Signalogs: orthology-based identification of novel signaling pathway components in three metazoans

Background

Uncovering novel components of signal transduction pathways and their interactions within species is a central task in current biological research. Orthology alignment and functional genomics approaches allow the effective identification of signaling proteins by cross-species data integration. Recently, functional annotation of orthologs was transferred across organisms to predict novel roles for proteins. Despite the wide use of these methods, annotation of complete signaling pathways has not yet been transferred systematically between species.

Principal Findings

Here we introduce the concept of ‘signalog’ to describe potential novel signaling function of a protein on the basis of the known signaling role(s) of its ortholog(s). To identify signalogs on genomic scale, we systematically transferred signaling pathway annotations among three animal species, the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster, and humans. Using orthology data from InParanoid and signaling pathway information from the SignaLink database, we predict 88 worm, 92 fly, and 73 human novel signaling components. Furthermore, we developed an on-line tool and an interactive orthology network viewer to allow users to predict and visualize components of orthologous pathways. We verified the novelty of the predicted signalogs by literature search and comparison to known pathway annotations. In C. elegans, 6 out of the predicted novel Notch pathway members were validated experimentally. Our approach predicts signaling roles for 19 human orthodisease proteins and 5 known drug targets, and suggests 14 novel drug target candidates.

Conclusions

Orthology-based pathway membership prediction between species enables the identification of novel signaling pathway components that we referred to as signalogs. Signalogs can be used to build a comprehensive signaling network in a given species. Such networks may increase the biomedical utilization of C. elegans and D. melanogaster. In humans, signalogs may identify novel drug targets and new signaling mechanisms for approved drugs.     

Quantitative Comparison of Catalytic Mechanisms and Overall Reactions in Convergently Evolved Enzymes: Implications for Classification of Enzyme Function

Functionally analogous enzymes are those that catalyze similar reactions on similar substrates but do not share common ancestry, providing a window on the different structural strategies nature has used to evolve required catalysts. Identification and use of this information to improve reaction classification and computational annotation of enzymes newly discovered in the genome projects would benefit from systematic determination of reaction similarities. Here, we quantified similarity in bond changes for overall reactions and catalytic mechanisms for 95 pairs of functionally analogous enzymes (non-homologous enzymes with identical first three numbers of their EC codes) from the MACiE database. Similarity of overall reactions was computed by comparing the sets of bond changes in the transformations from substrates to products. For similarity of mechanisms, sets of bond changes occurring in each mechanistic step were compared; these similarities were then used to guide global and local alignments of mechanistic steps. Using this metric, only 44% of pairs of functionally analogous enzymes in the dataset had significantly similar overall reactions. For these enzymes, convergence to the same mechanism occurred in 33% of cases, with most pairs having at least one identical mechanistic step. Using our metric, overall reaction similarity serves as an upper bound for mechanistic similarity in functional analogs. For example, the four carbon-oxygen lyases acting on phosphates (EC 4.2.3) show neither significant overall reaction similarity nor significant mechanistic similarity. By contrast, the three carboxylic-ester hydrolases (EC 3.1.1) catalyze overall reactions with identical bond changes and have converged to almost identical mechanisms. The large proportion of enzyme pairs that do not show significant overall reaction similarity (56%) suggests that at least for the functionally analogous enzymes studied here, more stringent criteria could be used to refine definitions of EC sub-subclasses for improved discrimination in their classification of enzyme reactions. The results also indicate that mechanistic convergence of reaction steps is widespread, suggesting that quantitative measurement of mechanistic similarity can inform approaches for functional annotation.     

Choke point analysis of metabolic pathways in E.histolytica: a computational approach for drug target identification

With the Entamoeba genome essentially complete, the organism can be studied from a whole genome standpoint. The understanding of cellular mechanisms and interactions between cellular components is instrumental to the development of new effective drugs and vaccines. Metabolic pathway analysis is becoming increasingly important for assessing inherent network properties in reconstructed biochemical reaction networks. Metabolic pathways illustrate how proteins work in concert to produce cellular compounds or to transmit information at different levels. Identification of drug targets in E. histolytica through metabolic pathway analysis promises to be a novel approach in this direction. This article focuses on the identification of drug targets by subjecting the Entamoeba genome to BLAST with the e-value inclusion threshold set to 0.005 and choke point analysis. A total of 86.9 percent of proposed drug targets with biological evidence are chokepoint reactions in Entamoeba genome database.     

