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Genotoxicity testing is an essential component of the safety assessment paradigm required by regulatory agencies world-wide for analysis of drug candidates, and environmental and industrial chemicals. Current genotoxicity testing batteries feature a high incidence of irrelevant positive findings—particularly for in vitro chromosomal damage (CD) assays. The risk management of compounds with positive in vitro findings is a major challenge and requires complex, time consuming, and costly follow-up strategies including animal testing. Thus, regulators are urgently in need of new testing approaches to meet legislated mandates. Using machine learning, we identified a set of transcripts that responds predictably to DNA-damage in human cells that we refer to as the TGx-DDI biomarker, which was originally referred to as TGx-28.65. We proposed to use this biomarker in conjunction with current genotoxicity testing batteries to differentiate compounds with irrelevant “false” positive findings in the in vitro CD assays from true DNA damaging agents (i.e., for de-risking agents that are clastogenic in vitro but not in vivo). We validated the performance of the TGx-DDI biomarker to identify true DNA damaging agents, assessed intra- and inter- laboratory reproducibility, and cross-platform performance. Recently, to augment the application of this biomarker, we developed a high-throughput cell-based genotoxicity testing system using the NanoString nCounter® technology. Here, we review the status of TGx-DDI development, its integration in the genotoxicity testing paradigm, and progress to date in its qualification at the US Food and Drug Administration (FDA) as a drug development tool. If successfully validated and implemented, the TGx-DDI biomarker assay is expected to significantly augment the current strategy for the assessment of genotoxic hazards for drugs and chemicals.

The use of integrated approaches in genetic toxicology, including the incorporation of gene expression data to determine the molecular pathways involved in the response, is becoming more common. In a companion article, a genomic biomarker was developed in human TK6 cells to classify chemicals as genotoxic or nongenotoxic. Because TK6 cells are not metabolically competent, we set out to broaden the utility of the biomarker for use with chemicals requiring metabolic activation. Specifically, chemical exposures were conducted in the presence of rat liver S9. The ability of the biomarker to classify genotoxic (benzo[a]pyrene, BaP; aflatoxin B1, AFB1) and nongenotoxic (dexamethasone, DEX; phenobarbital, PB) agents correctly was evaluated. Cells were exposed to increasing chemical concentrations for 4 hr and collected 0 hr, 4 hr, and 20 hr postexposure. Relative survival, apoptosis, and micronucleus frequency were measured at 24 hr. Transcriptome profiles were measured with Agilent microarrays. Statistical modeling and bioinformatics tools were applied to classify each chemical using the genomic biomarker. BaP and AFB1 were correctly classified as genotoxic at the mid‐ and high concentrations at all three time points, whereas DEX was correctly classified as nongenotoxic at all concentrations and time points. The high concentration of PB was misclassified at 24 hr, suggesting that cytotoxicity at later time points may cause misclassification. The data suggest that the use of S9 does not impair the ability of the biomarker to classify genotoxicity in TK6 cells. Finally, we demonstrate that the biomarker is also able to accurately classify genotoxicity using a publicly available dataset derived from human HepaRG cells. Environ. Mol. Mutagen. 56:520–534, 2015. © 2015 The Authors. Environmental and Molecular Mutagenesis Published by Wiley Periodicals, Inc.

The development of in vitro molecular biomarkers to accurately predict toxicological effects has become a priority to advance testing strategies for human health risk assessment. The application of in vitro transcriptomic biomarkers promises increased throughput as well as a reduction in animal use. However, the existing protocols for predictive transcriptional signatures do not establish appropriate guidelines for dose selection or account for the fact that toxic agents may have pleiotropic effects. Therefore, comparison of transcriptome profiles across agents and studies has been difficult. Here we present a dataset of transcriptional profiles for TK6 cells exposed to a battery of well-characterized genotoxic and nongenotoxic chemicals. The experimental conditions applied a new dose optimization protocol that was based on evaluating expression changes in several well-characterized stress-response genes using quantitative real-time PCR in preliminary dose-finding studies. The subsequent microarray-based transcriptomic analyses at the optimized dose revealed responses to the test chemicals that were typically complex, often exhibiting substantial overlap in the transcriptional responses between a variety of the agents making analysis challenging. Using the nearest shrunken centroids method we identified a panel of 65 genes that could accurately classify toxicants as genotoxic or nongenotoxic. To validate the 65-gene panel as a genomic biomarker of genotoxicity, the gene expression profiles of an additional three well-characterized model agents were analyzed and a case study demonstrating the practical application of this genomic biomarker-based approach in risk assessment was performed to demonstrate its utility in genotoxicity risk assessment.

