Visualizing regulatory interactions in metabolic networks

<p>Abstract</p> <p>Background</p> <p>Direct visualization of data sets in the context of biochemical network drawings is one of the most appealing approaches in the field of data evaluation within systems biology. One important type of information that is very helpful in interpreting and understanding metabolic networks has been overlooked so far. Here we focus on the representation of this type of information given by the strength of regulatory interactions between metabolite pools and reaction steps.</p> <p>Results</p> <p>The visualization of such interactions in a given metabolic network is based on a novel concept defining the regulatory strength (RS) of effectors regulating certain reaction steps. It is applicable to any mechanistic reaction kinetic formula. The RS values are measures for the strength of an up- or down-regulation of a reaction step compared with the completely non-inhibited or non-activated state, respectively. One numerical RS value is associated to any effector edge contained in the network. The RS is approximately interpretable on a percentage scale where 100% means the maximal possible inhibition or activation, respectively, and 0% means the absence of a regulatory interaction. If many effectors influence a certain reaction step, the respective percentages indicate the proportion in which the different effectors contribute to the total regulation of the reaction step. The benefits of the proposed method are demonstrated with a complex example system of a dynamic <it>E. coli </it>network.</p> <p>Conclusion</p> <p>The presented visualization approach is suitable for an intuitive interpretation of simulation data of metabolic networks under dynamic as well as steady-state conditions. Huge amounts of simulation data can be analyzed in a quick and comprehensive way. An extended time-resolved graphical network presentation provides a series of information about regulatory interaction within the biological system under investigation.</p

Glucocorticoid receptors are required for up‐regulation of neuronal 5‐lipoxygenase (5LOX) expression by dexamethasone

5‐lipoxygenase (5LOX) is the key enzyme in the synthesis of leukotrienes from arachidonic acid. Hyperglucocorticoidemia, dexamethasone, and aging up‐regulate 5LOX in the brain, including the cerebellum in vivo. We studied the mechanisms of dexamethasone‐triggered 5LOX up‐regulation in primary cultures of rat cerebellar granule neurons (CGN). We measured 5LOX mRNA and protein contents, and the formation of cysteinyl leukotrienes (LTC4, LTD4, and LTE4). The dexamethasone (0.1 μM or 1 μM)‐increased 5LOX mRNA and protein contents were already observed at 3 h of treatment, and they persisted for at least 24 h. Dexamethasone also increased the content of cysteinyl leukotrienes, assayed in the presence of 2 μM calcium ionophore A23187 and 10 μM arachidonic acid. The stimulatory effect of dexamethasone on 5LOX expression was inhibited by the glucocorticoid receptor (GR) antagonist RU486 and by reducing the CGN content of GR receptor protein with a GR‐specific antisense oligonucleotide. The 5LOX mRNA half‐life was longer in dexamethasone than in vehicle‐treated CGNs. Our results indicate that dexamethasone increases 5LOX expression in CGNs in a GR‐dependent manner and that it also increases the stability of 5LOX mRNA. Further studies are warranted to elucidate the physiologic/pathologic significance of glucocorticoid‐regulated expression of 5LOX in the central nervous system.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154349/1/fsb2fj000836fje-sup-0001.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154349/2/fsb2fj000836fje.pd

Effectiveness of Two Different Fluoride-Based Agents in the Treatment of Dentin Hypersensitivity: A Prospective Clinical Trial

Hyperesthesia is related to increased sensitivity of dental tissues to mechanical, chemical and thermal stimuli. The aim of this prospective clinical trial was to compare the effectiveness of a calcium-fluoride-forming agent (Tiefenfluorid®, Humanchemie GmbH, Alfeld, Germany) with that of a fluoride varnish (Enamelast™, Ultradent Inc., Cologne, Germany) in the treatment of dental hyperesthesia in adult patients. In total, 176 individuals (106 females and 70 males, aged 18–59 years old) diagnosed with dental hyperesthesia (DH) were enrolled. The main clinical symptoms were hyperesthesia from coldness and sweetness during chewing; the types of clinical lesions were also determined and recorded. The patients were selected randomly and divided into two groups: (i) the first group of 96 patients was treated with Tiefenfluorid® applied in three appointments at 7-day intervals; (ii) the second group of 80 patients was treated with Enamelast™, applied seven times at 7-day intervals. All the patients were recalled 7 days, 14 days, 1 month, 3 months, and 6 months from the last application. At the baseline and during every follow-up visit, the DH was measured with a pulp tester. A random intercept/random slope model was used to evaluate the effect of the treatment, at various times with respect to the initial diagnosis. Within the limits of the present study, Tiefenfluorid® was more effective than Enamelast™ against DH in that it provided long-lasting results, with a significant improvement still detected at the latest 6-month follow-up

