Information Flow Analysis of Interactome Networks

Recent studies of cellular networks have revealed modular organizations of genes and proteins. For example, in interactome networks, a module refers to a group of interacting proteins that form molecular complexes and/or biochemical pathways and together mediate a biological process. However, it is still poorly understood how biological information is transmitted between different modules. We have developed information flow analysis, a new computational approach that identifies proteins central to the transmission of biological information throughout the network. In the information flow analysis, we represent an interactome network as an electrical circuit, where interactions are modeled as resistors and proteins as interconnecting junctions. Construing the propagation of biological signals as flow of electrical current, our method calculates an information flow score for every protein. Unlike previous metrics of network centrality such as degree or betweenness that only consider topological features, our approach incorporates confidence scores of protein–protein interactions and automatically considers all possible paths in a network when evaluating the importance of each protein. We apply our method to the interactome networks of Saccharomyces cerevisiae and Caenorhabditis elegans. We find that the likelihood of observing lethality and pleiotropy when a protein is eliminated is positively correlated with the protein's information flow score. Even among proteins of low degree or low betweenness, high information scores serve as a strong predictor of loss-of-function lethality or pleiotropy. The correlation between information flow scores and phenotypes supports our hypothesis that the proteins of high information flow reside in central positions in interactome networks. We also show that the ranks of information flow scores are more consistent than that of betweenness when a large amount of noisy data is added to an interactome. Finally, we combine gene expression data with interaction data in C. elegans and construct an interactome network for muscle-specific genes. We find that genes that rank high in terms of information flow in the muscle interactome network but not in the entire network tend to play important roles in muscle function. This framework for studying tissue-specific networks by the information flow model can be applied to other tissues and other organisms as well.     

The SUMOylation pathway suppresses arbovirus replication in Aedes aegypti cells

Mosquitoes are responsible for the transmission of many clinically important arboviruses that cause significant levels of annual mortality and socioeconomic health burden worldwide. Deciphering the mechanisms by which mosquitoes modulate arbovirus infection is crucial to understand how viral-host interactions promote vector transmission and human disease. SUMOylation is a post-translational modification that leads to the covalent attachment of the Small Ubiquitin-like MOdifier (SUMO) protein to host factors, which in turn can modulate their stability, interaction networks, sub-cellular localisation, and biochemical function. While the SUMOylation pathway is known to play a key role in the regulation of host immune defences to virus infection in humans, the importance of this pathway during arbovirus infection in mosquito vectors, such as Aedes aegypti (Ae. aegypti), remains unknown. Here we characterise the sequence, structure, biochemical properties, and tissue-specific expression profiles of component proteins of the Ae. aegypti SUMOylation pathway. We demonstrate significant biochemical differences between Ae. aegypti and Homo sapiens SUMOylation pathways and identify cell-type specific patterns of SUMO expression in Ae. aegypti tissues known to support arbovirus replication. Importantly, depletion of core SUMOylation effector proteins (SUMO, Ubc9 and PIAS) in Ae. aegypti cells led to enhanced levels of arbovirus replication from three different families; Zika (Flaviviridae), Semliki Forest (Togaviridae), and Bunyamwera (Bunyaviridae) viruses. Our findings identify an important role for mosquito SUMOylation in the cellular restriction of arboviruses that may directly influence vector competence and transmission of clinically important arboviruses.     