Significance Standard in vitro assays to assess genotoxicity frequently generate positive results that are subsequently found to be irrelevant for in vivo carcinogenesis and human cancer risk assessment. Currently used follow-up methods, such as animal testing, are expensive and time-consuming, and the development of approaches enabling more accurate mechanism-based risk assessment is essential. We developed an in vitro transcriptomic biomarker-based approach that provides a robust biomarker reflecting stress-signaling responses. The biomarker correctly identifies the vast majority of irrelevant genotoxicity results from in vitro chromosome damage assays. TGx-DDI, a multigene biomarker for DNA damage-inducing agents, is the first biomarker that not only shows convincing interlaboratory and intralaboratory reproducibility, but also performs accurately in a system suitable for high-throughput screening., Interpretation of positive genotoxicity findings using the current in vitro testing battery is a major challenge to industry and regulatory agencies. These tests, especially mammalian cell assays, have high sensitivity but suffer from low specificity, leading to high rates of irrelevant positive findings (i.e., positive results in vitro that are not relevant to human cancer hazard). We developed an in vitro transcriptomic biomarker-based approach that provides biological relevance to positive genotoxicity assay data, particularly for in vitro chromosome damage assays, and propose its application for assessing the relevance of the in vitro positive results to carcinogenic hazard. The transcriptomic biomarker TGx-DDI (previously known as TGx-28.65) readily distinguishes DNA damage-inducing (DDI) agents from non-DDI agents. In this study, we demonstrated the ability of the biomarker to classify 45 test agents across a broad set of chemical classes as DDI or non-DDI. Furthermore, we assessed the biomarker’s utility in derisking known irrelevant positive agents and evaluated its performance across analytical platforms. We correctly classified 90% (9 of 10) of chemicals with irrelevant positive findings in in vitro chromosome damage assays as negative. We developed a standardized experimental and analytical protocol for our transcriptomics biomarker, as well as an enhanced application of TGx-DDI for high-throughput cell-based genotoxicity testing using nCounter technology. This biomarker can be integrated in genetic hazard assessment as a follow-up to positive chromosome damage findings. In addition, we propose how it might be used in chemical screening and assessment. This approach offers an opportunity to significantly improve risk assessment and reduce cost.

Motivation: Genome-scale metabolic models have been used extensively to investigate alterations in cellular metabolism. The accuracy of these models to represent cellular metabolism in specific conditions has been improved by constraining the model with omics data sources. However, few practical methods for integrating metabolomics data with other omics data sources into genome-scale models of metabolism have been developed. Results: GIM3E (Gene Inactivation Moderated by Metabolism, Metabolomics and Expression) is an algorithm that enables the development of condition-specific models based on an objective function, transcriptomics and cellular metabolomics data. GIM3E establishes metabolite use requirements with metabolomics data, uses model-paired transcriptomics data to find experimentally supported solutions and provides calculations of the turnover (production/consumption) flux of metabolites. GIM3E was used to investigate the effects of integrating additional omics datasets to create increasingly constrained solution spaces of Salmonella Typhimurium metabolism during growth in both rich and virulence media. This integration proved to be informative and resulted in a requirement of additional active reactions (12 in each case) or metabolites (26 or 29, respectively). The addition of constraints from transcriptomics also impacted the allowed solution space, and the cellular metabolites with turnover fluxes that were necessarily altered by the change in conditions increased from 118 to 271 of 1397. Availability: GIM3E has been implemented in Python and requires a COBRApy 0.2.x. The algorithm and sample data described here are freely available at: http://opencobra.sourceforge.net/ Contacts: brianjamesschmidt@gmail.com or hyduke@usu.edu Supplementary information: Supplementary information is available at Bioinformatics online.

Over the past decade a massive amount of research has been dedicated to generating omics data to gain insight into a variety of biological phenomena, including cancer, obesity, biofuel production, and infection. Although most of these omics data are available publicly, there is a growing concern that much of these data sit in databases without being used or fully analyzed. Statistical inference methods have been widely applied to gain insight into which genes may influence the activities of others in a given omics data set, however, they do not provide information on the underlying mechanisms or whether the interactions are direct or distal. Biochemically, genetically, and genomically consistent knowledge bases are increasingly being used to extract deeper biological knowledge and understanding from these data sets than possible by inferential methods. This improvement is largely due to knowledge bases providing a validated biological context for interpreting the data.

During infection or denitrification, bacteria encounter reactive nitrogen species. Although the molecular targets of and defensive response against nitric oxide (NO) in Escherichia coli are well studied, the response elements specific to S-nitrosothiols are less clear. Previously, we employed an integrated systems biology approach to unravel the E. coli NO-response network. Here we use a similar approach to confirm that S-nitrosoglutathione (GSNO) primarily impacts the metabolic and regulatory programs of E. coli in minimal medium by reaction with homocysteine and cysteine and subsequent disruption of the methionine biosynthesis pathway. Targeting of homocysteine and cysteine results in altered regulatory activity of MetJ, MetR, and CysB, activation of the stringent response and growth inhibition. Deletion of metJ or supplementation with methionine strongly attenuated the effect of GSNO on growth and gene expression. Furthermore, GSNO inhibited the ArcAB two-component system. Consistent with the underlying nitrosative and thiol-oxidative chemistry, growth inhibition and the majority of the regulatory perturbations were dependent upon GSNO internalization by the Dpp dipeptide transporter. Contrastingly, perturbation of NsrR appeared to be a result of the submicromolar levels of NO released from GSNO and did not require GSNO internalization.

