Metabolic modelling of stringent response in recombinant Escherichia coli

Recombinant protein production in Escherichia coli often derive cellular stress responses characterized by several biochemical reactions, most of which controlled at the genetic level. Drainage of precursors and energy often results in nutrient starvation, especially amino acid deprivation inducing the cellular stringent response. The production of a specific nucleotide, ppGpp, catalyzed by the enzyme RelA, is the primary signalling and initiating event in the stringent response, when an uncharged tRNA attaches to a ribosome. This nucleotide interacts with RNA polymerase to control its activity in a promoter-selective mechanism, which ultimately leads to the inhibition of synthesis of stable RNAs (rRNAs and tRNAs), which results in the decrease of ribosome concentration and, therefore comprising the overall protein synthesis machinery. Moreover, this stress response also leads to the activation of synthesis of specific mRNAs coding for proteins involved in proteolysis and other stress factors. To gain a better understanding of the dynamics of the stringent response, a mathematical model was developed. Here we propose an hybrid modelling approach based on cooperation between kinetics-based dynamic model and FBA-based static model. FBA simulation was used to incorporate valuable data in the kinetic model of the E. coli stringent response, i.e. amino acid synthesis. The model integrates the main cellular events involved in stringent response: the amino acids biosynthesis and the individual tRNA charging reactions, detection of uncharged tRNA by the ribosome and consequently activation of the enzyme relA, ultimately leading to the formation of ppGpp. The effect of ppGpp on the control of transcription rate is also predicted

Metabolomic approaches for the characterization of metabolic bottlenecks in recombinant protein production processes

Book of abstracts of the Meeting of the Institute for Biotechnology and Bioengineering, 2, Braga, Portugal, 2010The optimization of bioprocesses using recombinant microorganisms is still restrained by the lack of information available on the metabolic responses induced by various stress conditions. The rapid exhaustion of essential metabolic precursors (e.g. amino acids) and cellular energy toward recombinant biosynthetic processes may result in the imbalance of the metabolism of the host cell, also called metabolic burden. In the past few years, the association of this metabolic burden with other cellular events, like the stringent response, has been demonstrated [1]. The unusual accumulation of ppGpp, a molecule produced by the ribosome-associated RelA synthetase induced by the deprivation of amino acids, is the hallmark of this stress response that results in the inhibition of cellular growth and lower productivity levels. The regulatory mechanisms of this ppGpp-induced response are known in some detail, but the impact of this response on the cellular metabolism has been less studied. Metabolomic analyses can provide substantial information at the biochemical level, in particular during recombinant bioprocesses. Therefore, metabolomic-based approaches [2], including profiling of intracellular and extracellular metabolite pools, were applied to investigate the influence of recombinant processes on the host cells’ metabolism. In these studies two E. coli strains (E. coli W3110 and the isogenic relA mutant) were used to investigate the advantages of using “relaxed” phenotypes (i.e. relA mutant strain) as host cells in recombinant bioprocesses. Indeed, this cellular system presented major advantages in terms of biomass yield and productivity, which implied a remarkable improvement in recombinant bioprocesses

Obtaining pressure versus concentration phase diagrams in spin systems from Monte Carlo simulations

We propose an efficient procedure for determining phase diagrams of systems that are described by spin models. It consists of combining cluster algorithms with the method proposed by Sauerwein and de Oliveira where the grand canonical potential is obtained directly from the Monte Carlo simulation, without the necessity of performing numerical integrations. The cluster algorithm presented in this paper eliminates metastability in first order phase transitions allowing us to locate precisely the first-order transitions lines. We also produce a different technique for calculating the thermodynamic limit of quantities such as the magnetization whose infinite volume limit is not straightforward in first order phase transitions. As an application, we study the Andelman model for Langmuir monolayers made of chiral molecules that is equivalent to the Blume-Emery-Griffiths spin-1 model. We have obtained the phase diagrams in the case where the intermolecular forces favor interactions between enantiomers of the same type (homochiral interactions). In particular, we have determined diagrams in the surface pressure versus concentration plane which are more relevant from the experimental point of view and less usual in numerical studies

