Sampling the Solution Space in Genome-Scale Metabolic Networks Reveals Transcriptional Regulation in Key Enzymes

Genome-scale metabolic models are available for an increasing number of organisms and can be used to define the region of feasible metabolic flux distributions. In this work we use as constraints a small set of experimental metabolic fluxes, which reduces the region of feasible metabolic states. Once the region of feasible flux distributions has been defined, a set of possible flux distributions is obtained by random sampling and the averages and standard deviations for each of the metabolic fluxes in the genome-scale model are calculated. These values allow estimation of the significance of change for each reaction rate between different conditions and comparison of it with the significance of change in gene transcription for the corresponding enzymes. The comparison of flux change and gene expression allows identification of enzymes showing a significant correlation between flux change and expression change (transcriptional regulation) as well as reactions whose flux change is likely to be driven only by changes in the metabolite concentrations (metabolic regulation). The changes due to growth on four different carbon sources and as a consequence of five gene deletions were analyzed for Saccharomyces cerevisiae. The enzymes with transcriptional regulation showed enrichment in certain transcription factors. This has not been previously reported. The information provided by the presented method could guide the discovery of new metabolic engineering strategies or the identification of drug targets for treatment of metabolic diseases

Dynamic Modelling under Uncertainty: The Case of Trypanosoma brucei Energy Metabolism

Kinetic models of metabolism require detailed knowledge of kinetic parameters. However, due to measurement errors or lack of data this knowledge is often uncertain. The model of glycolysis in the parasitic protozoan Trypanosoma brucei is a particularly well analysed example of a quantitative metabolic model, but so far it has been studied with a fixed set of parameters only. Here we evaluate the effect of parameter uncertainty. In order to define probability distributions for each parameter, information about the experimental sources and confidence intervals for all parameters were collected. We created a wiki-based website dedicated to the detailed documentation of this information: the SilicoTryp wiki (http://silicotryp.ibls.gla.ac.uk/wiki/Glycolysis). Using information collected in the wiki, we then assigned probability distributions to all parameters of the model. This allowed us to sample sets of alternative models, accurately representing our degree of uncertainty. Some properties of the model, such as the repartition of the glycolytic flux between the glycerol and pyruvate producing branches, are robust to these uncertainties. However, our analysis also allowed us to identify fragilities of the model leading to the accumulation of 3-phosphoglycerate and/or pyruvate. The analysis of the control coefficients revealed the importance of taking into account the uncertainties about the parameters, as the ranking of the reactions can be greatly affected. This work will now form the basis for a comprehensive Bayesian analysis and extension of the model considering alternative topologies

FOX-2 Dependent Splicing of Ataxin-2 Transcript Is Affected by Ataxin-1 Overexpression

Alternative splicing is a fundamental posttranscriptional mechanism for controlling gene expression, and splicing defects have been linked to various human disorders. The splicing factor FOX-2 is part of a main protein interaction hub in a network related to human inherited ataxias, however, its impact remains to be elucidated. Here, we focused on the reported interaction between FOX-2 and ataxin-1, the disease-causing protein in spinocerebellar ataxia type 1. In this line, we further evaluated this interaction by yeast-2-hybrid analyses and co-immunoprecipitation experiments in mammalian cells. Interestingly, we discovered that FOX-2 localization and splicing activity is affected in the presence of nuclear ataxin-1 inclusions. Moreover, we observed that FOX-2 directly interacts with ataxin-2, a protein modulating spinocerebellar ataxia type 1 pathogenesis. Finally, we provide evidence that splicing of pre-mRNA of ataxin-2 depends on FOX-2 activity, since reduction of FOX-2 levels led to increased skipping of exon 18 in ataxin-2 transcripts. Most striking, we observed that ataxin-1 overexpression has an effect on this splicing event as well. Thus, our results demonstrate that FOX-2 is involved in splicing of ataxin-2 transcripts and that this splicing event is altered by overexpression of ataxin-1

Measurement of Epstein-Barr virus DNA load using a novel quantification standard containing two EBV DNA targets and SYBR Green I dye