Nitric oxide (NO) is used by mammalian immune systems to counter microbial invasions and is produced by bacteria during denitrification. As a defense, microorganisms possess a complex network to cope with NO. Here we report a combined transcriptomic, chemical, and phenotypic approach to identify direct NO targets and construct the biochemical response network. In particular, network component analysis was used to identify transcription factors that are perturbed by NO. Such information was screened with potential NO reaction mechanisms and phenotypic data from genetic knockouts to identify active chemistry and direct NO targets in Escherichia coli . This approach identified the comprehensive E. coli NO response network and evinced that NO halts bacterial growth via inhibition of the branched-chain amino acid biosynthesis enzyme dihydroxyacid dehydratase. Because mammals do not synthesize branched-chain amino acids, inhibition of dihydroxyacid dehydratase may have served to foster the role of NO in the immune arsenal.

Genotoxicity testing is a critical component of chemical assessment. The use of integrated approaches in genetic toxicology, including the incorporation of gene expression data to determine the DNA damage response pathways involved in response, is becoming more common. In companion papers previously published in Environmental and Molecular Mutagenesis, Li et al. (2015) [6] developed a dose optimization protocol that was based on evaluating expression changes in several well-characterized stress-response genes using quantitative real-time PCR in human lymphoblastoid TK6 cells in culture. This optimization approach was applied to the analysis of TK6 cells exposed to one of 14 genotoxic or 14 non-genotoxic agents, with sampling 4h post-exposure. Microarray-based transcriptomic analyses were then used to develop a classifier for genotoxicity using the nearest shrunken centroids method. A panel of 65 genes was identified that could accurately classify toxicants as genotoxic or non-genotoxic. In Buick et al. (2015) [1], the utility of the biomarker for chemicals that require metabolic activation was evaluated. In this study, TK6 cells were exposed to increasing doses of four chemicals (two genotoxic that require metabolic activation and two non-genotoxic chemicals) in the presence of rat liver S9 to demonstrate that S9 does not impair the ability to classify genotoxicity using this genomic biomarker in TK6cells.

Decreased nitric oxide (NO) bioavailability is associated with a number of pathological conditions. Administration of a supplemental source of NO can counter the pathological effects arising from decreased NO bioavailability. A class of NO-nucleophile adducts that spontaneously release NO (NONOates) has been developed, and its members show promise as therapeutic sources of NO. Because the NONOates release NO spontaneously, a significant portion of the NO may be consumed by the myriad of NO reactive species present in the body. Here we develop a model to analyze the efficacy of NO delivery, by membrane-impermeable NONOates, in the resistance arterioles. Our model identifies three features of blood vessels that will enhance NONOate efficacy: 1) the amount of NO delivered to the abluminal region increases with lumen radius; 2) the presence of a flow-induced red blood cell-free zone will augment NO delivery; and 3) extravasation of the NONOate into the interstitial space will increase abluminal NO delivery. These results suggest that NONOates may be more effective in larger vessels and that NONOate efficacy can be altered by modifying permeability to the interstitial space.

Toxicogenomics is proposed to be a useful tool in human health risk assessment. However, a systematic comparison of traditional risk assessment approaches with those applying toxicogenomics has never been done. We conducted a case study to evaluate the utility of toxicogenomics in the risk assessment of benzo[a]pyrene (BaP), a well-studied carcinogen, for drinking water exposures. Our study was intended to compare methodologies, not to evaluate drinking water safety. We compared traditional (RA1), genomics-informed (RA2) and genomics-only (RA3) approaches. RA2 and RA3 applied toxicogenomics data from human cell cultures and mice exposed to BaP to determine if these data could provide insight into BaP's mode of action (MOA) and derive tissue-specific points of departure (POD). Our global gene expression analysis supported that BaP is genotoxic in mice and allowed the development of a detailed MOA. Toxicogenomics analysis in human lymphoblastoid TK6 cells demonstrated a high degree of consistency in perturbed pathways with animal tissues. Quantitatively, the PODs for traditional and transcriptional approaches were similar (liver 1.2 vs. 1.0 mg/kg-bw/day; lungs 0.8 vs. 3.7 mg/kg-bw/day; forestomach 0.5 vs. 7.4 mg/kg-bw/day). RA3, which applied toxicogenomics in the absence of apical toxicology data, demonstrates that this approach provides useful information in data-poor situations. Overall, our study supports the use of toxicogenomics as a relatively fast and cost-effective tool for hazard identification, preliminary evaluation of potential carcinogens, and carcinogenic potency, in addition to identifying current limitations and practical questions for future work.

