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Background: COnstraint-Based Reconstruction and Analysis (COBRA) methods are widely used for genome-scale modeling of metabolic networks in both prokaryotes and eukaryotes. Due to the successes with metabolism, there is an increasing effort to apply COBRA methods to reconstruct and analyze integrated models of cellular processes. The COBRA Toolbox for MATLAB is a leading software package for genome-scale analysis of metabolism; however, it was not designed to elegantly capture the complexity inherent in integrated biological networks and lacks an integration framework for the multiomics data used in systems biology. The openCOBRA Project is a community effort to promote constraints-based research through the distribution of freely available software.Results: Here, we describe COBRA for Python (COBRApy), a Python package that provides support for basic COBRA methods. COBRApy is designed in an object-oriented fashion that facilitates the representation of the complex biological processes of metabolism and gene expression. COBRApy does not require MATLAB to function; however, it includes an interface to the COBRA Toolbox for MATLAB to facilitate use of legacy codes. For improved performance, COBRApy includes parallel processing support for computationally intensive processes.Conclusion: COBRApy is an object-oriented framework designed to meet the computational challenges associated with the next generation of stoichiometric constraint-based models and high-density omics data sets. © 2013 Ebrahim et al.; licensee BioMed Central Ltd.

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., (Copyright © 2019 Li, Yauk, Chen, Hyduke, Williams, Frötschl, Ellinger-Ziegelbauer, Pettit, Aubrecht and Fornace.)

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., Competing Interests: Conflict of interest statement: Georgetown University has filed a provisional patent application for the technology described in this paper, on which A.J.F. and H.-H.L. are inventors., (Copyright © 2017 the Author(s). Published by PNAS.)

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 4 h 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.

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., (© 2015 Wiley Periodicals, Inc.)

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., (© 2015 The Authors. Environmental and Molecular Mutagenesis Published by Wiley Periodicals, Inc.)

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.

Altered metabolism in cancer cells has been viewed as a passive response required for a malignant transformation. However, this view has changed through the recently described metabolic oncogenic factors: mutated isocitrate dehydrogenases (IDH), succinate dehydrogenase (SDH), and fumarate hydratase (FH) that produce oncometabolites that competitively inhibit epigenetic regulation. In this study, we demonstrate in silico predictions of oncometabolites that have the potential to dysregulate epigenetic controls in nine types of cancer by incorporating massive scale genetic mutation information (collected from more than 1,700 cancer genomes), expression profiling data, and deploying Recon 2 to reconstruct context-specific genome-scale metabolic models. Our analysis predicted 15 compounds and 24 substructures of potential oncometabolites that could result from the loss-of-function and gain-of-function mutations of metabolic enzymes, respectively. These results suggest a substantial potential for discovering unidentified oncometabolites in various forms of cancers.

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: GIM(3)E (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. GIM(3)E 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. GIM(3)E 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: GIM(3)E 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

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 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.

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.

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.

Background: Breast tissue is among the most sensitive tissues to the carcinogenic actions of ionizing radiation and epidemiological studies have linked radiation exposure to breast cancer. Currently, molecular understanding of radiation carcinogenesis in mammary gland is hindered due to the scarcity of in vivo long-term follow up data. We undertook this study to delineate radiation-induced persistent alterations in gene expression in mouse mammary glands 2-month after radiation exposure., Methods: Six to eight week old female C57BL/6J mice were exposed to 2 Gy of whole body γ radiation and mammary glands were surgically removed 2-month after radiation. RNA was isolated and microarray hybridization performed for gene expression analysis. Ingenuity Pathway Analysis (IPA) was used for biological interpretation of microarray data. Real time quantitative PCR was performed on selected genes to confirm the microarray data., Results: Compared to untreated controls, the mRNA levels of a total of 737 genes were significantly (p<0.05) perturbed above 2-fold of control. More genes (493 genes; 67%) were upregulated than the number of downregulated genes (244 genes; 33%). Functional analysis of the upregulated genes mapped to cell proliferation and cancer related canonical pathways such as 'ERK/MAPK signaling', 'CDK5 signaling', and '14-3-3-mediated signaling'. We also observed upregulation of breast cancer related canonical pathways such as 'breast cancer regulation by Stathmin1', and 'HER-2 signaling in breast cancer' in IPA. Interestingly, the downregulated genes mapped to fewer canonical pathways involved in cell proliferation. We also observed that a number of genes with tumor suppressor function (GPRC5A, ELF1, NAB2, Sema4D, ACPP, MAP2, RUNX1) persistently remained downregulated in response to radiation exposure. Results from qRT-PCR on five selected differentially expressed genes confirmed microarray data. The PCR data on PPP4c, ELF1, MAPK12, PLCG1, and E2F6 showed similar trend in up and downregulation as has been observed with the microarray., Conclusions: Exposure to a clinically relevant radiation dose led to long-term activation of mammary gland genes involved in proliferative and metabolic pathways, which are known to have roles in carcinogenesis. When considered along with downregulation of a number of tumor suppressor genes, our study has implications for breast cancer initiation and progression after therapeutic radiation exposure.