CADLIVE for constructing a large-scale biochemical network based on a simulation-directed notation and its application to yeast cell cycle

The further understanding of the mechanisms of gene regulatory networks requires comprehensive tools for both the representation of complicated signal transduction pathways and the in silico identification of genomic signals that govern the regulation of gene expression. Consequently, sophisticated notation must be developed to represent the signal transduction pathways in a form that can be readily processed by both computers and humans. We propose the regulator–reaction equations combined with detailed attributes including the associated cellular component, molecular function, and biological process and present the simulation-directed graphical notation that is derived from modification of Kohn’s method. We have developed the software suite, CADLIVE (Computer-Aided Design of LIVing systEms), which features a graphical user interface (GUI) to edit large-scale maps of complicated signal transduction pathways using a conventional XML-based representation. The regulator–reaction equations represent not only mechanistic reactions, but also semantic models containing ambiguous and incomplete processes. In order to demonstrate the feasibility of CADLIVE, we constructed a detailed map of the budding yeast cell cycle, which consists of 184 molecules and 152 reactions, in a really compact space. CADLIVE enables one to look at the whole view of a large-scale map, to integrate postgenomic data into the map, and to computationally simulate the signal transduction pathways, which greatly facilitates exploring novel or unexpected interactions.     

Evolution of enzyme superfamilies

Enzyme evolution is often constrained by aspects of catalysis. Sets of homologous proteins that catalyze different overall reactions but share an aspect of catalysis, such as a common partial reaction, are called mechanistically diverse superfamilies. The common mechanistic steps and structural characteristics of several of these superfamilies, including the enolase, Nudix, amidohydrolase, and haloacid dehalogenase superfamilies have been characterized. In addition, studies of mechanistically diverse superfamilies are helping to elucidate mechanisms of functional diversification, such as catalytic promiscuity. Understanding how enzyme superfamilies evolve is vital for accurate genome annotation, predicting protein functions, and protein engineering.     

Finding off‐targets,biological pathways,and target diseases for chymase inhibitors via structure‐based systems biology approach

Off‐target binding connotes the binding of a small molecule of therapeutic significance to a protein target in addition to the primary target for which it was proposed. Progressively such off‐targeting is emerging to be regular practice to reveal side effects. Chymase is an enzyme of hydrolase class that catalyzes hydrolysis of peptide bonds. A link between heart failure and chymase is ascribed, and a chymase inhibitor is in clinical phase II for treatment of heart failure. However, the underlying mechanisms of the off‐target effects of human chymase inhibitors are still unclear. Here, we develop a robust computational strategy that is applicable to any enzyme system and that allows the prediction of drug effects on biological processes. Putative off‐targets for chymase inhibitors were identified through various structural and functional similarity analyses along with molecular docking studies. Finally, literature survey was performed to incorporate these off‐targets into biological pathways and to establish links between pathways and particular adverse effects. Off‐targets of chymase inhibitors are linked to various biological pathways such as classical and lectin pathways of complement system, intrinsic and extrinsic pathways of coagulation cascade, and fibrinolytic system. Tissue kallikreins, granzyme M, neutrophil elastase, and mesotrypsin are also identified as off‐targets. These off‐targets and their associated pathways are elucidated for the effects of inflammation, cancer, hemorrhage, thrombosis, and central nervous system diseases (Alzheimer's disease). Prospectively, our approach is helpful not only to better understand the mechanisms of chymase inhibitors but also for drug repurposing exercises to find novel uses for these inhibitors. Proteins 2015; 83:1209–1224. © 2014 Wiley Periodicals, Inc.     

Mining DNA microarray data using a novel approach based on graph theory.