Although irreversible reaction of NO with the oxyheme of hemoglobin (producing nitrate and methemoglobin) is extremely rapid, it has been proposed that, under normoxic conditions, NO binds preferentially to the minority deoxyheme to subsequently form S -nitrosohemoglobin (SNOHb). Thus, the primary reaction would be conservation, rather than consumption, of nitrogen oxide. Data supporting this conclusion were generated by using addition of a small volume of a concentrated aqueous solution of NO to a normoxic hemoglobin solution. Under these conditions, however, extremely rapid reactions can occur before mixing. We have thus compared bolus NO addition to NO generated homogeneously throughout solution by using NO donors, a more physiologically relevant condition. With bolus addition, multiple hemoglobin species are formed (as judged by visible spectroscopy) as well as both nitrite and nitrate. With donor, only nitrate and methemoglobin are formed, stoichiometric with the amount of NO liberated from the donor. Studies with increasing hemoglobin concentrations reveal that the nitrite-forming reaction (which may be NO autoxidation under these conditions) competes with reaction with hemoglobin. SNOHb formation is detectable with either bolus or donor; however, the amounts formed are much smaller than the amount of NO added (less than 1%). We conclude that the reaction of NO with hemoglobin under normoxic conditions results in consumption, rather than conservation, of NO.

The development of in vitro molecular biomarkers to accurately predict toxicological effects has become a priority to advance testing strategies for human health risk assessment. The application of in vitro transcriptomic biomarkers promises increased throughput as well as a reduction in animal use. However, the existing protocols for predictive transcriptional signatures do not establish appropriate guidelines for dose selection or account for the fact that toxic agents may have pleiotropic effects. Therefore, comparison of transcriptome profiles across agents and studies has been difficult. Here we present a dataset of transcriptional profiles for TK6 cells exposed to a battery of well-characterized genotoxic and non-genotoxic chemicals. The experimental conditions applied a new dose optimization protocol that was based on evaluating expression changes in several well-characterized stress-response genes using quantitative real-time PCR in preliminary dose-finding studies. The subsequent microarray-based transcriptomic analyses at the optimized dose revealed responses to the test chemicals that were typically complex, often exhibiting substantial overlap in the transcriptional responses between a variety of the agents making analysis challenging. Using the nearest shrunken centroids method we identified a panel of 65 genes that could accurately classify toxicants as genotoxic or non-genotoxic. To validate the 65-gene panel as a genomic biomarker of genotoxicity, the gene expression profiles of an additional three well-characterized model agents were analyzed and a case study demonstrating the practical application of this genomic biomarker-based approach in risk assessment was performed to demonstrate its utility in genotoxicity risk assessment.

A modified Horvath–Kawazoe (HK) pore size analysis method is presented in this paper. The new HK model is generalized so as to account for the effect of the temperature and the solid–fluid interaction potential strength on the adsorbed fluid density within a slit-shaped pore. Two representations of the local adsorbate density profile are considered in the modified HK model, an ‘unweighted’ version similar to the original HK method in which a uniform density profile is assumed; and a ‘weighted’ version in which a structured density profile is adopted. The pore filling correlations predicted by the two HK models are compared with density functional theory (DFT) results calculated using the same solid–fluid potential and potential parameters. It is found that the pore filling correlation of the unweighted HK model agrees surprisingly well with the DFT correlation for argon and nitrogen adsorption at 77 K. Interestingly, the weighted HK pore filling correlation is in poorer agreement with DFT results, even though the weighted HK model more realistically represents the local density profile than does the unweighted HK approach. As in the original HK method, the modified HK models do not describe the pore wall wetting that occurs prior to capillary condensation in mesopores. In this respect, they remain inferior to DFT or molecular simulation adsorption models. The modified HK method is, however, much more computationally efficient than either DFT or molecular simulation, and it can be conveniently implemented on a spreadsheet for interpretation of adsorbent PSDs in gas–solid systems for which DFT model isotherms are not available.

We present isotherms calculated from density functional theory for the adsorption of argon in model slit-shaped carbon pores at 77 K. The model isotherms are used to interpret experimental argon uptake measurements and to obtain the pore size distributions of several porous carbons. A similar set of density functional theory isotherms, previously reported for nitrogen adsorption on carbon slit pores at 77 K, are used to determine pore size distributions for the same set of carbons. The pore size distribution maxima, mean pore widths, and specific pore volumes measured using the two different probe gases are all found to agree to within approximately 8% on average. Some of the differences in the pore size distributions obtained from argon and nitrogen porosimetry may be attributable to quadrupolar interactions of the nitrogen molecules with functional groups on the carbon surface.