<p>Abstract</p> <p>Background</p> <p>Reactivation of Epstein-Barr virus (EBV) infection may cause serious, life-threatening complications in immunocompromised individuals. EBV DNA is often detected in EBV-associated disease states, with viral load believed to be a reflection of virus activity. Two separate real-time quantitative polymerase chain reaction (QPCR) assays using SYBR Green I dye and a single quantification standard containing two EBV genes, Epstein-Barr nuclear antigen-1 (EBNA-1) and BamHI fragment H rightward open reading frame-1 (BHRF-1), were developed to detect and measure absolute EBV DNA load in patients with various EBV-associated diseases. EBV DNA loads and viral capsid antigen (VCA) IgG antibody titres were also quantified on a population sample.</p> <p>Results</p> <p>EBV DNA was measurable in ethylenediaminetetraacetic acid (EDTA) whole blood, peripheral blood mononuclear cells (PBMCs), plasma and cerebrospinal fluid (CSF) samples. EBV DNA loads were detectable from 8.0 × 10²to 1.3 × 10⁸copies/ml in post-transplant lymphoproliferative disease (n = 5), 1.5 × 10³to 2.0 × 10⁵copies/ml in infectious mononucleosis (n = 7), 7.5 × 10⁴to 1.1 × 10⁵copies/ml in EBV-associated haemophagocytic syndrome (n = 1), 2.0 × 10²to 5.6 × 10³copies/ml in HIV-infected patients (n = 12), and 2.0 × 10²to 9.1 × 10⁴copies/ml in the population sample (n = 218). EBNA-1 and BHRF-1 DNA were detected in 11.0% and 21.6% of the population sample respectively. There was a modest correlation between VCA IgG antibody titre and BHRF-1 DNA load (rho = 0.13, p = 0.05) but not EBNA-1 DNA load (rho = 0.11, p = 0.11).</p> <p>Conclusion</p> <p>Two sensitive and specific real-time PCR assays using SYBR Green I dye and a single quantification standard containing two EBV DNA targets, were developed for the detection and measurement of EBV DNA load in a variety of clinical samples. These assays have application in the investigation of EBV-related illnesses in immunocompromised individuals.</p

A framework for evolutionary systems biology

<p>Abstract</p> <p>Background</p> <p>Many difficult problems in evolutionary genomics are related to mutations that have weak effects on fitness, as the consequences of mutations with large effects are often simple to predict. Current systems biology has accumulated much data on mutations with large effects and can predict the properties of knockout mutants in some systems. However experimental methods are too insensitive to observe small effects.</p> <p>Results</p> <p>Here I propose a novel framework that brings together evolutionary theory and current systems biology approaches in order to quantify small effects of mutations and their epistatic interactions <it>in silico</it>. Central to this approach is the definition of fitness correlates that can be computed in some current systems biology models employing the rigorous algorithms that are at the core of much work in computational systems biology. The framework exploits synergies between the realism of such models and the need to understand real systems in evolutionary theory. This framework can address many longstanding topics in evolutionary biology by defining various 'levels' of the adaptive landscape. Addressed topics include the distribution of mutational effects on fitness, as well as the nature of advantageous mutations, epistasis and robustness. Combining corresponding parameter estimates with population genetics models raises the possibility of testing evolutionary hypotheses at a new level of realism.</p> <p>Conclusion</p> <p>EvoSysBio is expected to lead to a more detailed understanding of the fundamental principles of life by combining knowledge about well-known biological systems from several disciplines. This will benefit both evolutionary theory and current systems biology. Understanding robustness by analysing distributions of mutational effects and epistasis is pivotal for drug design, cancer research, responsible genetic engineering in synthetic biology and many other practical applications.</p

Metabolic network analysis integrated with transcript verification for sequenced genomes.

With sequencing of thousands of organisms completed or in progress, there is a growing need to integrate gene prediction with metabolic network analysis. Using Chlamydomonas reinhardtii as a model, we describe a systems-level methodology bridging metabolic network reconstruction with experimental verification of enzyme encoding open reading frames. Our quantitative and predictive metabolic model and its associated cloned open reading frames provide useful resources for metabolic engineering