The recent demonstration that biochemical pathways from diverse organisms are arranged in scale-free, rather than random, systems [Jeong et al., Nature 407 (2000) 651-654], emphasizes the importance of developing methods for the identification of biochemical nexuses--the nodes within biochemical pathways that serve as the major input/output hubs, and therefore represent potentially important targets for modulation. Here we describe a bioinformatics approach that identifies candidate nexuses for biochemical pathways without requiring functional gene annotation; we also provide proof-of-principle experiments to support this technique. This approach, called Nexxus, may lead to the identification of new signal transduction pathways and targets for drug design.     

Hydroperoxide-induced DNA damage and mutations

Hydroperoxides (ROOH) are believed to play an important role in the generation of free radical damage in biology. Hydrogen peroxide (R=H) is produced by endogenous metabolic and catabolic processes in cells, while alkyl hydroperoxides (R=lipid, protein, DNA) are produced by free radical chain reactions involving molecular oxygen (autooxidation). The role of metal ions in generating DNA damage from hydroperoxides has long been recognized, and several distinct, biologically relevant mechanisms have been identified. Identification of the mechanistic pathways is important since it will largely determine the types of free radicals generated, which will largely determine the spectrum of DNA damage produced. Some mechanistic aspects of the reactions of low valent transition metal ions with ROOH and their role in mutagenesis are reviewed with a perspective on their possible role in the biological generation of DNA damage. A survey of hydroperoxide-induced mutagenesis studies is also presented. In vitro footprinting of DNA damage induced by hydroperoxides provides relevant information on sequence context dependent reactivity, and is valuable for the interpretation of mutation spectra since it represents the damage pattern prior to cellular repair. Efforts in this area are also reviewed.     

Secretory function of autophagy in innate immune cells

Eukaryotic cells utilize two main secretory pathways to transport proteins to the extracellular space. Proteins with a leader signal sequence often undergo co‐translational transport into the endoplasmic reticulum (ER), and then to the Golgi apparatus before they reach their destination. This pathway is called the conventional secretory pathway. Proteins without signal peptides can bypass this ER‐Golgi system and are secreted by a variety of mechanisms collectively called the unconventional secretory pathway. The molecular mechanisms of unconventional secretion are emerging. Autophagy is a conserved bulk degradation mechanism that regulates many intracellular functions. Recent evidence implicates autophagy in the secretory pathway. This review focuses on potential secretory roles of autophagy and how they could modulate the functions of innate immune cells that secrete a wide range of mediators in response to environmental and biological stimuli. We provide a brief overview of the secretory pathways, enumerate the potential mechanistic themes by which autophagy interacts with these pathways and describe their relevance in the context of innate immune cell function.     

Mitochondrial function,ornamentation, and immunocompetence

Understanding the mechanisms that link ornamental displays and individual condition is key to understanding the evolution and function of ornaments. Immune function is an aspect of individual quality that is often associated with the expression of ornamentation, but a general explanation for why the expression of some ornaments seems to be consistently linked to immunocompetence remains elusive. We propose that condition‐dependent ornaments may be linked to key aspects of immunocompetence through co‐dependence on mitochondrial function. Mitochondrial involvement in immune function is rarely considered outside of the biomedical literature, but the role of mitochondria as the primary energy producers of the cell and the centres of biosynthesis, the oxidative stress response, and cellular signalling place them at the hub of a variety of immune pathways. A promising new mechanistic explanation for correlations between a wide range of ornamental traits and the properties of individual quality is that mitochondrial function may be the ‘shared pathway’ responsible for links between ornament production and individual condition. Herein, we first review the role of mitochondria as both signal transducers and metabolic regulators of immune function. We then describe connections between hormonal pathways and mitochondria, with implications for both immune function and the expression of ornamentation. Finally, we explore the possibility that ornament expression may link directly to mitochondrial function. Considering condition‐dependent traits within the framework of mitochondrial function has the potential to unify central tenets within the study of sexual selection, eco‐immunology, oxidative stress ecology, stress and reproductive hormone biology, and animal physiology.     

