier PXD000537.

Rheological and thermal characteristics of wheat gluten biopolymers plasticized with glycerol

The objective this work was to obtain bioplastics from mixtures of wheat gluten and glycerol by two different processes and evaluate their respective rheological properties. The mixtures and their respective bioplastics were obtained through direct batch mixing under approximately adiabatic and isothermal conditions. The bioplastics showed high values for the storage (G') and loss (G") moduli, suggesting a stronger protein network formed in both processes. The temperature onset and the percentage of weight loss to be estimated were found to be near in both bioplastics. The bioplastics have demonstrated to be materials of interesting potential of use as biodegradable barrier materials.

Phosphoproteome profiling approaches for comprehensive monitoring of cell signaling events in interferon-[gamma] stimulated macrophages

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal.

Mean Squared Residue Based Biclustering Algorithms for the Analysis of Gene Expression Data

Computational Biology is the research are that contributes to the analysis of biological data through the development of algorithms which will address significant research problems.The data from molecular biology includes DNA,RNA ,Protein and Gene expression data.Gene Expression Data provides the expression level of genes under different conditions.Gene expression is the process of transcribing the DNA sequence of a gene into mRNA sequences which in turn are later translated into proteins.The number of copies of mRNA produced is called the expression level of a gene.Gene expression data is organized in the form of a matrix. Rows in the matrix represent genes and columns in the matrix represent experimental conditions.Experimental conditions can be different tissue types or time points.Entries in the gene expression matrix are real values.Through the analysis of gene expression data it is possible to determine the behavioral patterns of genes such as similarity of their behavior,nature of their interaction,their respective contribution to the same pathways and so on. Similar expression patterns are exhibited by the genes participating in the same biological process.These patterns have immense relevance and application in bioinformatics and clinical research.Theses patterns are used in the medical domain for aid in more accurate diagnosis,prognosis,treatment planning.drug discovery and protein network analysis.To identify various patterns from gene expression data,data mining techniques are essential.Clustering is an important data mining technique for the analysis of gene expression data.To overcome the problems associated with clustering,biclustering is introduced.Biclustering refers to simultaneous clustering of both rows and columns of a data matrix. Clustering is a global whereas biclustering is a local model.Discovering local expression patterns is essential for identfying many genetic pathways that are not apparent otherwise.It is therefore necessary to move beyond the clustering paradigm towards developing approaches which are capable of discovering local patterns in gene expression data.A biclusters is a submatrix of the gene expression data matrix.The rows and columns in the submatrix need not be contiguous as in the gene expression data matrix.Biclusters are not disjoint.Computation of biclusters is costly because one will have to consider all the combinations of columans and rows in order to find out all the biclusters.The search space for the biclustering problem is 2 m+n where m and n are the number of genes and conditions respectively.Usually m+n is more than 3000.The biclustering problem is NP-hard.Biclustering is a powerful analytical tool for the biologist.The research reported in this thesis addresses the problem of biclustering.Ten algorithms are developed for the identification of coherent biclusters from gene expression data.All these algorithms are making use of a measure called mean squared residue to search for biclusters.The objective here is to identify the biclusters of maximum size with the mean squared residue lower than a given threshold. All these algorithms begin the search from tightly coregulated submatrices called the seeds.These seeds are generated by K-Means clustering algorithm.The algorithms developed can be classified as constraint based,greedy and metaheuristic.Constarint based algorithms uses one or more of the various constaints namely the MSR threshold and the MSR difference threshold.The greedy approach makes a locally optimal choice at each stage with the objective of finding the global optimum.In metaheuristic approaches particle Swarm Optimization(PSO) and variants of Greedy Randomized Adaptive Search Procedure(GRASP) are used for the identification of biclusters.These algorithms are implemented on the Yeast and Lymphoma datasets.Biologically relevant and statistically significant biclusters are identified by all these algorithms which are validated by Gene Ontology database.All these algorithms are compared with some other biclustering algorithms.Algorithms developed in this work overcome some of the problems associated with the already existing algorithms.With the help of some of the algorithms which are developed in this work biclusters with very high row variance,which is higher than the row variance of any other algorithm using mean squared residue, are identified from both Yeast and Lymphoma data sets.Such biclusters which make significant change in the expression level are highly relevant biologically.