A Mighty Small Heart: The Cardiac Proteome of Adult Drosophila melanogaster

Drosophila melanogaster is emerging as a powerful model system for the study of cardiac disease. Establishing peptide and protein maps of the Drosophila heart is central to implementation of protein network studies that will allow us to assess the hallmarks of Drosophila heart pathogenesis and gauge the degree of conservation with human disease mechanisms on a systems level. Using a gel-LC-MS/MS approach, we identified 1228 protein clusters from 145 dissected adult fly hearts. Contractile, cytostructural and mitochondrial proteins were most abundant consistent with electron micrographs of the Drosophila cardiac tube. Functional/Ontological enrichment analysis further showed that proteins involved in glycolysis, Ca2+-binding, redox, and G-protein signaling, among other processes, are also over-represented. Comparison with a mouse heart proteome revealed conservation at the level of molecular function, biological processes and cellular components. The subsisting peptidome encompassed 5169 distinct heart-associated peptides, of which 1293 (25%) had not been identified in a recent Drosophila peptide compendium. PeptideClassifier analysis was further used to map peptides to specific gene-models. 1872 peptides provide valuable information about protein isoform groups whereas a further 3112 uniquely identify specific protein isoforms and may be used as a heart-associated peptide resource for quantitative proteomic approaches based on multiple-reaction monitoring. In summary, identification of excitation-contraction protein landmarks, orthologues of proteins associated with cardiovascular defects, and conservation of protein ontologies, provides testimony to the heart-like character of the Drosophila cardiac tube and to the utility of proteomics as a complement to the power of genetics in this growing model of human heart disease

Current challenges in software solutions for mass spectrometry-based quantitative proteomics

This work was in part supported by the PRIME-XS project, grant agreement number 262067, funded by the European Union seventh Framework Programme; The Netherlands Proteomics Centre, embedded in The Netherlands Genomics Initiative; The Netherlands Bioinformatics Centre; and the Centre for Biomedical Genetics (to S.C., B.B. and A.J.R.H); by NIH grants NCRR RR001614 and RR019934 (to the UCSF Mass Spectrometry Facility, director: A.L. Burlingame, P.B.); and by grants from the MRC, CR-UK, BBSRC and Barts and the London Charity (to P.C.

Information Visualization Techniques for Metabolic Engineering

The main purpose of metabolic engineering is the modification of biological systems towards specific goals using genetic manipulations. For this purpose, models are built that describe the stationary and dynamic behaviour of biochemical reaction networks inside a biological cell. Based on these models, simulations are carried out with the intention to understand the cell's behaviour. The modeling process leads to the generation of large amounts of data, both during the modeling itself and after the simulation of the created models. The manual interpretation is almost impossible; consequently, appropriate techniques for supporting the analysis and visualization of these data are needed. The purpose of this thesis is to investigate visualization and data mining techniques to support the metabolic modeling process. The work presented in this thesis is divided into several tracks: -Visualization of metabolic networks and the associated simulation data. Novel visualization techniques will be presented, which allow the visual exploration of metabolic network dynamics, beyond static snapshots of the simulated data plots. Node-link representations of the metabolic network are animated using the time series of metabolite concentrations and reaction rates. In this way, bottlenecks and active parts of metabolic networks can be distinguished. Additionally, 3D visualization techniques for metabolic networks are explored for cross-free drawing of the networks in 3D visualization space. Steerable drawing of metabolic networks is also investigated. In contrast to other approaches for drawing metabolic networks, user guided drawing of the networks allows the creation of high quality drawings by including user feedback in the drawing process. -Comparison of XML/SBML files. SBML (Systems Biology Markup Language) has become ubiquitous in metabolic modeling, serving the storage and exchange of models in XML format. Generally, the modeling process is an iterative task where the next generation model is a further development of the current model, resulting in a family of models stored in SBML format. The SBML format, however, includes a great deal of information, from the structure of the biochemical network to parameters of the model or measured data. Consequently, the CustX-Diff algorithm for a customizable comparison of XML files will be introduced. By customizing the comparison process through the specification of XPath expressions, an adaptable change detection process is enabled. Thus, the comparison process can be focused on specific parts of a XML/SBML document, e.g. on the structure of a metabolic network. -Visual exploration of time-varying sensitivity matrices. Sensitivity analysis is a special method used in simulation to analyze the sensitivity of a model with respect to its parameters. The results of sensitivity analysis of a metabolic network are large time-varying matrices, which need to be properly visualized. However, the visualization of time-varying high-dimensional data is a challenging problem. For this purpose, an extensible framework is proposed, consisting of existing and novel visualization methods, which allow the visual exploration of time-varying sensitivity matrices. Tabular visualization techniques, such as the reorderable matrix, are developed further, and algorithms for their reordering are discussed. Existing and novel techniques for exploring proximity data, both in matrix form and projected using multi-dimensional scaling (MDS), are also discussed. Information visualization paradigms such as focus+context based distortion and overview+details are proposed to enhance such techniques. -Cluster ensembles for analyzing time-varying sensitivity matrices. A novel relationship-based cluster ensemble, which relies on the accumulation of the evolving pairwise similarities of objects (i.e. parameters) will be proposed, as a robust and efficient method for clustering time-varying high-dimensional data. The time-dependent similarities, obtained from the fuzzy partitions created during the fuzzy clustering process, are aggregated, and the final clustering result is derived from this aggregation