Target network analysis of adiponectin,a multifaceted adipokine

Application of network analysis to dissect the potential molecular mechanisms of biological processes and complicated diseases has been the new trend in biology and medicine in recent years. Among which, the protein–protein interactions (PPI) networks attract interests of most researchers. Adiponectin, a cytokine secreted from adipose tissue, participates in a number of metabolic processes, including glucose regulation and fatty acid metabolism and involves in a series of complicated diseases from head to toe. Hundreds of proteins including many identified and potential drug targets have been reported to be involved in adiponectin related signaling pathways, which comprised a complicated regulation network. Therapeutic target database (TTD) provides extensive information about the known and explored therapeutic protein targets and the signaling pathway information. In this study, adiponectin associated drug targets based PPI was constructed and its topological properties were analyzed, which might provide some insight into the dissection of adiponectin action mechanisms and promote adiponectin signaling based drug target identification and drug discovery. J. Cell. Biochem. 114: 1145–1152, 2013. © 2012 Wiley Periodicals, Inc.     

Trends in application of advancing computational approaches in GPCR ligand discovery

G protein-coupled receptors (GPCRs) comprise the most important superfamily of protein targets in current ligand discovery and drug development. GPCRs are integral membrane proteins that play key roles in various cellular signaling processes. Therefore, GPCR signaling pathways are closely associated with numerous diseases, including cancer and several neurological, immunological, and hematological disorders. Computer-aided drug design (CADD) can expedite the process of GPCR drug discovery and potentially reduce the actual cost of research and development. Increasing knowledge of biological structures, as well as improvements on computer power and algorithms, have led to unprecedented use of CADD for the discovery of novel GPCR modulators. Similarly, machine learning approaches are now widely applied in various fields of drug target research. This review briefly summarizes the application of rising CADD methodologies, as well as novel machine learning techniques, in GPCR structural studies and bioligand discovery in the past few years. Recent novel computational strategies and feasible workflows are updated, and representative cases addressing challenging issues on olfactory receptors, biased agonism, and drug-induced cardiotoxic effects are highlighted to provide insights into future GPCR drug discovery.     

Detailed qualitative dynamic knowledge representation using a BioNetGen model of TLR-4 signaling and preconditioning

INTRODUCTION: Intracellular signaling/synthetic pathways are being increasingly extensively characterized. However, while these pathways can be displayed in static diagrams, in reality they exist with a degree of dynamic complexity that is responsible for heterogeneous cellular behavior. Multiple parallel pathways exist and interact concurrently, limiting the ability to integrate the various identified mechanisms into a cohesive whole. Computational methods have been suggested as a means of concatenating this knowledge to aid in the understanding of overall system dynamics. Since the eventual goal of biomedical research is the identification and development of therapeutic modalities, computational representation must have sufficient detail to facilitate this 'engineering' process. Adding to the challenge, this type of representation must occur in a perpetual state of incomplete knowledge. We present a modeling approach to address this challenge that is both detailed and qualitative. This approach is termed 'dynamic knowledge representation,' and is intended to be an integrated component of the iterative cycle of scientific discovery. METHODS: BioNetGen (BNG), a software platform for modeling intracellular signaling pathways, was used to model the toll-like receptor 4 (TLR-4) signal transduction cascade. The informational basis of the model was a series of reference papers on modulation of (TLR-4) signaling, and some specific primary research papers to aid in the characterization of specific mechanistic steps in the pathway. This model was detailed with respect to the components of the pathway represented, but qualitative with respect to the specific reaction coefficients utilized to execute the reactions. Responsiveness to simulated lipopolysaccharide (LPS) administration was measured by tumor necrosis factor (TNF) production. Simulation runs included evaluation of initial dose-dependent response to LPS administration at 10, 100, 1000 and 10,000, and a subsequent examination of preconditioning behavior with increasing LPS at 10, 100, 1000 and 10,000 and a secondary dose of LPS at 10,000 administered at approximately 27h of simulated time. Simulations of 'knockout' versions of the model allowed further examination of the interactions within the signaling cascade. RESULTS: The model demonstrated a dose-dependent TNF response curve to increasing stimulus by LPS. Preconditioning simulations demonstrated a similar dose-dependency of preconditioning doses leading to attenuation of response to subsequent LPS challenge - a 'tolerance' dynamic. These responses match dynamics reported in the literature. Furthermore, the simulated 'knockout' results suggested the existence and need for dual negative feedback control mechanisms, represented by the zinc ring-finger protein A20 and inhibitor kappa B proteins (IkappaB), in order for both effective attenuation of the initial stimulus signal and subsequent preconditioned 'tolerant' behavior. CONCLUSIONS: We present an example of detailed, qualitative dynamic knowledge representation using the TLR-4 signaling pathway, its control mechanisms and overall behavior with respect to preconditioning. The intent of this approach is to demonstrate a method of translating the extensive mechanistic knowledge being generated at the basic science level into an executable framework that can provide a means of 'conceptual model verification.' This allows for both the 'checking' of the dynamic consequences of a mechanistic hypothesis and the creation of a modular component of an overall model directed at the engineering goal of biomedical research. It is hoped that this paper will increase the use of knowledge representation and communication in this fashion, and facilitate the concatenation and integration of community-wide knowledge.     