Effect of steeping conditions (sulfur dioxide, lactic acid, and temperature) on starch yield, starch quality, and germ quality from the Intermittent Milling and Dynamic Steeping process (IMDS) for a Brazilian corn hybrid

Effect of lactic acid, SO2, temperature, and their interactions were assessed on the dynamic steeping of a Brazilian dent corn (hybrid XL 606) to determine the ideal relationship among these variables to improve the wet-milling process for starch and corn by-products production. A 2x2x3 factorial experimental design was used with SO2 levels of 0.05 and 0.1% (w/v), lactic acid levels of 0 and 0.5% (v/v), and temperatures of 52, 60, and 68degreesC. Starch yield was used as deciding factor to choose the best treatment. Lactic acid added in the steep solution improved the starch yield by an average of 5.6 percentage points. SO2 was more available to break down the structural protein network at 0.1% than at the 0.05% level. Starch-gluten separation was difficult at 68degreesC. The lactic acid and SO2 concentrations and steeping temperatures for better starch recovery were 0.5, 0.1, and 52degreesC, respectively. The Intermittent Milling and Dynamic Steeping (IMDS) process produced, on average, 1.4% more starch than the conventional 36- hr steeping process. Protein in starch, oil content in germ, and germ damage were used as quality factors. Total steep time can be reduced from 36 hr for conventional wet-milling to 8 hr for the IMDS process.

Adesão de Paracoccidioides brasiliensis e outros fungos anemófilos a teias de aranhas: implicações para a identificação de nichos microbianos no ambiente

The dimorphic pathogenic fungus, Paracoccidioides brasiliensis (Pb), is the etiological agent of paracoccidioidomycosis (PCM), the most important systemic mycosis in Latin America. While the yeast phase can be isolated from patients affected by paracoccidioidomycosis, dogs and naturally infected armadillos; several elements related to the ecology of the saprophytic phase of the pathogen, which is responsible for the production of infective propagules, are poorly understood, hampering the adoption of preventive measures. The demonstration of the high incidence of Pb infection in the 9- banded armadillo, Dasypus novemcinctus, has opened new perspectives for the identification of the pathogen’s habitat. At the opening of the armadillos’ burrows, spider webs are commonly found. The objective of this study was to evaluate the presence of Pb in spider webs samples related to the habitat of armadillos. Spider web samples were collected at Lageado Farm, Botucatu/SP and prepared for microscopic, molecular and mycological analyses. Microscopic analysis showed that different fungi were closely attached to spider web samples. Nested-PCR reaction showed positive amplification for Pb in 4 samples, with identity confirmed by amplicon sequencing. Fungal colonies also included members of Aspergillus, Blastobotrys, Penicillium, Candida, and Sporothrix genera, which are related to opportunistic disease and primary infections of great medical importance. In vitro adhesion tests of mycelia and yeast form of Pb to the spider webs were also performed, in order to analyze the possible physical attraction between fungal cells and the spider web protein network. The results showed a clear adherence of fungal particles to spider webs. In the current literature, there are no studies reporting adhesive properties of microorganisms to spider webs... (Complete abstract click electronic access below)

Effect of the Type of Emulsifying Salt on Microstructure and Rheological Properties of "Requeijao Cremoso" Processed Cheese Spreads

The role of different types of emulsifying saltssodium citrate (TSC), sodium hexametaphosphate (SHMP), sodium tripolyphosphate (STPP) and tetrasodium pyrophosphate (TSPP)on microstructure and rheology of requeijao cremoso processed cheese was determined. The cheeses manufactured with TSC, TSPP, and STPP behaved like concentrated solutions, while the cheese manufactured with SHMP exhibited weak gel behavior and the lowest values for the phase angle (G/G). This means that SHMP cheese had the protein network with the largest amount of molecular interactions, which can be explained by its highest degree of fat emulsification. Rotational viscometry indicated that all the spreadable cheeses behaved like pseudoplastic fluids. The cheeses made with SHMP and TSPP presented low values for the flow behavior index, meaning that viscosity was more dependent on shear rate. Regarding the consistency index, TSPP cheese showed the highest value, which could be attributed to the combined effect of its high pH and homogeneous fat particle size distribution.