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.

Analysis of protein networks in resting and collagen receptor (GPVI)-stimulated platelet sub-proteomes.

Proteomics approaches have made important contributions to the characterisation of platelet regulatory mechanisms. A common problem encountered with this method, however, is the masking of low-abundance (e.g. signalling) proteins in complex mixtures by highly abundant proteins. In this study, subcellular fractionation of washed human platelets either inactivated or stimulated with the glycoprotein (GP) VI collagen receptor agonist, collagen-related peptide, reduced the complexity of the platelet proteome. The majority of proteins identified by tandem mass spectrometry are involved in signalling. The effect of GPVI stimulation on levels of specific proteins in subcellular compartments was compared and analysed using in silico quantification, and protein associations were predicted using STRING (the search tool for recurring instances of neighbouring genes/proteins). Interestingly, we observed that some proteins that were previously unidentified in platelets including teneurin-1 and Van Gogh-like protein 1, translocated to the membrane upon GPVI stimulation. Newly identified proteins may be involved in GPVI signalling nodes of importance for haemostasis and thrombosis.

Genetic and environmental risk factors in congenital heart disease functionally converge in protein networks driving heart development

Congenital heart disease (CHD) occurs in similar to 1% of newborns. CHD arises from many distinct etiologies, ranging from genetic or genomic variation to exposure to teratogens, which elicit diverse cell and molecular responses during cardiac development. To systematically explore the relationships between CHD risk factors and responses, we compiled and integrated comprehensive datasets from studies of CHD in humans and model organisms. We examined two alternative models of potential functional relationships between genes in these datasets: direct convergence, in which CHD risk factors significantly and directly impact the same genes and molecules and functional convergence, in which risk factors significantly impact different molecules that participate in a discrete heart development network. We observed no evidence for direct convergence. In contrast, we show that CHD risk factors functionally converge in protein networks driving the development of specific anatomical structures (e.g., outflow tract, ventricular septum, and atrial septum) that are malformed by CHD. This integrative analysis of CHD risk factors and responses suggests a complex pattern of functional interactions between genomic variation and environmental exposures that modulate critical biological systems during heart development.

Comparison of module detection algorithms in protein networks and investigation of the biological meaning of predicted modules

Background
It is generally acknowledged that a functional understanding of a biological system can only be obtained by an understanding of the collective of molecular interactions in form of biological networks. Protein networks are one particular network type of special importance, because proteins form the functional base units of every biological cell. On a mesoscopic level of protein networks, modules are of significant importance because these building blocks may be the next elementary functional level above individual proteins allowing to gain insight into fundamental organizational principles of biological cells.
Results
In this paper, we provide a comparative analysis of five popular and four novel module detection algorithms. We study these module prediction methods for simulated benchmark networks as well as 10 biological protein interaction networks (PINs). A particular focus of our analysis is placed on the biological meaning of the predicted modules by utilizing the Gene Ontology (GO) database as gold standard for the definition of biological processes. Furthermore, we investigate the robustness of the results by perturbing the PINs simulating in this way our incomplete knowledge of protein networks.
Conclusions
Overall, our study reveals that there is a large heterogeneity among the different module prediction algorithms if one zooms-in the biological level of biological processes in the form of GO terms and all methods are severely affected by a slight perturbation of the networks. However, we also find pathways that are enriched in multiple modules, which could provide important information about the hierarchical organization of the system

Limitations of Gene Duplication Models: Evolution of Modules in Protein Interaction Networks

It has been generally acknowledged that the module structure of protein interaction networks plays a crucial role with respect to the functional understanding of these networks. In this paper, we study evolutionary aspects of the module structure of protein interaction networks, which forms a mesoscopic level of description with respect to the architectural principles of networks. The purpose of this paper is to investigate limitations of well known gene duplication models by showing that these models are lacking crucial structural features present in protein interaction networks on a mesoscopic scale. This observation reveals our incomplete understanding of the structural evolution of protein networks on the module level. © 2012 Emmert-Streib.

Comparison of the interactomic networks of different species in terms of accessibility

Protein-protein interaction networks were investigated in terms of outward accessibility, which quantifies the effectiveness of each protein in accessing other proteins and is related to the internality of nodes. By comparing the accessibility between 144 ortholog proteins in yeast and the fruit fly, we found that the accessibility tends to be higher among proteins in the fly than in yeast. In addition, z-scores of the accessibility calculated for different species revealed that the protein networks of less evolved species tend to be more random than those of more evolved species. The accessibility was also used to identify the border of the yeast protein interaction network, which was found to be mainly composed of viable proteins.