In silico reconstitution of Listeria propulsion exhibits nano-saltation

To understand how the actin-polymerization-mediated movements in cells emerge from myriad individual protein–protein interactions, we developed a computational model of Listeria monocytogenes propulsion that explicitly simulates a large number of monomer-scale biochemical and mechanical interactions. The literature on actin networks and L. monocytogenes motility provides the foundation for a realistic mathematical/computer simulation, because most of the key rate constants governing actin network dynamics have been measured. We use a cluster of 80 Linux processors and our own suite of simulation and analysis software to characterize salient features of bacterial motion. Our “in silico reconstitution” produces qualitatively realistic bacterial motion with regard to speed and persistence of motion and actin tail morphology. The model also produces smaller scale emergent behavior; we demonstrate how the observed nano-saltatory motion of L. monocytogenes, in which runs punctuate pauses, can emerge from a cooperative binding and breaking of attachments between actin filaments and the bacterium. We describe our modeling methodology in detail, as it is likely to be useful for understanding any subcellular system in which the dynamics of many simple interactions lead to complex emergent behavior, e.g., lamellipodia and filopodia extension, cellular organization, and cytokinesis.     

Quantitative Redox Biology: An Approach to Understand the Role of Reactive Species in Defining the Cellular Redox Environment

Systems biology is now recognized as a needed approach to understand the dynamics of inter- and intra-cellular processes. Redox processes are at the foundation of nearly all aspects of biology. Free radicals, related oxidants, and antioxidants are central to the basic functioning of cells and tissues. They set the cellular redox environment and, therefore, are the key to regulation of biochemical pathways and networks, thereby influencing organism health. To understand how short-lived, quasi-stable species, such as superoxide, hydrogen peroxide, and nitric oxide, connect to the metabolome, proteome, lipidome, and genome we need absolute quantitative information on all redox active compounds as well as thermodynamic and kinetic information on their reactions, i.e., knowledge of the complete redoxome. Central to the state of the redoxome are the interactive details of the superoxide/peroxide formation and removal systems. Quantitative information is essential to establish the dynamic mathematical models needed to reveal the temporal evolution of biochemical pathways and networks. This new field of Quantitative Redox Biology will allow researchers to identify new targets for intervention to advance our efforts to achieve optimal human health.     

The Modular Organization of Protein Interactions in Escherichia coli

Escherichia coli serves as an excellent model for the study of fundamental cellular processes such as metabolism, signalling and gene expression. Understanding the function and organization of proteins within these processes is an important step towards a ‘systems’ view of E. coli. Integrating experimental and computational interaction data, we present a reliable network of 3,989 functional interactions between 1,941 E. coli proteins (∼45% of its proteome). These were combined with a recently generated set of 3,888 high-quality physical interactions between 918 proteins and clustered to reveal 316 discrete modules. In addition to known protein complexes (e.g., RNA and DNA polymerases), we identified modules that represent biochemical pathways (e.g., nitrate regulation and cell wall biosynthesis) as well as batteries of functionally and evolutionarily related processes. To aid the interpretation of modular relationships, several case examples are presented, including both well characterized and novel biochemical systems. Together these data provide a global view of the modular organization of the E. coli proteome and yield unique insights into structural and evolutionary relationships in bacterial networks.     

Connecting Mutations of the RNA Polymerase II C-Terminal Domain to Complex Phenotypic Changes Using Combined Gene Expression and Network Analyses

The C-terminal domain (CTD) of the largest subunit in DNA-dependent RNA polymerase II (RNAP II) is essential for mRNA synthesis and processing, through coordination of an astounding array of protein-protein interactions. Not surprisingly, CTD mutations can have complex, pleiotropic impacts on phenotype. For example, insertions of five alanine residues between CTD diheptads in yeast, which alter the CTD''s overall tandem structure and physically separate core functional units, dramatically reduce growth rate and result in abnormally large cells that accumulate increased DNA content over time. Patterns by which specific CTD-protein interactions are disrupted by changes in CTD structure, as well as how downstream metabolic pathways are impacted, are difficult to target for direct experimental analyses. In an effort to connect an altered CTD to complex but quantifiable phenotypic changes, we applied network analyses of genes that are differentially expressed in our five alanine CTD mutant, combined with established genetic interactions from the Saccharomyces cerevisiae Genome Database (SGD). We were able to identify candidate genetic pathways, and several key genes, that could explain how this change in CTD structure leads to the specific phenotypic changes observed. These hypothetical networks identify links between CTD-associated proteins and mitotic function, control of cell cycle checkpoint mechanisms, and expression of cell wall and membrane components. Such results can help to direct future genetic and biochemical investigations that tie together the complex impacts of the CTD on global cellular metabolism.     

