Daniel R. Hyduke

Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0

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.

Nitric oxide is consumed, rather than conserved, by reaction with oxyhemoglobin under physiological conditions

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.

COBRApy: COnstraints-Based Reconstruction and Analysis for Python

BackgroundCOnstraint-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.ResultsHere, 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.ConclusionCOBRApy 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.Availabilityhttp://opencobra.sourceforge.net/

Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction

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.

Modulation of nitric oxide bioavailability by erythrocytes

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.

In silico method for modelling metabolism and gene product expression at genome scale

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 organisms 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.

Integrated network analysis identifies nitric oxide response networks and dihydroxyacid dehydratase as a crucial target in Escherichia coli

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.

AMP-activated protein kinase promotes human prostate cancer cell growth and survival

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. [Mol Cancer Ther 2009;8(4):733–41]

Analysis of omics data with genome-scale models of metabolism.

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.

A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonella Typhimurium LT2.

BackgroundMetabolic 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.ResultsHere, 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.ConclusionTaken 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.