Ionising radiation is a pleiotropic stress agent that may induce a variety of adverse effects. Molecular biomarker approaches possess promise to assess radiation exposure, however, the pleiotropic nature of ionising radiation induced transcriptional responses and the historically poor inter-laboratory performance of omics-derived biomarkers serve as barriers to identification of unequivocal biomarker sets. Here, we present a whole-genome survey of the murine transcriptomic response to physiologically relevant radiation doses, 2 Gy and 8 Gy. We used this dataset with the Random Forest algorithm to correctly classify independently generated data and to identify putative metabolite biomarkers for radiation exposure.

Salmonella enterica serovar Typhimurium (S. Typhimurium) is a facultative pathogen that uses complex mechanisms to invade and proliferate within mammalian host cells. To investigate possible contributions of metabolic processes to virulence in S. Typhimurium grown under conditions known to induce expression of virulence genes, we used a metabolomics-driven systems biology approach coupled with genome-scale modeling. First, we identified distinct metabolite profiles associated with bacteria grown in either rich or virulence-inducing media and report the most comprehensive coverage of the S. Typhimurium metabolome to date. Second, we applied an omics-informed genome-scale modeling analysis of the functional consequences of adaptive alterations in S. Typhimurium metabolism during growth under our conditions. Modeling efforts highlighted a decreased cellular capability to both produce and utilize intracellular amino acids during stationary phase culture in virulence conditions, despite significant abundance increases for these molecules as observed by our metabolomics measurements. Furthermore, analyses of omics data in the context of the metabolic model indicated rewiring of the metabolic network to support pathways associated with virulence. For example, cellular concentrations of polyamines were perturbed, as well as the predicted capacity for secretion and uptake.

A constraint-based approach for integrative modeling of metabolism and gene expression is developed. New constraints on molecular catalysis increase both the accuracy and scope of computable phenotypes corresponding to optimal microbial growth., An integrated network of metabolic and gene expression pathways is built for E. coli. A growth model is developed by adding demands and constraints on molecular catalysis. Model yields accurate predictions of growth phenotypes from molecules to whole cell. A few basic principles underlie growth rate optimization at the systems level., Growth is a fundamental process of life. Growth requirements are well-characterized experimentally for many microbes; however, we lack a unified model for cellular growth. Such a model must be predictive of events at the molecular scale and capable of explaining the high-level behavior of the cell as a whole. Here, we construct an ME-Model for Escherichia coli—a genome-scale model that seamlessly integrates metabolic and gene product expression pathways. The model computes ∼80% of the functional proteome (by mass), which is used by the cell to support growth under a given condition. Metabolism and gene expression are interdependent processes that affect and constrain each other. We formalize these constraints and apply the principle of growth optimization to enable the accurate prediction of multi-scale phenotypes, ranging from coarse-grained (growth rate, nutrient uptake, by-product secretion) to fine-grained (metabolic fluxes, gene expression levels). Our results unify many existing principles developed to describe bacterial growth.

Salmonella and Yersinia are two distantly related genera containing species with wide host-range specificity and pathogenic capacity. The metabolic complexity of these organisms facilitates robust lifestyles both outside of and within animal hosts. Using a pathogen-centric systems biology approach, we are combining a multi-omics (transcriptomics, proteomics, metabolomics) strategy to define properties of these pathogens under a variety of conditions including those that mimic the environments encountered during pathogenesis. These high-dimensional omics datasets are being integrated in selected ways to improve genome annotations, discover novel virulence-related factors, and model growth under infectious states. We will review the evolving technological approaches toward understanding complex microbial life through multi-omic measurements and integration, while highlighting some of our most recent successes in this area.

Transcription and translation use raw materials and energy generated metabolically to create the macromolecular machinery responsible for all cellular functions, including metabolism. A biochemically accurate model of molecular biology and metabolism will facilitate comprehensive and quantitative computations of an organism's molecular constitution as a function of genetic and environmental parameters. Here we formulate a model of metabolism and macromolecular expression. Prototyping it using the simple microorganism Thermotoga maritima, we show our model accurately simulates variations in cellular composition and gene expression. Moreover, through in silico comparative transcriptomics, the model allows the discovery of new regulons and improving the genome and transcription unit annotations. Our method presents a framework for investigating molecular biology and cellular physiology in silico and may allow quantitative interpretation of multi-omics data sets in the context of an integrated biochemical description of an organism.

