Opportunities for Web-based Drug Repositioning: Searching for Potential Antihypertensive Agents with Hypotension Adverse Events

Background

Drug repositioning refers to the process of developing new indications for existing drugs. As a phenotypic indicator of drug response in humans, clinical side effects may provide straightforward signals and unique opportunities for drug repositioning.

Objective

We aimed to identify drugs frequently associated with hypotension adverse reactions (ie, the opposite condition of hypertension), which could be potential candidates as antihypertensive agents.

Methods

We systematically searched the electronic records of the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) through the openFDA platform to assess the association between hypotension incidence and antihypertensive therapeutic effect regarding a list of 683 drugs.

Results

Statistical analysis of FAERS data demonstrated that those drugs frequently co-occurring with hypotension events were more likely to have antihypertensive activity. Ranked by the statistical significance of frequent hypotension reporting, the well-known antihypertensive drugs were effectively distinguished from others (with an area under the receiver operating characteristic curve > 0.80 and a normalized discounted cumulative gain of 0.77). In addition, we found a series of antihypertensive agents (particularly drugs originally developed for treating nervous system diseases) among the drugs with top significant reporting, suggesting the good potential of Web-based and data-driven drug repositioning.

Conclusions

We found several candidate agents among the hypotension-related drugs on our list that may be redirected for lowering blood pressure. More important, we showed that a pharmacovigilance system could alternatively be used to identify antihypertensive agents and sustainably create opportunities for drug repositioning.

Precision Measurement of the Helium Flux in Primary Cosmic Rays of Rigidities 1.9 GV to 3 TV with the Alpha Magnetic Spectrometer on the International Space Station

Knowledge of the precise rigidity dependence of the helium flux is important in understanding the origin, acceleration, and propagation of cosmic rays. A precise measurement of the helium flux in primary cosmic rays with rigidity (momentum/charge) from 1.9 GV to 3 TV based on 50 million events is presented and compared to the proton flux. The detailed variation with rigidity of the helium flux spectral index is presented for the first time. The spectral index progressively hardens at rigidities larger than 100 GV. The rigidity dependence of the helium flux spectral index is similar to that of the proton spectral index though the magnitudes are different. Remarkably, the spectral index of the proton to helium flux ratio increases with rigidity up to 45 GV and then becomes constant; the flux ratio above 45 GV is well described by a single power law.

Precision Measurement of the Proton Flux in Primary Cosmic Rays from Rigidity 1 GV to 1.8 TV with the Alpha Magnetic Spectrometer on the International Space Station

A precise measurement of the proton flux in primary cosmic rays with rigidity (momentum/charge) from 1 GV to 1.8 TV is presented based on 300 million events. Knowledge of the rigidity dependence of the proton flux is important in understanding the origin, acceleration, and propagation of cosmic rays. We present the detailed variation with rigidity of the flux spectral index for the first time. The spectral index progressively hardens at high rigidities.

Systems medicine: evolution of systems biology from bench to bedside

High-throughput experimental techniques for generating genomes, transcriptomes, proteomes, metabolomes, and interactomes have provided unprecedented opportunities to interrogate biological systems and human diseases on a global level. Systems biology integrates the mass of heterogeneous high-throughput data and predictive computational modeling to understand biological functions as system-level properties. Most human diseases are biological states caused by multiple components of perturbed pathways and regulatory networks rather than individual failing components. Systems biology not only facilitates basic biological research but also provides new avenues through which to understand human diseases, identify diagnostic biomarkers, and develop disease treatments. At the same time, systems biology seeks to assist in drug discovery, drug optimization, drug combinations, and drug repositioning by investigating the molecular mechanisms of action of drugs at a system's level. Indeed, systems biology is evolving to systems medicine as a new discipline that aims to offer new approaches for addressing the diagnosis and treatment of major human diseases uniquely, effectively, and with personalized precision.