Cross-cutting computational strategies to genome-scale modelling

Book of abstracts of the Meeting of the Institute for Biotechnology and Bioengineering, 2, Braga, Portugal, 2010Hereby, the aim is to present some of our research efforts towards the reconstruction of genome-scale models. Namely, we focus on the development of cross-cutting computational strategies for the integration and validation of heterogeneous data in support to traditional manual curation and, describe application scenarios on the model organism E. coli. We address the systematic comparison of database contents and the harvest and extraction of contents from scientific literature. Aiming to help researchers assess the gains and losses to be accounted for in biological repositories and thus, choose the most content-bearing repositories for each particular integration problem/domain, we have implemented a Webalike report tool [1]. This tool analyses the contents of well-known repositories under userspecified integration scenarios considering the coverage of main biological entities (genes, proteins and compounds) and the evaluation of standard nomenclatures, common names and repository cross-links as elements of integration. Also, acknowledging that most biological data still lays on scientific literature and requires extensive and time-consuming manual curation, we have been developing literature screening and processing tools [2]. The goal is to systematise the search of relevant literature based on user-specified keywords and the extraction of relevant information by applying statistical approaches that exploit simple pattern matching, machine learning and ontological enrichment. Considering the wide scope of current applications that can benefit from the analysis of large amounts of data, all our tools are publicly available through our group’s Web pages (http://biopseg.deb.uminho.pt)

Application of genome-scale metabolic models to the optimization of recombinant protein production in Escherichia coli

Escherichia coli has been the organism of choice for the production of many recombinant proteins with high therapeutic value. However, while the research on molecular biology has allowed the development of very strong promoters, there are still some phenomena associated with this process that hamper the full use of those technologies: aerobic acetate production associated with high specific growth rates, and the so-called stringent response that usually occurs when very high levels of heterologous protein production takes place. In both cases, productivity is affected due to a decrease in the specific growth and production rates. In this work, a systems biology approach for modelling recombinant protein production processes was used aiming its optimization. The existing genome-scale metabolic model of Escherichia coli was modified by including an equation for protein production (the model protein GFP – Green Florescent Protein), based on its amino acids content. For the validation of the genome-scale model in high-cell density processes, highly reproducible fed-batch fermentations are run with constant specific growth rate. The developed data acquisition and control system allows to control the substrate addition rate, and to acquire on-line the fermenter’s weight, to calculate oxygen and carbon dioxide transfer rates, as well as to obtain glucose and acetate concentrations using a developed Flow Injection Analysis system