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.

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.

Metabolomics on easily accessible biofluids has the potential to provide rapid identification and distinction between stressors and inflammatory states. In the event of a radiological event, individuals with underlying medical conditions could present with similar symptoms to radiation poisoning, prominently nausea, diarrhea, vomiting and fever. Metabolomics of radiation exposure in mice has provided valuable biomarkers, and in this study we aimed to identify biomarkers of lipopolysaccharide (LPS) exposure to compare and contrast with ionizing radiation. LPS treatment leads to a severe inflammatory response and a cytokine storm, events similar to radiation exposure, and LPS exposure can recapitulate many of the responses seen in sepsis. Urine from control mice, LPS-treated mice, and mice irradiated with 3, 8 and 15 Gy of γ rays was analyzed by LCMS, and markers were extracted using SIMCA-P(+) and Random Forests. Markers were validated through tandem mass spectrometry against pure chemicals. Five metabolites, cytosine, cortisol, adenine, O-propanoylcarnitine and isethionic acid, showed increased excretion at 24 h after LPS treatment (P < 0.0001, 0.0393, 0.0393, <0.0001 and 0.0004, respectively). Of these, cytosine, adenine and O-propanoylcarnitine showed specificity to LPS treatment when compared to radiation. On the other hand, increased excretion of cortisol after LPS and radiation treatments indicated a rapid systemic response to inflammatory agents. Isethionic acid excretion, however, showed elevated levels not only after LPS treatment but also after a very high dose of radiation (15 Gy), while additional metabolites showed responsiveness to radiation but not LPS. Metabolomics therefore has the potential to distinguish between different inflammatory responses based on differential ion signatures. It can also provide quick and reliable assessment of medical conditions in a mass casualty radiological scenario and aid in effective triaging.

Background: Yersinia pestis is a gram-negative bacterium that causes plague, a disease linked historically to the Black Death in Europe during the Middle Ages and to several outbreaks during the modern era. Metabolism in Y. pestis displays remarkable flexibility and robustness, allowing the bacterium to proliferate in both warm-blooded mammalian hosts and cold-blooded insect vectors such as fleas., Results: Here we report a genome-scale reconstruction and mathematical model of metabolism for Y. pestis CO92 and supporting experimental growth and metabolite measurements. The model contains 815 genes, 678 proteins, 963 unique metabolites and 1678 reactions, accurately simulates growth on a range of carbon sources both qualitatively and quantitatively, and identifies gaps in several key biosynthetic pathways and suggests how those gaps might be filled. Furthermore, our model presents hypotheses to explain certain known nutritional requirements characteristic of this strain., Conclusions: Y. pestis continues to be a dangerous threat to human health during modern times. The Y. pestis genome-scale metabolic reconstruction presented here, which has been benchmarked against experimental data and correctly reproduces known phenotypes, provides an in silico platform with which to investigate the metabolism of this important human pathogen.