Cytochrome P450 2E1 is responsible for the initiation of 1,2-dichloropropane-induced liver damage

1,2-Dichloropropane (1,2-DCP), a solvent, which is the main component of the cleaner used in the offset printing companies in Japan, is suspected to be the causative agent of bile duct cancer, which has been recently reported at high incidence in those offset printing workplaces. While there are some reports about the acute toxicity of 1,2-DCP, no information about its metabolism related to toxicity in animals is available. As part of our efforts toward clarifying the role of 1,2-DCP in the development of cancer, we studied the metabolic pathways and the hepatotoxic effect of 1,2-DCP in mice with or without cytochrome P450 2E1 (CYP2E1) activity. In an in vitro reaction system containing liver homogenate, 1,2-DCP was only metabolized by liver tissue of wild-type mice but not by that of cyp2e1-null mice. Furthermore, the kinetics of the solvent in mice revealed a great difference between the two genotypes; 1,2-DCP administration resulted in dose-dependent hepatic damage, as shown biochemically and pathologically, but this effect was only observed in wild-type mice. The nuclear factor κB p52 pathway was involved in the liver response to 1,2-DCP. Our results clearly indicate that the oxidative metabolism of 1,2-DCP in mice is exclusively catalyzed by CYP2E1, and this step is indispensable for the manifestation of the hepatotoxic effect of the solvent.

Contemporary smoking habits among nurses in Mainland China

Although China is the world's largest consumer of tobacco and tobacco-related products, the epidemiology of smoking has not been well studied among nurses. Given this serious gap in the literature, we considered it necessary to investigate tobacco smoking habits among a large cross-section of contemporary Chinese nurses, by means of a questionnaire survey. A total of 509 replies were obtained from 520 nurses (response rate: 97.9%). The overall prevalence of smoking was 2.6% (95%CI 1.5 - 4.3). When stratified by gender, the prevalence rate among male nurses was 52.2% (33.0 - 70.8). Of those who smoked, the median number was 11 smokes per day for a period of 25.0 years. When categorized by severity, 15.4% were light smokers, 69.2% moderate smokers and 15.4% heavy smokers. When stratified by age there were no smokers under 25 years, with the prevalence between 25 and 34 years similarly low, at 1.1%. The highest smoking rate was seen among nurses aged 45 to 50 years (10.1%), even though they only comprised 9.8% of the total workforce. Although our study suggests that tobacco usage is relatively uncommon among Chinese nurses overall, the rate among male nurses was alarmingly high. The distribution of smoking by age was not uniform however, with a high proportion being concentrated in the older age ranges. As such, future preventive measures will need to consider the individual situation of Chinese nurses who smoke, particularly those who occupy the older age groups.

Precision Measurement of the (e^{+}+e^{-}) Flux in Primary Cosmic Rays from 0.5 GeV to 1 TeV with the Alpha Magnetic Spectrometer on the International Space Station

We present a measurement of the cosmic ray (e^{+}+e^{-}) flux in the range 0.5 GeV to 1 TeV based on the analysis of 10.6 million (e^{+}+e^{-}) events collected by AMS. The statistics and the resolution of AMS provide a precision measurement of the flux. The flux is smooth and reveals new and distinct information. Above 30.2 GeV, the flux can be described by a single power law with a spectral index γ=-3.170±0.008(stat+syst)±0.008(energy scale).

Network-based association of hypoxia-responsive genes with cardiovascular diseases