Genome-scale metabolic network reconstruction and analysis of the murine leukemic macrophage cell line RAW 264.7 reveal a complementary relationship between how known metabolic immunomodulators are biochemically processed and their role in macrophage activation., The RAW 264.7 metabolic model was constructed based on transcriptomic and proteomic data, and validated for its quantitative accuracy in the prediction of growth rate, ATP, and nitric oxide production. Metabolic network-based analyses identified well-established critical metabolite effectors and intracellular pathways that impact activation or suppression of M1- and M2-metabolic activation phenotypes. Three levels of high-throughput data (transcriptomic, proteomic, and metabolomic) were analyzed in the context of the model-based predictions to elucidate underlying metabolic mechanisms of macrophage activation. Results suggest a potential contending link between de-novo nucleotide synthesis and macrophage activation phenotypes at a glutamine junction., Macrophages are central players in immune response, manifesting divergent phenotypes to control inflammation and innate immunity through release of cytokines and other signaling factors. Recently, the focus on metabolism has been reemphasized as critical signaling and regulatory pathways of human pathophysiology, ranging from cancer to aging, often converge on metabolic responses. Here, we used genome-scale modeling and multi-omics (transcriptomics, proteomics, and metabolomics) analysis to assess metabolic features that are critical for macrophage activation. We constructed a genome-scale metabolic network for the RAW 264.7 cell line to determine metabolic modulators of activation. Metabolites well-known to be associated with immunoactivation (glucose and arginine) and immunosuppression (tryptophan and vitamin D3) were among the most critical effectors. Intracellular metabolic mechanisms were assessed, identifying a suppressive role for de-novo nucleotide synthesis. Finally, underlying metabolic mechanisms of macrophage activation are identified by analyzing multi-omic data obtained from LPS-stimulated RAW cells in the context of our flux-based predictions. Our study demonstrates metabolism's role in regulating activation may be greater than previously anticipated and elucidates underlying connections between activation and metabolic effectors.

Introduction Infections are a major cause of both human disease and loss of crop yields and animal stocks and thus cause immense damage to the worldwide economy. The significance of infectious diseases is expected to increase in an ever more connected warming world, in which new viral, bacterial and fungal pathogens can find novel hosts and ecologic niches. At the same time, the complex and sophisticated mechanisms by which diverse pathogenic agents evade defense mechanisms and subvert their hosts networks to suit their lifestyle needs is still very incompletely understood especially from a systems perspective [1]. Thus, understanding host-pathogen interactions is an important and timely topic.

The manner in which microorganisms utilize their metabolic processes can be predicted using constraint-based analysis of genome-scale metabolic networks. Herein, we present the constraint-based reconstruction and analysis toolbox, a software package running in the Matlab environment, which allows for quantitative prediction of cellular behavior using a constraint-based approach. Specifically, this software allows predictive computations of both steady-state and dynamic optimal growth behavior, the effects of gene deletions, comprehensive robustness analyses, sampling the range of possible cellular metabolic states and the determination of network modules. Functions enabling these calculations are included in the toolbox, allowing a user to input a genome-scale metabolic model distributed in Systems Biology Markup Language format and perform these calculations with just a few lines of code. The results are predictions of cellular behavior that have been verified as accurate in a growing body of research. After software installation, calculation time is minimal, allowing the user to focus on the interpretation of the computational results.

Salmonella is a primary cause of enteric diseases in a variety of animals. During its evolution into a pathogenic bacterium, Salmonella acquired an elaborate regulatory network that responds to multiple environmental stimuli within host animals and integrates them resulting in fine regulation of the virulence program. The coordinated action by this regulatory network involves numerous virulence regulators, necessitating genome-wide profiling analysis to assess and combine efforts from multiple regulons. In this review we discuss recent high-throughput analytic approaches used to understand the regulatory network of Salmonella that controls virulence processes. Application of high-throughput analyses have generated large amounts of data and necessitated the development of computational approaches for data integration. Therefore, we also cover computer-aided network analyses to infer regulatory networks, and demonstrate how genome-scale data can be used to construct regulatory and metabolic systems models of Salmonella pathogenesis. Genes that are coordinately controlled by multiple virulence regulators under infectious conditions are more likely to be important for pathogenesis. Thus, reconstructing the global regulatory network during infection or, at the very least, under conditions that mimic the host cellular environment not only provides a bird's eye view of Salmonella survival strategy in response to hostile host environments but also serves as an efficient means to identify novel virulence factors that are essential for Salmonella to accomplish systemic infection in the host.

Background Metabolic reconstructions (MRs) are common denominators in systems biology and represent biochemical, genetic, and genomic (BiGG) knowledge-bases for target organisms by capturing currently available information in a consistent, structured manner. Salmonella enterica subspecies I serovar Typhimurium is a human pathogen, causes various diseases and its increasing antibiotic resistance poses a public health problem. Results Here, we describe a community-driven effort, in which more than 20 experts in S. Typhimurium biology and systems biology collaborated to reconcile and expand the S. Typhimurium BiGG knowledge-base. The consensus MR was obtained starting from two independently developed MRs for S. Typhimurium. Key results of this reconstruction jamboree include i) development and implementation of a community-based workflow for MR annotation and reconciliation; ii) incorporation of thermodynamic information; and iii) use of the consensus MR to identify potential multi-target drug therapy approaches. Conclusion Taken together, with the growing number of parallel MRs a structured, community-driven approach will be necessary to maximize quality while increasing adoption of MRs in experimental design and interpretation.