A Structure-Based Approach for Mapping Adverse Drug Reactions to the Perturbation of Underlying Biological Pathways

Adverse drug reactions (ADR), also known as side-effects, are complex undesired physiologic phenomena observed secondary to the administration of pharmaceuticals. Several phenomena underlie the emergence of each ADR; however, a dominant factor is the drug''s ability to modulate one or more biological pathways. Understanding the biological processes behind the occurrence of ADRs would lead to the development of safer and more effective drugs. At present, no method exists to discover these ADR-pathway associations. In this paper we introduce a computational framework for identifying a subset of these associations based on the assumption that drugs capable of modulating the same pathway may induce similar ADRs. Our model exploits multiple information resources. First, we utilize a publicly available dataset pairing drugs with their observed ADRs. Second, we identify putative protein targets for each drug using the protein structure database and in-silico virtual docking. Third, we label each protein target with its known involvement in one or more biological pathways. Finally, the relationships among these information sources are mined using multiple stages of logistic-regression while controlling for over-fitting and multiple-hypothesis testing. As proof-of-concept, we examined a dataset of 506 ADRs, 730 drugs, and 830 human protein targets. Our method yielded 185 ADR-pathway associations of which 45 were selected to undergo a manual literature review. We found 32 associations to be supported by the scientific literature.     

Modulation of skeletal muscle antioxidant defense by exercise: Role of redox signaling

Contraction-induced production of reactive oxygen species has been shown to cause oxidative stress to skeletal muscle. As an adaptive response, muscle antioxidant defense systems are upregulated in response to exercise. Nuclear factor kappaB and mitogen-activated protein kinase are two major oxidative-stress-sensitive signal transduction pathways that have been shown to activate the gene expression of a number of enzymes and proteins that play important roles in maintenance of intracellular oxidant-antioxidant homeostasis. This mini-review will discuss the main mechanisms and gene targets for these signaling pathways during exercise and the biological significance of the adaptation.     

Reduction of N<Subscript>2</Subscript> by Fe<Superscript>2+</Superscript> via Homogeneous and Heterogeneous Reactions Part 2: The Role of Metal Binding in Activating N<Subscript>2</Subscript> for Reduction; a Requirement for Both Pre-biotic and Biological Mechanisms

Nitrogen reduction by ferrous iron has been suggested as an important mechanism in the formation of ammonia on pre-biotic Earth. This paper examines the effects of adsorption of ferrous iron onto a goethite (alpha-FeOOH) substrate on the thermodynamic driving force and rate of a ferrous iron-mediated reduction of N2 as compared with the homogeneous aqueous reaction. Utilizing density functional theory and Marcus Theory of proton coupled electron transfer reactions, the following two reactions were studied: Fe2+aq + N2aq + H2Oaq --> N2H* + FeOH2+aq and triple bond Fe2+ads + N2aq + 2H2Oaq --> N2H* + alpha-FeOOHs + 2H+aq. Although the rates of both reactions were calculated to be approximately zero at 298 K, the model results suggest that adsorption alters the thermodynamic driving force for the reaction but has no other effect on the direct electron transfer kinetics. Given that simply altering the thermodynamic driving force will not reduce dinitrogen, we can make mechanistic connections between possible prebiotic pathways and biological N2 reduction. The key to reduction in both cases is N2 adsorption to multiple transition metal centers with competitive H2 production.