Molecular oxygen is indispensable for cellular viability and function. Hypoxia is a stress condition in which oxygen demand exceeds supply. Low cellular oxygen content induces a number of molecular changes to activate regulatory pathways responsible for increasing the oxygen supply and optimizing cellular metabolism under limited oxygen conditions. Hypoxia plays critical roles in the pathobiology of many diseases, such as cancer, heart failure, myocardial ischemia, stroke, and chronic lung diseases. Although the complicated associations between hypoxia and cardiovascular (and cerebrovascular) diseases (CVD) have been recognized for some time, there are few studies that investigate their biological link from a systems biology perspective. In this study, we integrate hypoxia genes, CVD genes, and the human protein interactome in order to explore the relationship between hypoxia and cardiovascular diseases at a systems level. We show that hypoxia genes are much closer to CVD genes in the human protein interactome than that expected by chance. We also find that hypoxia genes play significant bridging roles in connecting different cardiovascular diseases. We construct an hypoxia-CVD bipartite network and find several interesting hypoxia-CVD modules with significant Gene Ontology (GO) similarity. Finally, we show that hypoxia genes tend to have more CVD interactors in the human interactome than in random networks of matching topology. Based on these observations, we can predict novel genes that may be associated with CVD. This network-based association study gives us a broad view of the relationships between hypoxia and cardiovascular diseases and provides new insights into the role of hypoxia in cardiovascular biology.

Electron and positron fluxes in primary cosmic rays measured with the alpha magnetic spectrometer on the international space station

Precision measurements by the Alpha Magnetic Spectrometer on the International Space Station of the primary cosmic-ray electron flux in the range 0.5 to 700 GeV and the positron flux in the range 0.5 to 500 GeV are presented. The electron flux and the positron flux each require a description beyond a single power-law spectrum. Both the electron flux and the positron flux change their behavior at ∼30  GeV but the fluxes are significantly different in their magnitude and energy dependence. Between 20 and 200 GeV the positron spectral index is significantly harder than the electron spectral index. The determination of the differing behavior of the spectral indices versus energy is a new observation and provides important information on the origins of cosmic-ray electrons and positrons.

High statistics measurement of the positron fraction in primary cosmic rays of 0.5-500 GeV with the alpha magnetic spectrometer on the international space station

A precision measurement by AMS of the positron fraction in primary cosmic rays in the energy range from 0.5 to 500 GeV based on 10.9 million positron and electron events is presented. This measurement extends the energy range of our previous observation and increases its precision. The new results show, for the first time, that above ∼200  GeV the positron fraction no longer exhibits an increase with energy.

Assessment of the genotoxicity of 1,2-dichloropropane and dichloromethane after individual and co-exposure by inhalation in mice

OBJECTIVE

Occurrence of cholangiocarcinoma was recently reported at a high incidence rate among the employees working for an offset printing company in Osaka, Japan. 1,2-Dichloropropane (1,2-DCP) and dichloromethane (DCM) are suspected to be the causes of the cancer, as they had been used as ink cleaners in large amounts. However, it is not clear whether these chlorinated organic solvents played a role in the occurrence of cholangiocarcinoma or why the incidence rate is so high among the workers in this industry. To provide possible evidence for this severe occupational problem, we investigated the genotoxic effects of 1,2-DCP and DCM.

METHODS

Male B6C3F1 and gpt Delta C57BL/6J mice were exposed by inhalation to the individual solvents or both solvents at multiple concentrations including the levels that were possibly present in the workplaces. The genotoxicity was analyzed by Pig-a gene mutation and micronuclei assays in peripheral blood and gpt mutation and comet assays in the livers of mice after repeated inhalation of 1,2-DCP or/and DCM.

RESULTS

The Pig-a mutant frequencies and micronuclei incidences were not significantly increased by exposure of either 1,2-DCP or/and DCM at any concentration, suggesting there was no genotoxic potential in bone marrow for both solvents. In the liver, DNA damage, as measured by the comet assay, was dose dependently increased by 1,2-DCP but not by DCM. The gpt mutant frequency was 2.6-fold that of the controls in the co-exposure group.

CONCLUSIONS

These results indicate that 1,2-DCP showed stronger genotoxicity in the liver and that the genotoxic effects were greatly enhanced by simultaneous exposure to DCM.