Biological signalling networks allow living organisms to issue an integrated response to current conditions and make limited predictions about future environmental changes. Small-scale dynamic models of signalling cascades, including mitogen-activated protein kinase cascades, have been developed to generate hypotheses about signal transduction. Owing to technical limitations, these models and the hypotheses they generate have focused on a limited subset of signalling molecules. Now that we can simultaneously measure a substantial portion of the molecular components of a cell, we can begin to develop and test systems-level models of cellular signalling and regulatory processes, therefore gaining insights into the 'thought' processes of a cell.

Publisher Summary This chapter emphasizes the key stress signals in various cellular compartments and focuses on current knowledge of the sensing mechanisms. The mitochondria are a crucial center for stress detection and processing of stress signals arising in other cellular compartments. Mitochondria activate apoptosis in response to external signals or stresses detected within the mitochondrion. The p53 in the cytosol can localize to the mitochondria and instigate apoptosis. Metabolic and other stresses, such as ionizing and some types of ultraviolet (UV) radiation may increase the mitochondrial reactive nitrogen species (ROS) production rate beyond the detoxification capacity. Elevated metabolic activity increases electron leakage from complex I and subsequent ROS production. The ER is where secretory proteins are processed, a variety of posttranslational modifications are made, and it is the site of intracellular Ca 2+ storage. Stresses affecting Ca 2+ levels or the redox balance are the most notable ER stress signals that impact protein folding. Ca 2+ is essential for a number of enzymes, including some involved in protein processing. Signal transduction can occur through precise protein to protein relays, or through less specific mechanisms where the concentration of a molecule is changed and sensors respond to the change in concentration gradient. The archetypical protein based method for conveying a highly specific signal is through phosphorelays where proteins pass a phosphate through a transduction pathway. Protein modification is a major mechanism for stress signal propagation within the cell and other methods that proteins use for passing messages includes acetylation, ISGylation, nitrosylation, ROS generation, and sumolyation.

Publisher Summary This chapter describes various components of reactive nitrogen species (RNS) response networks and their associated regulatory circuitry. It focuses on how these components are used to identify additional regulatory components and elucidate RNS response networks as a function of RNS source and growth conditions. It summarizes the complementary nature of traditional biochemical analysis and systems-wide analysis in identification and characterization of the bacterial response to RNS. Bacteria have evolved complex regulatory networks for dealing with a variety of chemical stressors. The bacterial response to RNS is relevant to both pathogenesis and denitrification and thus has been extensively characterized. Traditional biochemical analysis has identified and characterized many of the key RNS response elements, such as flavohemoglobin hmpA and response regulator NorR. Additionally, systems-wide analysis aids in the identification of additional network components and determination of their contribution to the overall network behavior. These systems-wide analyses have led to the identification of response regulator NsrR, Fe-S cluster repair agent YtfE, and the critical NO target. The extensive body of knowledge regarding the bacterial RNS response and RNS chemistry makes the RNS response network an excellent example of the combined power of traditional biochemical analysis and systems-wide analysis.

DNA microarray data are affected by variations from a number of sources. Before these data can be used to infer biological information, the extent of these variations must be assessed. Here we describe an open source software package, lcDNA, that provides tools for filtering, normalizing, and assessing the statistical significance of cDNA microarray data. The program employs a hierarchical Bayesian model and Markov Chain Monte Carlo simulation to estimate gene-specific confidence intervals for each gene in a cDNA microarray data set. This program is designed to perform these primary analytical operations on data from two-channel spotted, or in situ synthesized, DNA microarrays.

The homeostasis of nitric oxide (NO) is attained through a balance between its production and consumption. Shifts in NO bioavailability have been linked to a variety of diseases. Although the regulation of NO production has been well documented, its consumption is largely thought to be unregulated. Here, we have demonstrated that under hypoxic conditions, NO accelerates its own consumption by increasing its entry into RBCs. When RBCs were exposed to NO (1:400 NO/heme ratio) under hypoxic conditions to form HbFe(II)NO, the consumption rate of NO increased significantly. This increase in NO consumption converted the bioactivity of serotonin from a vasodilator to a vasoconstrictor in isolated coronary arterioles. We identified HbFe(II)NO as a potential mediator of accelerated NO consumption. Accelerated NO consumption by HbFe(II)NO-bearing RBCs may contribute to hypoxic pulmonary vasoconstriction and the rebound effect seen on termination of NO inhalation therapy. Furthermore, accelerated NO consumption may exacerbate ischemia-mediated vasospasm and nitrate tolerance. Finally, this phenomenon may be an evolved mechanism to stabilize the vasculature in sepsis.