Cell signaling as a cognitive process

Cellular identity as defined through morphology and function emerges from intracellular signaling networks that communicate between cells. Based on recursive interactions within and among these intracellular networks, dynamical solutions in terms of biochemical behavior are generated that can differ from those in isolated cells. In this way, cellular heterogeneity in tissues can be established, implying that cell identity is not intrinsically predetermined by the genetic code but is rather dynamically maintained in a cognitive manner. We address how to experimentally measure the flow of information in intracellular biochemical networks and demonstrate that even simple causality motifs can give rise to rich, context‐dependent dynamic behavior. The concept how intercellular communication can result in novel dynamical solutions is applied to provide a contextual perspective on cell differentiation and tumorigenesis.     

Drug-efficacy depends on the inhibitor type and the target position in a metabolic network--a systematic study

Drug discovery usually focuses on candidate molecules that affect individual reactions with presumed essential functions in the cellular reaction network, especially in the development of diseases. Unfortunately, appropriately designed drugs often fail to show the expected biological effect, since the multitude of interactions in the biochemical reaction network buffers the individual changes or causes significant side effects. We address this problem through a computational approach, which considers the effect of drug application within a generalized biochemical pathway and by studying the effect of changes regarding the type and strength of inhibitors on the reduction of flux. This allows us to systematically search for the appropriate target and for type and concentration of the optimal inhibitor. We propose the flux selectivity as a measure for the discrimination of the effect on different pathways. Since the calculation of the flux selectivity is based on flux control coefficients that are calculated in the non-affected state, it is also a means for predicting the inhibitor efficacy. Furthermore, we will propose how to increase discriminative inhibition in the case of a parasitic disease by using multi-target drugs.This work is devoted to the memorial of our teacher Reinhart Heinrich, who made important contributions to the investigation of the regulation of metabolic networks, namely by introducing and applying the concept of metabolic control.     

Epistatic interaction maps relative to multiple metabolic phenotypes

An epistatic interaction between two genes occurs when the phenotypic impact of one gene depends on another gene, often exposing a functional association between them. Due to experimental scalability and to evolutionary significance, abundant work has been focused on studying how epistasis affects cellular growth rate, most notably in yeast. However, epistasis likely influences many different phenotypes, affecting our capacity to understand cellular functions, biochemical networks adaptation, and genetic diseases. Despite its broad significance, the extent and nature of epistasis relative to different phenotypes remain fundamentally unexplored. Here we use genome-scale metabolic network modeling to investigate the extent and properties of epistatic interactions relative to multiple phenotypes. Specifically, using an experimentally refined stoichiometric model for Saccharomyces cerevisiae, we computed a three-dimensional matrix of epistatic interactions between any two enzyme gene deletions, with respect to all metabolic flux phenotypes. We found that the total number of epistatic interactions between enzymes increases rapidly as phenotypes are added, plateauing at approximately 80 phenotypes, to an overall connectivity that is roughly 8-fold larger than the one observed relative to growth alone. Looking at interactions across all phenotypes, we found that gene pairs interact incoherently relative to different phenotypes, i.e. antagonistically relative to some phenotypes and synergistically relative to others. Specific deletion-deletion-phenotype triplets can be explained metabolically, suggesting a highly informative role of multi-phenotype epistasis in mapping cellular functions. Finally, we found that genes involved in many interactions across multiple phenotypes are more highly expressed, evolve slower, and tend to be associated with diseases, indicating that the importance of genes is hidden in their total phenotypic impact. Our predictions indicate a pervasiveness of nonlinear effects in how genetic perturbations affect multiple metabolic phenotypes. The approaches and results reported could influence future efforts in understanding metabolic diseases and the role of biochemical regulation in the cell.     