Discovering link communities in complex networks by an integer programming model and a genetic algorithm

Identification of communities in complex networks is an important topic and issue in many fields such as sociology, biology, and computer science. Communities are often defined as groups of related nodes or links that correspond to functional subunits in the corresponding complex systems. While most conventional approaches have focused on discovering communities of nodes, some recent studies start partitioning links to find overlapping communities straightforwardly. In this paper, we propose a new quantity function for link community identification in complex networks. Based on this quantity function we formulate the link community partition problem into an integer programming model which allows us to partition a complex network into overlapping communities. We further propose a genetic algorithm for link community detection which can partition a network into overlapping communities without knowing the number of communities. We test our model and algorithm on both artificial networks and real-world networks. The results demonstrate that the model and algorithm are efficient in detecting overlapping community structure in complex networks.

Abscisic acid-responsive guard cell metabolomes of Arabidopsis wild-type and gpa1 G-protein mutants

Individual metabolites have been implicated in abscisic acid (ABA) signaling in guard cells, but a metabolite profile of this specialized cell type is lacking. We used liquid chromatography-multiple reaction monitoring mass spectrometry for targeted analysis of 85 signaling-related metabolites in Arabidopsis thaliana guard cell protoplasts over a time course of ABA treatment. The analysis utilized ∼ 350 million guard cell protoplasts from ∼ 30,000 plants of the Arabidopsis Columbia accession (Col) wild type and the heterotrimeric G-protein α subunit mutant, gpa1, which has ABA-hyposensitive stomata. These metabolomes revealed coordinated regulation of signaling metabolites in unrelated biochemical pathways. Metabolites clustered into different temporal modules in Col versus gpa1, with fewer metabolites showing ABA-altered profiles in gpa1. Ca(2+)-mobilizing agents sphingosine-1-phosphate and cyclic adenosine diphosphate ribose exhibited weaker ABA-stimulated increases in gpa1. Hormone metabolites were responsive to ABA, with generally greater responsiveness in Col than in gpa1. Most hormones also showed different ABA responses in guard cell versus mesophyll cell metabolomes. These findings suggest that ABA functions upstream to regulate other hormones, and are also consistent with G proteins modulating multiple hormonal signaling pathways. In particular, indole-3-acetic acid levels declined after ABA treatment in Col but not gpa1 guard cells. Consistent with this observation, the auxin antagonist α-(phenyl ethyl-2-one)-indole-3-acetic acid enhanced ABA-regulated stomatal movement and restored partial ABA sensitivity to gpa1.

First result from the Alpha Magnetic Spectrometer on the International Space Station: precision measurement of the positron fraction in primary cosmic rays of 0.5-350 GeV

A precision measurement by the Alpha Magnetic Spectrometer on the International Space Station of the positron fraction in primary cosmic rays in the energy range from 0.5 to 350 GeV based on 6.8 × 10(6) positron and electron events is presented. The very accurate data show that the positron fraction is steadily increasing from 10 to ∼ 250  GeV, but, from 20 to 250 GeV, the slope decreases by an order of magnitude. The positron fraction spectrum shows no fine structure, and the positron to electron ratio shows no observable anisotropy. Together, these features show the existence of new physical phenomena.

Effects of community structure on the dynamics of random threshold networks

Random threshold networks (RTNs) have been widely used as models of neural or genetic regulatory networks. Network topology plays a central role in the dynamics of these networks. Recently it has been shown that many social and biological networks are scale-free and also exhibit community structure, in which autonomous modules are wired together to perform relatively independent functions. In this study we use both synchronous and asynchronous models of RTNs to systematically investigate how community structure affects the dynamics of RTNs with scale-free topology. Extensive simulation experiments show that RTNs with high modularity have more attractors than those RTNs with low modularity, and RTNs with smaller communities tend to have more attractors. Damage resulting from perturbation of initial conditions spreads less effectively in RTNs with higher modularity and RTNs with smaller communities. In addition, RTNs with high modularity can coordinate their internal dynamics better than RTNs with low modularity under the synchronous update scheme, and it is the other way around under the asynchronous update. This study shows that community structure has a strong effect on the dynamics of RTNs.