Understanding the interaction of nitric oxide (NO) with red blood cells (RBCs) is vital to elucidating the metabolic fate of NO in the vasculature. Because hemoglobin (Hb) is the most abundant intraerythrocytic protein and reacts rapidly with NO, the interaction of NO with Hb has been studied extensively. We and others have shown the NO reaction with RBCs is nearly 1,000-fold slower than the reaction with cell-free Hb. Because the reaction rate of NO with cell-free Hb and RBCs is quite different, we hypothesize that different reaction products evolve under locally high NO concentrations, which can be generated by bolus NO addition or NO inhalation. Here we use electron paramagnetic resonance to show that bolus NO addition to cell-free Hb solutions results in nitrosylhemoglobin [HbFe(II)NO] formation as a minor product through a MetHb-dependent pathway. Further, the RBC is shown to be more prone to form HbFe(II)NO under this heterogeneous condition compared with an equivalent free-Hb solution. In both cases, trapping MetHb with cyanide blocked the formation of HbFe(II)NO. We conclude that the formation of HbFe(II)NO is a heterogeneous phenomenon involving three successive reactions of NO with the same heme molecule. These results were supported further by mathematically modeling NO–Hb reactions and diffusion.

Nitric oxide (NO) activates soluble guanylyl cyclase in smooth muscle cells to induce vasodilation in the vasculature. However, as hemoglobin (Hb) is an effective scavenger of NO and is present in high concentrations inside the red blood cell (RBC), the bioavailability of NO would be too low to elicit soluble guanylyl cyclase activation in the presence of blood. Therefore, NO bioactivity must be preserved. Here we present evidence suggesting that the RBC participates in the preservation of NO bioactivity by reducing NO influx. The NO uptake by RBCs was increased and decreased by altering the degree of band 3 binding to the cytoskeleton. Methemoglobin and denatured hemoglobin binding to the RBC membrane or cytoskeleton also were shown to contribute to reducing the NO uptake rate of the RBC. These alterations in NO uptake by the RBC, hence the NO bioavailability, were determined to correlate with the vasodilation of isolated blood vessels. Our observations suggest that RBC membrane and cytoskeleton associated NO-inert proteins provide a barrier for NO diffusion and thus account for the reduction in the NO uptake rate of RBCs.

Breast cancer is the second most commonly diagnosed cancer in women in the USA. The mammary gland is known to be particularly sensitive to radiation injury and persistent cellular changes are known to occur after exposure to ionizing radiation (IR). While measurable effects of IR have been observed in vitro and in vivo, the longer-term consequences still have considerable uncertainty. Delayed consequences of exposure to γ radiation including tumorigensis were compared to heavy ion radiation in wild-type C57BL/6J and in APC+/min mice. Studying heavy ion radiation has implications in two distinct fields - space missions and heavy ion radiotherapy. We used 137-Cs for γ radiation and 56-Fe as heavy ion at the NASA space radiation research laboratory at Brookhaven National Laboratory. Experiments were performed as per approved protocol and 6-8 weeks female C57BL/6J mice were irradiated with 2 to 7 Gy of γ and 56-Fe radiation. APC+/min mice were irradiated at 2 and 5 Gy of γ-rays and equitoxic doses of 56-Fe radiation using an RBE (relative biological effectiveness) of 1.25. Mammary glands were harvested from WT mice at 2 and 9 months postradiation for morphological, histological, transcriptomic, and proteomic analysis. Tumorigenesis was studied in APC+/min mice at 100 days post-radiation. Mammary gland morphology in whole mount revealed a more pronounced decrease in duct density and lateral branching in the 56-Fe exposed mice. H&E stained sections of 2 Gy at 9 months were near normal than 7 Gy γ radiation. The 56-Fe glands, in contrast, were markedly different even at 9 months and showed proliferative changes in the fatty component confirmed by Ki67 staining. With γ radiation proliferative activity was more evident in the ductal tree in a dose dependent manner. In contrast to 56-Fe radiation, transcriptomic analysis at 2 months showed increased estrogen signaling with γ radiation, which correlates with the urinary estradiol level. Expression profiles at 2 or 9 month post-radiation suggests that γ-rays causes greater upregulation of gene expression. In contrast to upregulation of mostly proliferative pathways by γ radiation, 56-Fe radiation showed upregulation of major metabolic pathways. Enhanced metabolic pathways with 56-Fe are consistent with the phenotypic changes in these mice. Sustained activation of proliferative pathways by γ radiation is consistent with tumorigensis in APC+/min mice. An average 40% of the γ irradiated Min mice had mammary tumors most of which were adenomas. Only 8 % of the 56-Fe mice had mammary tumor mostly desmoid cysts. In conclusion, γ radiation although less damaging than heavy ion radiation could have more carcinogenic potential and lower radiation doses are currently investigated. We speculate that reduced tumorigensis could be due to early apoptosis and removal of 56-Fe radiation-induced complex-damages-containing cells and will be further studied. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 1323.