Building the Microtubule Cytoskeleton Piece by Piece

The microtubule (MT) cytoskeleton gives cells their shape, organizes the cellular interior, and segregates chromosomes. These functions rely on the precise arrangement of MTs, which is achieved by the coordinated action of MT-associated proteins (MAPs). We highlight the first and most important examples of how different MAP activities are combined in vitro to create an ensemble function that exceeds the simple addition of their individual activities, and how the Xenopus laevis egg extract system has been utilized as a powerful intermediate between cellular and purified systems to uncover the design principles of self-organized MT networks in the cell.     

Dynamic Changes in Protein Functional Linkage Networks Revealed by Integration with Gene Expression Data

Response of cells to changing environmental conditions is governed by the dynamics of intricate biomolecular interactions. It may be reasonable to assume, proteins being the dominant macromolecules that carry out routine cellular functions, that understanding the dynamics of protein∶protein interactions might yield useful insights into the cellular responses. The large-scale protein interaction data sets are, however, unable to capture the changes in the profile of protein∶protein interactions. In order to understand how these interactions change dynamically, we have constructed conditional protein linkages for Escherichia coli by integrating functional linkages and gene expression information. As a case study, we have chosen to analyze UV exposure in wild-type and SOS deficient E. coli at 20 minutes post irradiation. The conditional networks exhibit similar topological properties. Although the global topological properties of the networks are similar, many subtle local changes are observed, which are suggestive of the cellular response to the perturbations. Some such changes correspond to differences in the path lengths among the nodes of carbohydrate metabolism correlating with its loss in efficiency in the UV treated cells. Similarly, expression of hubs under unique conditions reflects the importance of these genes. Various centrality measures applied to the networks indicate increased importance for replication, repair, and other stress proteins for the cells under UV treatment, as anticipated. We thus propose a novel approach for studying an organism at the systems level by integrating genome-wide functional linkages and the gene expression data.     

The Carbon Assimilation Network in Escherichia coli Is Densely Connected and Largely Sign-Determined by Directions of Metabolic Fluxes

Gene regulatory networks consist of direct interactions but also include indirect interactions mediated by metabolites and signaling molecules. We describe how these indirect interactions can be derived from a model of the underlying biochemical reaction network, using weak time-scale assumptions in combination with sensitivity criteria from metabolic control analysis. We apply this approach to a model of the carbon assimilation network in Escherichia coli. Our results show that the derived gene regulatory network is densely connected, contrary to what is usually assumed. Moreover, the network is largely sign-determined, meaning that the signs of the indirect interactions are fixed by the flux directions of biochemical reactions, independently of specific parameter values and rate laws. An inversion of the fluxes following a change in growth conditions may affect the signs of the indirect interactions though. This leads to a feedback structure that is at the same time robust to changes in the kinetic properties of enzymes and that has the flexibility to accommodate radical changes in the environment.     

In Silico Reconstitution of Listeria Propulsion Exhibits Nano-Saltation

To understand how the actin-polymerization-mediated movements in cells emerge from myriad individual protein–protein interactions, we developed a computational model of Listeria monocytogenes propulsion that explicitly simulates a large number of monomer-scale biochemical and mechanical interactions. The literature on actin networks and L. monocytogenes motility provides the foundation for a realistic mathematical/computer simulation, because most of the key rate constants governing actin network dynamics have been measured. We use a cluster of 80 Linux processors and our own suite of simulation and analysis software to characterize salient features of bacterial motion. Our “in silico reconstitution” produces qualitatively realistic bacterial motion with regard to speed and persistence of motion and actin tail morphology. The model also produces smaller scale emergent behavior; we demonstrate how the observed nano-saltatory motion of L. monocytogenes, in which runs punctuate pauses, can emerge from a cooperative binding and breaking of attachments between actin filaments and the bacterium. We describe our modeling methodology in detail, as it is likely to be useful for understanding any subcellular system in which the dynamics of many simple interactions lead to complex emergent behavior, e.g., lamellipodia and filopodia extension, cellular organization, and cytokinesis.     