Survey on tetracycline resistance and antibiotic-resistant genotype of avian Escherichia coli in North China

The experiment was performed to investigate the tetracycline resistance and antibiotic-resistant genotype of avian Escherichia coli in North China and to analyze the correlation of genotype and phenotype. The resistance of 164 E. coli isolates (from Beijing, Tianjin, inner Mongolia, Shanxi, and Hebei regions of China) to tetracycline, doxycycline, and minocycline was investigated by using a drug susceptibility test. The results show that the rate of resistance to tetracycline antibiotics was 89.63% (147/164). The higher resistance rate was 84.76% (139/164) to tetracycline and 70.12% (115/164) to doxycycline, and the lowest resistance rate was 4.88% (8/164) to minocycline. The distribution of tetracycline resistance (Tcr) genes (tetA, tetB, tetC, and tetM) in avian E. coli isolates was detected by PCR. Of the isolates, 82.32% (135/164) carried tetracycline resistance genes. The positive rates of tetA, tetB, and tetM were 57.93% (95/164), 38.41% (63/164), and 10.97% (18/164), respectively. No tetC was amplified in avian E. coli isolates. The total positive rate of resistance genes (82.32%) was almost equal to the total rate of resistance to tetracycline antibiotics (89.63%). Thus, the positive rate of genotype was basically in line with that of phenotype for tetracycline resistance. The tetracycline resistance genes are widely distributed in E. coli and their main resistance mechanism to tetracycline is the active efflux effect mediated by tetA and tetB.

Inferring domain-domain interactions from protein-protein interactions in the complex network conformation

BACKGROUND

As protein domains are functional and structural units of proteins, a large proportion of protein-protein interactions (PPIs) are achieved by domain-domain interactions (DDIs), many computational efforts have been made to identify DDIs from experimental PPIs since high throughput technologies have produced a large number of PPIs for different species. These methods can be separated into two categories: deterministic and probabilistic. In deterministic methods, parsimony assumption has been utilized. Parsimony principle has been widely used in computational biology as the evolution of the nature is considered as a continuous optimization process. In the context of identifying DDIs, parsimony methods try to find a minimal set of DDIs that can explain the observed PPIs. This category of methods are promising since they can be formulated and solved easily. Besides, researches have shown that they can detect specific DDIs, which is often hard for many probabilistic methods. We notice that existing methods just view PPI networks as simply assembled by single interactions, but there is now ample evidence that PPI networks should be considered in a global (systematic) point of view for it exhibits general properties of complex networks, such as 'scale-free' and 'small-world'.

RESULTS

In this work, we integrate this global point of view into the parsimony-based model. Particularly, prior knowledge is extracted from these global properties by plausible reasoning and then taken as input. We investigate the role of the added information extensively through numerical experiments. Results show that the proposed method has improved performance, which confirms the biological meanings of the extracted prior knowledge.

CONCLUSIONS

This work provides us some clues for using these properties of complex networks in computational models and to some extent reveals the biological meanings underlying these general network properties.

Differential genotoxic effects of subchronic exposure to ethyl tertiary butyl ether in the livers of Aldh2 knockout and wild-type mice

Ethyl tertiary butyl ether (ETBE) is used as an additive to gasoline to reduce carbon monoxide emissions in some developed countries. So far, ETBE was not found with positive results in many genotoxic assays. This study is undertaken to investigate the modifying effects of deficiency of aldehyde dehydrogenase 2 (ALDH2) on the toxicity of ETBE in the livers of mice. Eight-week-old wild-type (WT) and Aldh2 knockout (KO) C57BL/6 mice of both sexes were exposed to 0, 500, 1,750, and 5,000 ppm ETBE for 6 h/day with 5 days per weeks for 13 weeks. Histopathology assessments and measurements of genetic effects in the livers were performed. Significantly increased accidences of centrilobular hypertrophy were observed in the livers of WT and KO mice of both sexes in 5,000 ppm group; there was a sex difference in centrilobular hypertrophy between male and female KO mice, with more severe damage in the males. In addition, DNA strand breaks, 8-hydroxyguanine DNA-glycosylase (hOGG1)-modified oxidative base modification, and 8-hydroxydeoxyguanosine as genetic damage endpoints were significantly increased in three exposure groups in KO male mice, while these genotoxic effects were only found in 5,000 ppm group of KO female mice. In WT mice, significant DNA damage was seen in 5,000 ppm group of male mice, but not in females. Thus, sex differences in DNA damage were found not only in KO mice, but also in WT mice. These results suggest that ALDH2 polymorphisms and sex should be taken into considerations in predicting human health effects of ETBE exposure.