Information Visualization Techniques for Metabolic Engineering

The main purpose of metabolic engineering is the modification of biological systems towards specific goals using genetic manipulations. For this purpose, models are built that describe the stationary and dynamic behaviour of biochemical reaction networks inside a biological cell. Based on these models, simulations are carried out with the intention to understand the cell's behaviour. The modeling process leads to the generation of large amounts of data, both during the modeling itself and after the simulation of the created models. The manual interpretation is almost impossible; consequently, appropriate techniques for supporting the analysis and visualization of these data are needed. The purpose of this thesis is to investigate visualization and data mining techniques to support the metabolic modeling process. The work presented in this thesis is divided into several tracks: -Visualization of metabolic networks and the associated simulation data. Novel visualization techniques will be presented, which allow the visual exploration of metabolic network dynamics, beyond static snapshots of the simulated data plots. Node-link representations of the metabolic network are animated using the time series of metabolite concentrations and reaction rates. In this way, bottlenecks and active parts of metabolic networks can be distinguished. Additionally, 3D visualization techniques for metabolic networks are explored for cross-free drawing of the networks in 3D visualization space. Steerable drawing of metabolic networks is also investigated. In contrast to other approaches for drawing metabolic networks, user guided drawing of the networks allows the creation of high quality drawings by including user feedback in the drawing process. -Comparison of XML/SBML files. SBML (Systems Biology Markup Language) has become ubiquitous in metabolic modeling, serving the storage and exchange of models in XML format. Generally, the modeling process is an iterative task where the next generation model is a further development of the current model, resulting in a family of models stored in SBML format. The SBML format, however, includes a great deal of information, from the structure of the biochemical network to parameters of the model or measured data. Consequently, the CustX-Diff algorithm for a customizable comparison of XML files will be introduced. By customizing the comparison process through the specification of XPath expressions, an adaptable change detection process is enabled. Thus, the comparison process can be focused on specific parts of a XML/SBML document, e.g. on the structure of a metabolic network. -Visual exploration of time-varying sensitivity matrices. Sensitivity analysis is a special method used in simulation to analyze the sensitivity of a model with respect to its parameters. The results of sensitivity analysis of a metabolic network are large time-varying matrices, which need to be properly visualized. However, the visualization of time-varying high-dimensional data is a challenging problem. For this purpose, an extensible framework is proposed, consisting of existing and novel visualization methods, which allow the visual exploration of time-varying sensitivity matrices. Tabular visualization techniques, such as the reorderable matrix, are developed further, and algorithms for their reordering are discussed. Existing and novel techniques for exploring proximity data, both in matrix form and projected using multi-dimensional scaling (MDS), are also discussed. Information visualization paradigms such as focus+context based distortion and overview+details are proposed to enhance such techniques. -Cluster ensembles for analyzing time-varying sensitivity matrices. A novel relationship-based cluster ensemble, which relies on the accumulation of the evolving pairwise similarities of objects (i.e. parameters) will be proposed, as a robust and efficient method for clustering time-varying high-dimensional data. The time-dependent similarities, obtained from the fuzzy partitions created during the fuzzy clustering process, are aggregated, and the final clustering result is derived from this aggregation