Footprinting microbial metabolites in nature and medicine

The study of metabolic alterations in response to genetic and environmental perturbations has been a central topic in microbial metabolomics (Fiehn, 2002; Kol et al., 2010; Villas-Boas et al., 2008). Some of these alterations can be readily detected by changes in their surroundings, normally associated with metabolites that are released by cells as by-products of the metabolism or as extracellular signalling molecules to mediate cross-talk within microbial communities. The analysis of these metabolites, also known as metabolic footprinting, has been widely applied with different purposes: discriminating between metabolic phenotypes in order to classify and identify mutant strains (Villas-Boas et al., 2008); monitoring bioprocesses with the aim to detect specific metabolites that indicate alterations in the culture performance (Carneiro et al., 2011; Sue et al., 2011); and identifying quorum-sensing metabolites that indicate potential targets to annihilate pathogens (Birkenstock et al., 2012). These metabolic readouts have been also useful to give insights into intracellular metabolic activities and provide a straightforward way to analyse simultaneously multiple metabolic activities, since no extraction procedures are required to analyse the endometabolome (i.e., intracellular metabolites). Thus, through metabolic footprint analysis we can assess central metabolic activities that characterize the reproduction and survival of organisms. We have developed a methodology to evaluate the metabolic state of microbial cultures by analysing the footprints of two microbial systems: the bacterium Escherichia coli and the human pathogen Helicobacter pylori. Strategies for sampling and sample preparation were developed, as well as the analytical procedures based on gas chromatography with mass spectrometry (GC-MS). A wide variety of metabolites was detected, including fatty, amino and organic acids, which allowed us to address changes in most central metabolic pathways, such as the tricarboxylic acid cycle (TCA cycle), the biosynthesis of amino and fatty acids, as well as other energy generating metabolic reactions. The analysis of extracellular metabolites of E. coli cultures at different growth conditions were first performed to discriminate the physiological state of cultures and to evaluate the metabolic alterations produced at different growth conditions. According to our results in these experiments, metabolic footprints are good indicators of alterations in the intracellular metabolism. Next, the metabolic footprints of H. pylori cultures were investigated to get insights on the catabolism of this human pathogen. Overall, fifteen amino acids were detected in extracellular medium; six of them were confirmed as essentials for H. pylori growth, four amino acids were identified as non-essentials and can be used as carbon source, whilst five amino acids were identified as non-essentials and non-carbon source. In addition, some organic acids were also identified as carbon sources for H. pylori. This metabolic footprint analysis of H. pylori cultures allowed us to uncover key metabolic activities, mainly related with amino acids catabolism and to get insight on the metabolic behaviour of this organism. The characterization of catabolic pathways, as well as of possible metabolic constraints, is of major importance to understand the dynamic basis of the interactions host–microbe in the human gut, and in particular to discover potential ‘diagnostic’ biomarkers. It is well-known that pathogen's metabolism can influence the host health and may affect drug metabolism, toxicity and the efficacy of therapies (Holmes et al., 2011). However, little is known about their metabolic structure and behaviour. Our methodology allows uncovering part of the metabolic structure of H. pylori metabolism and undisclosed catabolic activities. Acknowledgments This work was partially supported by the MIT-Portugal Program in Bioengineering (MIT-Pt/BS-BB/0082/2008), the research project HeliSysBio-Molecular Systems Biology Helicobacter pylori (FCT PTDC/EBB-EBI/104235/2008) and a Post-doc grant from Portuguese FCT (Fundação para a Ciência e Tecnologia) (ref. SFRH/BPD/73951/2010). 1. Fiehn O. 2002. Metabolomics - the link between genotypes and phenotypes. Plant Molecular Biology 48: 155-71. 2. Kol S, Merlo ME, Scheltema RA, de VM, Vonk RJ, Kikkert NA, Dijkhuizen L, Breitling R, Takano E. 2010. Metabolomic characterization of the salt stress response in Streptomyces coelicolor. Applied and Environmental Microbiology 76: 2574-81. 3. Villas-Boas SG, Moon CD, Noel S, Hussein H, Kelly WJ, Cao M, Lane GA, Cookson AL, Attwood GT. 2008. Phenotypic characterization of transposon-inserted mutants of Clostridium proteoclasticum B316(T) using extracellular metabolomics. Journal of Biotechnology 134: 55-63. 4. Sue T, Obolonkin V, Griffiths H, Villas-Boas SG. 2011. An exometabolomics approach to monitoring microbial contamination in microalgal fermentation processes by using metabolic footprint analysis. Appl Environ Microbiol 77: 7605-10. 5. Carneiro S, Villas-Boas SG, Ferreira EC, Rocha I. 2011. Metabolic footprint analysis of recombinant Escherichia coli strains during fed-batch fermentations. Molecular Biosystems 7: 899-910. 6. Birkenstock T, Liebeke M, Winstel V, Krismer B, Gekeler C, Niemiec MJ, Bisswanger H, Lalk M, Peschel A. 2012. Exometabolome analysis identifies pyruvate dehydrogenase as a target for the antibiotic triphenylbismuthdichloride in multiresistant bacterial pathogens. J Biol Chem 287: 2887-95. 7. Holmes E, Li JV, Athanasiou T, Ashrafian H, Nicholson JK. 2011. Understanding the role of gut microbiome-host metabolic signal disruption in health and disease. Trends Microbiol 19: 349-59