Dynamical and structural analysis of a T cell survival network identifies novel candidate therapeutic targets for large granular lymphocyte leukemia

The blood cancer T cell large granular lymphocyte (T-LGL) leukemia is a chronic disease characterized by a clonal proliferation of cytotoxic T cells. As no curative therapy is yet known for this disease, identification of potential therapeutic targets is of immense importance. In this paper, we perform a comprehensive dynamical and structural analysis of a network model of this disease. By employing a network reduction technique, we identify the stationary states (fixed points) of the system, representing normal and diseased (T-LGL) behavior, and analyze their precursor states (basins of attraction) using an asynchronous Boolean dynamic framework. This analysis identifies the T-LGL states of 54 components of the network, out of which 36 (67%) are corroborated by previous experimental evidence and the rest are novel predictions. We further test and validate one of these newly identified states experimentally. Specifically, we verify the prediction that the node SMAD is over-active in leukemic T-LGL by demonstrating the predominant phosphorylation of the SMAD family members Smad2 and Smad3. Our systematic perturbation analysis using dynamical and structural methods leads to the identification of 19 potential therapeutic targets, 68% of which are corroborated by experimental evidence. The novel therapeutic targets provide valuable guidance for wet-bench experiments. In addition, we successfully identify two new candidates for engineering long-lived T cells necessary for the delivery of virus and cancer vaccines. Overall, this study provides a bird's-eye-view of the avenues available for identification of therapeutic targets for similar diseases through perturbation of the underlying signal transduction network.

T-LGL leukemia is a blood cancer characterized by an abnormal increase in the abundance of a type of white blood cell called T cell. Since there is no known curative therapy for this disease, identification of potential therapeutic targets is of utmost importance. Experimental identification of manipulations capable of reversing the disease condition is usually a long, arduous process. Mathematical modeling can aid this process by identifying potential therapeutic interventions. In this work, we carry out a systematic analysis of a network model of T cell survival in T-LGL leukemia to get a deeper insight into the unknown facets of the disease. We identify the T-LGL status of 54 components of the system, out of which 36 (67%) are corroborated by previous experimental evidence and the rest are novel predictions, one of which we validate by follow-up experiments. By deciphering the structure and dynamics of the underlying network, we identify component perturbations that lead to programmed cell death, thereby suggesting several novel candidate therapeutic targets for future experiments.

Two-stage flux balance analysis of metabolic networks for drug target identification

Background

Efficient identification of drug targets is one of major challenges for drug discovery and drug development. Traditional approaches to drug target identification include literature search-based target prioritization and in vitro binding assays which are both time-consuming and labor intensive. Computational integration of different knowledge sources is a more effective alternative. Wealth of omics data generated from genomic, proteomic and metabolomic techniques changes the way researchers view drug targets and provides unprecedent opportunities for drug target identification.

Results

In this paper, we develop a method based on flux balance analysis (FBA) of metabolic networks to identify potential drug targets. This method consists of two linear programming (LP) models, which first finds the steady optimal fluxes of reactions and the mass flows of metabolites in the pathologic state and then determines the fluxes and mass flows in the medication state with the minimal side effect caused by the medication. Drug targets are identified by comparing the fluxes of reactions in both states and examining the change of reaction fluxes. We give an illustrative example to show that the drug target identification problem can be solved effectively by our method, then apply it to a hyperuricemia-related purine metabolic pathway. Known drug targets for hyperuricemia are correctly identified by our two-stage FBA method, and the side effects of these targets are also taken into account. A number of other promising drug targets are found to be both effective and safe.