Over the past decade, a growing community of researchers has emerged around the use of constraint-based reconstruction and analysis (COBRA) methods to simulate, analyze and predict a variety of metabolic phenotypes using genome-scale models. The COBRA Toolbox, a MATLAB package for implementing COBRA methods, was presented earlier. Here we present a substantial update of this in silico toolbox. Version 2.0 of the COBRA Toolbox expands the scope of computations by including in silico analysis methods developed since its original release. New functions include (i) network gap filling, (ii) (13)C analysis, (iii) metabolic engineering, (iv) omics-guided analysis and (v) visualization. As with the first version, the COBRA Toolbox reads and writes systems biology markup language-formatted models. In version 2.0, we improved performance, usability and the level of documentation. A suite of test scripts can now be used to learn the core functionality of the toolbox and validate results. This toolbox lowers the barrier of entry to use powerful COBRA methods.

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.

Background: Network Component Analysis (NCA) has been used to deduce the activities of transcription factors (TFs) from gene expression data and the TF-gene binding relationship. However, the TF-gene interaction varies in different environmental conditions and tissues, but such information is rarely available and cannot be predicted simply by motif analysis. Thus, it is beneficial to identify key TF-gene interactions under the experimental condition based on transcriptome data. Such information would be useful in identifying key regulatory pathways and gene markers of TFs in further studies., Results: We developed an algorithm to trim network connectivity such that the important regulatory interactions between the TFs and the genes were retained and the regulatory signals were deduced. Theoretical studies demonstrated that the regulatory signals were accurately reconstructed even in the case where only three independent transcriptome datasets were available. At least 80% of the main target genes were correctly predicted in the extreme condition of high noise level and small number of datasets. Our algorithm was tested with transcriptome data taken from mice under rapamycin treatment. The initial network topology from the literature contains 70 TFs, 778 genes, and 1423 edges between the TFs and genes. Our method retained 1074 edges (i.e. 75% of the original edge number) and identified 17 TFs as being significantly perturbed under the experimental condition. Twelve of these TFs are involved in MAPK signaling or myeloid leukemia pathways defined in the KEGG database, or are known to physically interact with each other. Additionally, four of these TFs, which are Hif1a, Cebpb, Nfkb1, and Atf1, are known targets of rapamycin. Furthermore, the trimmed network was able to predict Eno1 as an important target of Hif1a; this key interaction could not be detected without trimming the regulatory network., Conclusions: The advantage of our new algorithm, relative to the original NCA, is that our algorithm can identify the important TF-gene interactions. Identifying the important TF-gene interactions is crucial for understanding the roles of pleiotropic global regulators, such as p53. Also, our algorithm has been developed to overcome NCA's inability to analyze large networks where multiple TFs regulate a single gene. Thus, our algorithm extends the applicability of NCA to the realm of mammalian regulatory network analysis.

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.

The molecular mechanisms underlying the development and progression of prostate cancer are poorly understood. AMP-activated protein kinase (AMPK) is a serine-threonine kinase that is activated in response to the hypoxic conditions found in human prostate cancers. In response to energy depletion, AMPK activation promotes metabolic changes to maintain cell proliferation and survival. Here, we report prevalent activation of AMPK in human prostate cancers and provide evidence that inhibition or depletion of AMPK leads to decreased cell proliferation and increased cell death. AMPK was highly activated in 40% of human prostate cancer specimens examined. Endogenous AMPK was active in both the androgen-sensitive LNCaP cells and the androgen-independent CWR22Rv1 human prostate cancer cells. Depletion of AMPK catalytic subunits by small interfering RNA or inhibition of AMPK activity with a small-molecule AMPK inhibitor (compound C) suppresses human prostate cancer cell proliferation. Apoptotic cell death was induced in LNCaP and CWR22Rv1 cells at compound C concentrations that inhibited AMPK activity. The evidence provided here is the first report that the activated AMPK pathway is involved in the growth and survival of human prostate cancer and offers novel potential targets for chemoprevention of human prostate cancer.

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.

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.

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.

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.

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.

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.