Seeking for simplicity in complex networks

Complex networks can be understood as graphs whose connectivity properties deviate from those of regular or near-regular graphs, which are understood as being ""simple"". While a great deal of the attention so far dedicated to complex networks has been duly driven by the ""complex"" nature of these structures, in this work we address the identification of their simplicity. The basic idea is to seek for subgraphs whose nodes exhibit similar measurements. This approach paves the way for complementing the characterization of networks, including results suggesting that the protein-protein interaction networks, and to a lesser extent also the Internet, may be getting simpler over time. Copyright (C) EPLA, 2009

Intrinsically disordered proteins and conformational noise Implications in cancer

Intrinsically disordered proteins, IDPs, are proteins that lack a rigid 3D structure under physiological conditions, at least in vitro. Despite the lack of structure, IDPs play important roles in biological processes and transition from disorder to order upon binding to their targets. With multiple conformational states and rapid conformational dynamics, they engage in myriad and often ``promiscuous'' interactions. These stochastic interactions between IDPs and their partners, defined here as conformational noise, is an inherent characteristic of IDP interactions. The collective effect of conformational noise is an ensemble of protein network configurations, from which the most suitable can be explored in response to perturbations, conferring protein networks with remarkable flexibility and resilience. Moreover, the ubiquitous presence of IDPs as transcriptional factors and, more generally, as hubs in protein networks, is indicative of their role in propagation of transcriptional (genetic) noise. As effectors of transcriptional and conformational noise, IDPs rewire protein networks and unmask latent interactions in response to perturbations. Thus, noise-driven activation of latent pathways could underlie state-switching events such as cellular transformation in cancer. To test this hypothesis, we created a model of a protein network with the topological characteristics of a cancer protein network and tested its response to a perturbation in presence of IDP hubs and conformational noise. Because numerous IDPs are found to be epigenetic modifiers and chromatin remodelers, we hypothesize that they could further channel noise into stable, heritable genotypic changes.

MPTP activates ASK1-p38 MAPK signaling pathway through TNF-dependent Trx1 oxidation in parkinsonism mouse model

Activation of apoptosis signal regulating kinase 1 (ASK1)-p38 MAPK death signaling cascade is irn plicated in the death of dopaminergic neurons in substantia nigra in Parkinson's disease (PD). We investigated upstream activators of ASK1 using an MPTP mouse model of parkinsonism and assessed the temporal cascade of death signaling in ventral midbrain (VMB) and striatum (ST). MPTP selectively activated ASK1 and downstream 1)38 MAPK in a time dependent manner in VMB alone. This occurred through selective protein thiol oxidation of the redox-sensitive thiol disulfide oxidoreductase, thiorcdoxin (Trxl), resulting in release of its inhibitory association with ASK1, while glutathione-S-transferase ji 1 (GSTM1) remained in reduced form in association with ASK1. Levels of tumor necrosis factor (TNF), a known activator of ASK1, increased early after MPTP in VMB. Protein ovariation netvvork analysis (PCNA) using protein states as nodes revealed TNF to be an important node regulating the ASK1 signaling cascade. In confirmation, blocking MPTP-mecliated TNF signaling through intrathecal administration of TNFneutralizing antibody prevented Trxl oxidation and downstream ASK1-p38 MAPK activation. Averting an early increase in TNF, which leads to protein thiol oxidation resulting in activation of ASK1-p38 signaling, may be critical for neuroprotection in PD. Importantly, network analysis can help in understanding the cause/effect relationship within protein networks in complex disease states. (C) 2015 Published by Elsevier Inc.

Tigecycline challenge triggers sRNA production in Salmonella enterica serovar Typhimurium

Background: Bacteria employ complex transcriptional networks involving multiple genes in response to stress, which is not limited to gene and protein networks but now includes small RNAs (sRNAs). These regulatory RNA molecules are increasingly shown to be able to initiate regulatory cascades and modulate the expression of multiple genes that are involved in or required for survival under environmental challenge. Despite mounting evidence for the importance of sRNAs in stress response, their role upon antibiotic exposure remains unknown. In this study, we sought to determine firstly, whether differential expression of sRNAs occurs upon antibiotic exposure and secondly, whether these sRNAs could be attributed to microbial tolerance to antibiotics.

Results: A small scale sRNA cloning strategy of Salmonella enterica serovar Typhimurium SL1344 challenged with half the minimal inhibitory concentration of tigecycline identified four sRNAs (sYJ5, sYJ20, sYJ75 and sYJ118) which were reproducibly upregulated in the presence of either tigecycline or tetracycline. The coding sequences of the four sRNAs were found to be conserved across a number of species. Genome analysis found that sYJ5 and sYJ118 mapped between the 16S and 23S rRNA encoding genes. sYJ20 (also known as SroA) is encoded upstream of the tbpAyabKyabJ operon and is classed as a riboswitch, whilst its role in antibiotic stress-response appears independent of its riboswitch function. sYJ75 is encoded between genes that are involved in enterobactin transport and metabolism. Additionally we find that the genetic deletion of sYJ20 rendered a reduced viability phenotype in the presence of tigecycline, which was recovered when complemented. The upregulation of some of these sRNAs were also observed when S. Typhimurium was challenged by ampicillin (sYJ5, 75 and 118); or when Klebsiella pneumoniae was challenged by tigecycline (sYJ20 and 118).