Community Structure Detection for Overlapping Modules through Mathematical Programming in Protein Interaction Networks

Community structure detection has proven to be important in revealing the underlying properties of complex networks. The standard problem, where a partition of disjoint communities is sought, has been continually adapted to offer more realistic models of interactions in these systems. Here, a two-step procedure is outlined for exploring the concept of overlapping communities. First, a hard partition is detected by employing existing methodologies. We then propose a novel mixed integer non linear programming (MINLP) model, known as OverMod, which transforms disjoint communities to overlapping. The procedure is evaluated through its application to protein-protein interaction (PPI) networks of the rat, E. coli, yeast and human organisms. Connector nodes of hard partitions exhibit topological and functional properties indicative of their suitability as candidates for multiple module membership. OverMod identifies two types of connector nodes, inter and intra-connector, each with their own particular characteristics pertaining to their topological and functional role in the organisation of the network. Inter-connector proteins are shown to be highly conserved proteins participating in pathways that control essential cellular processes, such as proliferation, differentiation and apoptosis and their differences with intra-connectors is highlighted. Many of these proteins are shown to possess multiple roles of distinct nature through their participation in different network modules, setting them apart from proteins that are simply ‘hubs’, i.e. proteins with many interaction partners but with a more specific biochemical role.     

An accurate binding interaction model in de novo computational protein design of interactions: If you build it,they will bind

Computational protein design efforts aim to create novel proteins and functions in an automated manner and, in the process, these efforts shed light on the factors shaping natural proteins. The focus of these efforts has progressed from the interior of proteins to their surface and the design of functions, such as binding or catalysis. Here we examine progress in the development of robust methods for the computational design of non-natural interactions between proteins and molecular targets such as other proteins or small molecules. This problem is referred to as the de novo computational design of interactions. Recent successful efforts in de novo enzyme design and the de novo design of protein–protein interactions open a path towards solving this problem. We examine the common themes in these efforts, and review recent studies aimed at understanding the nature of successes and failures in the de novo computational design of interactions. While several approaches culminated in success, the use of a well-defined structural model for a specific binding interaction in particular has emerged as a key strategy for a successful design, and is therefore reviewed with special consideration.     

The molecular basis of spinocerebellar ataxia type 48 caused by a de novo mutation in the ubiquitin ligase CHIP

The spinocerebellar ataxias (SCAs) are a class of incurable diseases characterized by degeneration of the cerebellum that results in movement disorder. Recently, a new heritable form of SCA, spinocerebellar ataxia type 48 (SCA48), was attributed to dominant mutations in STIP1 homology and U box-containing 1 (STUB1); however, little is known about how these mutations cause SCA48. STUB1 encodes for the protein C terminus of Hsc70 interacting protein (CHIP), an E3 ubiquitin ligase. CHIP is known to regulate proteostasis by recruiting chaperones via a N-terminal tetratricopeptide repeat domain and recruiting E2 ubiquitin-conjugating enzymes via a C-terminal U-box domain. These interactions allow CHIP to mediate the ubiquitination of chaperone-bound, misfolded proteins to promote their degradation via the proteasome. Here we have identified a novel, de novo mutation in STUB1 in a patient with SCA48 encoding for an A52G point mutation in the tetratricopeptide repeat domain of CHIP. Utilizing an array of biophysical, biochemical, and cellular assays, we demonstrate that the CHIP^A52G point mutant retains E3-ligase activity but has decreased affinity for chaperones. We further show that this mutant decreases cellular fitness in response to certain cellular stressors and induces neurodegeneration in a transgenic Caenorhabditis elegans model of SCA48. Together, our data identify the A52G mutant as a cause of SCA48 and provide molecular insight into how mutations in STUB1 cause SCA48.