Conclusions

Our method is an efficient procedure for drug target identification through flux balance analysis of large-scale metabolic networks. It can generate testable predictions, provide insights into drug action mechanisms and guide experimental design of drug discovery.

Common and unique elements of the ABA-regulated transcriptome of Arabidopsis guard cells

Background

In the presence of drought and other desiccating stresses, plants synthesize and redistribute the phytohormone abscisic acid (ABA). ABA promotes plant water conservation by acting on specialized cells in the leaf epidermis, guard cells, which border and regulate the apertures of stomatal pores through which transpirational water loss occurs. Following ABA exposure, solute uptake into guard cells is rapidly inhibited and solute loss is promoted, resulting in inhibition of stomatal opening and promotion of stomatal closure, with consequent plant water conservation. There is a wealth of information on the guard cell signaling mechanisms underlying these rapid ABA responses. To investigate ABA regulation of gene expression in guard cells in a systematic genome-wide manner, we analyzed data from global transcriptomes of guard cells generated with Affymetrix ATH1 microarrays, and compared these results to ABA regulation of gene expression in leaves and other tissues.

Results

The 1173 ABA-regulated genes of guard cells identified by our study share significant overlap with ABA-regulated genes of other tissues, and are associated with well-defined ABA-related promoter motifs such as ABREs and DREs. However, we also computationally identified a unique cis-acting motif, GTCGG, associated with ABA-induction of gene expression specifically in guard cells. In addition, approximately 300 genes showing ABA-regulation unique to this cell type were newly uncovered by our study. Within the ABA-regulated gene set of guard cells, we found that many of the genes known to encode ion transporters associated with stomatal opening are down-regulated by ABA, providing one mechanism for long-term maintenance of stomatal closure during drought. We also found examples of both negative and positive feedback in the transcriptional regulation by ABA of known ABA-signaling genes, particularly with regard to the PYR/PYL/RCAR class of soluble ABA receptors and their downstream targets, the type 2C protein phosphatases. Our data also provide evidence for cross-talk at the transcriptional level between ABA and another hormonal inhibitor of stomatal opening, methyl jasmonate.

Conclusions

Our results engender new insights into the basic cell biology of guard cells, reveal common and unique elements of ABA-regulation of gene expression in guard cells, and set the stage for targeted biotechnological manipulations to improve plant water use efficiency.

Elementary signaling modes predict the essentiality of signal transduction network components

Background

Understanding how signals propagate through signaling pathways and networks is a central goal in systems biology. Quantitative dynamic models help to achieve this understanding, but are difficult to construct and validate because of the scarcity of known mechanistic details and kinetic parameters. Structural and qualitative analysis is emerging as a feasible and useful alternative for interpreting signal transduction.

Results

In this work, we present an integrative computational method for evaluating the essentiality of components in signaling networks. This approach expands an existing signaling network to a richer representation that incorporates the positive or negative nature of interactions and the synergistic behaviors among multiple components. Our method simulates both knockout and constitutive activation of components as node disruptions, and takes into account the possible cascading effects of a node's disruption. We introduce the concept of elementary signaling mode (ESM), as the minimal set of nodes that can perform signal transduction independently. Our method ranks the importance of signaling components by the effects of their perturbation on the ESMs of the network. Validation on several signaling networks describing the immune response of mammals to bacteria, guard cell abscisic acid signaling in plants, and T cell receptor signaling shows that this method can effectively uncover the essentiality of components mediating a signal transduction process and results in strong agreement with the results of Boolean (logical) dynamic models and experimental observations.

Conclusions

This integrative method is an efficient procedure for exploratory analysis of large signaling and regulatory networks where dynamic modeling or experimental tests are impractical. Its results serve as testable predictions, provide insights into signal transduction and regulatory mechanisms and can guide targeted computational or experimental follow-up studies. The source codes for the algorithms developed in this study can be found at http://www.phys.psu.edu/~ralbert/ESM.