Conclusions: Small RNAs are overexpressed as a result of antibiotic exposure in S. Typhimurium where the same molecules are upregulated in a related species or after exposure to different antibiotics. sYJ20, a riboswitch, appears to possess a trans-regulatory sRNA role in antibiotic tolerance. These findings imply that the sRNA mediated response is a component of the bacterial response to antibiotic challenge.

Role of the AP2 beta-appendage hub in recruiting partners for clathrin-coated vesicle assembly

Adaptor protein complex 2 alpha and beta-appendage domains act as hubs for the assembly of accessory protein networks involved in clathrin-coated vesicle formation. We identify a large repertoire of beta-appendage interactors by mass spectrometry. These interact with two distinct ligand interaction sites on the beta-appendage (the "top" and "side" sites) that bind motifs distinct from those previously identified on the alpha-appendage. We solved the structure of the beta-appendage with a peptide from the accessory protein Eps15 bound to the side site and with a peptide from the accessory cargo adaptor beta-arrestin bound to the top site. We show that accessory proteins can bind simultaneously to multiple appendages, allowing these to cooperate in enhancing ligand avidities that appear to be irreversible in vitro. We now propose that clathrin, which interacts with the beta-appendage, achieves ligand displacement in vivo by self-polymerisation as the coated pit matures. This changes the interaction environment from liquid-phase, affinity-driven interactions, to interactions driven by solid-phase stability ("matricity"). Accessory proteins that interact solely with the appendages are thereby displaced to areas of the coated pit where clathrin has not yet polymerised. However, proteins such as beta-arrestin (non-visual arrestin) and autosomal recessive hypercholesterolemia protein, which have direct clathrin interactions, will remain in the coated pits with their interacting receptors.

Understanding Subsystems in Biology through Dimensionality Reduction, Graph Partitioning and Analytical Modeling

Biological systems exhibit rich and complex behavior through the orchestrated interplay of a large array of components. It is hypothesized that separable subsystems with some degree of functional autonomy exist; deciphering their independent behavior and functionality would greatly facilitate understanding the system as a whole. Discovering and analyzing such subsystems are hence pivotal problems in the quest to gain a quantitative understanding of complex biological systems. In this work, using approaches from machine learning, physics and graph theory, methods for the identification and analysis of such subsystems were developed. A novel methodology, based on a recent machine learning algorithm known as non-negative matrix factorization (NMF), was developed to discover such subsystems in a set of large-scale gene expression data. This set of subsystems was then used to predict functional relationships between genes, and this approach was shown to score significantly higher than conventional methods when benchmarking them against existing databases. Moreover, a mathematical treatment was developed to treat simple network subsystems based only on their topology (independent of particular parameter values). Application to a problem of experimental interest demonstrated the need for extentions to the conventional model to fully explain the experimental data. Finally, the notion of a subsystem was evaluated from a topological perspective. A number of different protein networks were examined to analyze their topological properties with respect to separability, seeking to find separable subsystems. These networks were shown to exhibit separability in a nonintuitive fashion, while the separable subsystems were of strong biological significance. It was demonstrated that the separability property found was not due to incomplete or biased data, but is likely to reflect biological structure.

Functional studies of the deubiquitinating enzymes USP19, USP4 and UCH-L1

El marcaje de proteínas con ubiquitina, conocido como ubiquitinación, cumple diferentes funciones que incluyen la regulación de varios procesos celulares, tales como: la degradación de proteínas por medio del proteosoma, la reparación del ADN, la señalización mediada por receptores de membrana, y la endocitosis, entre otras (1). Las moléculas de ubiquitina pueden ser removidas de sus sustratos gracias a la acción de un gran grupo de proteasas, llamadas enzimas deubiquitinizantes (DUBs) (2). Las DUBs son esenciales para la manutención de la homeostasis de la ubiquitina y para la regulación del estado de ubiquitinación de diferentes sustratos. El gran número y la diversidad de DUBs descritas refleja tanto su especificidad como su utilización para regular un amplio espectro de sustratos y vías celulares. Aunque muchas DUBs han sido estudiadas a profundidad, actualmente se desconocen los sustratos y las funciones biológicas de la mayoría de ellas. En este trabajo se investigaron las funciones de las DUBs: USP19, USP4 y UCH-L1. Utilizando varias técnicas de biología molecular y celular se encontró que: i) USP19 es regulada por las ubiquitin ligasas SIAH1 y SIAH2 ii) USP19 es importante para regular HIF-1α, un factor de transcripción clave en la respuesta celular a hipoxia, iii) USP4 interactúa con el proteosoma, iv) La quimera mCherry-UCH-L1 reproduce parcialmente los fenotipos que nuestro grupo ha descrito previamente al usar otros constructos de la misma enzima, y v) UCH-L1 promueve la internalización de la bacteria Yersinia pseudotuberculosis.

Kinome profiling of osteoblasts on hydroxyapatite opens new avenues on biomaterial cell signaling

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)