Modelling allosteric regulation for prediction of flux control in the central carbon metabolism of E. coli

Rational strain design is a fundamental step in the development of microbial cell factories. Multiple genetic manipulations are often required in order to redirect the metabolic flux towards a product of industrial interest. Most manipulation targets are focused on central carbon metabolism, which provides the molecular precursors and the energy required for other biochemical pathways. However, the complex regulation of those pathways is still not completely unraveled. Recent studies have shown that central carbon metabolism is mostly regulated at post-transcriptional levels. In this work, we explore the role of allosteric regulation in the control of metabolic fluxes. We begin by expanding a metabolic network reconstruction of the central carbon metabolism of E. coli with allosteric interaction information from relevant databases. This model is used to integrate a multi-omic dataset for this organism. We analyze the coordinated changes in enzyme, metabolite and flux levels between multiple experimental conditions, and observe cases where allosteric regulators have a major contribution in the metabolic flux changes. We then develop a method for systematic prediction of potential cases of allosteric control for given metabolic perturbations. This is a valuable approach for predicting coordinated flux changes that would not be predicted with a purely stoichiometric model representation.BioInd - Biotechnology and Bioengineering for improved Industrial and Agro-Food processes, REF. NORTE-07-0124-FEDER-00002

Modeling the contribution of allosteric regulation for flux control in the central carbon metabolism of E. coli

Redesign of microbial metabolism is a critical step in biotechnology for the production of industrially relevant compounds. Central carbon metabolism provides the energy and building blocks required for cellular growth and synthesis of the desired byproducts and, consequently, it is the main target for intervention in most rational strain design approaches. However, the complexity of central carbon metabolism is still not completely understood. Recent studies in different organisms show that flux control in central carbon metabolism is predominantly regulated by non-transcriptional mechanisms, leaving post-translational modifications, allosteric regulation, and thermodynamics as main candidates. In this work, we extend a model of central carbon metabolism of E.coli with allosteric interactions in order to reveal a hidden topology in metabolic networks. We use this model to integrate a multi-omic dataset containing transcript, protein, flux and metabolite levels to further dissect and analyze the contribution of allosteric regulation for metabolic flux control

Transcriptional vs post-transcriptional regulation of the central carbon metabolism of E. coli

Transcriptomics data are currently one of the most available types of large-scale biological data. A large number of methods have been developed to improve constraint-based simulations using these data. We recently performed a systematic comparison of these methods and observed that, at least for central carbon metabolism, there is no significant improvement in the prediction of flux distributions when gene expression data is used. These results are consistent with recent studies, in different organisms, showing that central carbon metabolism is predominantly regulated at post-transcriptional levels. Central carbon metabolism provides the precursors for the production of multiple compounds used in industrial biotechnology. Hence, it is the main target for intervention in most rational strain design strategies. However, its complexity is still not completely understood. In this work, we analyze the role of allosteric regulation, one of the main mechanisms of post-transcriptional regulation, for the control of central carbon metabolism. We extend a model of central carbon metabolism of E. coli with allosteric interactions, revealing a hidden topology in metabolic networks. We use this model to integrate a multi-omic dataset containing transcript, protein, flux and metabolite levels to further dissect the contribution of different types of regulation for metabolic flux control in these central pathways. Situations of predominant allosteric control could be identified, highlighting the importance of this kind of regulation in central carbon metabolism

Viestintä osana vastuullista henkilöstöjohtamista : Case: Vuorovaikutuksellinen oikeudenmukaisuus

Viestinnän rooli henkilöstöjohtamisessa on kasvanut ja sen merkitystä on alettu ymmärtä-mään yhä paremmin. Vielä on kuitenkin paljon kysymyksiä ja ongelmakohtia viestinnän su-jumisessa. Sanomat eivät aina välity esihenkilöiltä alaisille toivotulla tavalla, tai joskus ollen-kaan. Tässä tutkimuksessa tutkitaan viestinnän roolia vastuullisessa henkilöstöjohtamisessa. Viestintä kuuluu olennaisesti vastuulliseen henkilöstöjohtamiseen. Vastuullinen henkilöstö-johtaminen ei ole enää uusia asia, mutta edelleen erittäin tärkeä ja ajankohtainen. Se sijaan vuorovaikutteinen oikeudenmukaisuus on Suomessa vielä suhteellisen tuntematon käsite. Vuorovaikutteista oikeudenmukaisuutta on tutkittu Suomessa vielä varsin vähän. Viestintää tutkitaankin tässä tutkimuksessa vuorovaikutteisen oikeudenmukaisuuden kautta. On mie-lenkiintoista nähdä, millä tavalla vastuullinen henkilöstöjohtaminen tukee vuorovaikutteista oikeudenmukaisuutta. Toinen mielenkiintoinen asia on alaisten ja esihenkilöiden väliset nä-kemyserot viestinnän sujumisessa. Tutkimuksessa pohditaan siis myös millaisia näkemysero-ja esihenkilöstön ja alaisten välillä on viestinnässä, missä asioissa alaiset kaipaisivat enem-män vuorovaikutusta ja viestintää, miten viestinnän vajavaisuus vaikuttaa ja löytyykö viestin-nän puolelta asioita, joista esihenkilöt ja alaiset ovat samaa mieltä. Näitä asioita tarkastellaan empiirisen aineiston avulla. Empiirinen aineisto kerätään kahden kyselyn avulla, jotka laite-taan Mothers in Business- järjestön omille Facebook sivuille jäsenten vapaasti vastattaviksi. Empiirisestä aineistosta selvisi, että esihenkilöt ja alaiset ovat usein samaa mieltä viestinnän sujumisesta, mutta myös näkemyseroja löytyi

Domain architecture of a Caenorhabditis elegans AKAP suggests a novel AKAP function

AbstractA-kinase anchoring proteins (AKAPs) are adapter proteins that are involved in directing cAMP-dependent protein kinase and some other signaling enzymes to certain intracellular locations. In this study, we investigate the domain architecture of an AKAP from Caenorhabditis elegans (AKAPCE). We show that AKAPCE shares two domains with the Smad anchor for receptor activation, a FYVE-finger and a transforming growth factor β (TGFβ) receptor binding domain, suggesting that AKAPCE may interact with a receptor belonging to the TGFβ receptor family. This predicted novel AKAP function supports the recent view of AKAPs as adapter proteins that can be involved in various signaling pathways

Connecting extracellular metabolomic measurements to intracellular flux states in yeast

<p>Abstract</p> <p>Background</p> <p>Metabolomics has emerged as a powerful tool in the quantitative identification of physiological and disease-induced biological states. Extracellular metabolome or metabolic profiling data, in particular, can provide an insightful view of intracellular physiological states in a noninvasive manner.</p> <p>Results</p> <p>We used an updated genome-scale metabolic network model of Saccharomyces cerevisiae, <it>i</it>MM904, to investigate how changes in the extracellular metabolome can be used to study systemic changes in intracellular metabolic states. The <it>i</it>MM904 metabolic network was reconstructed based on an existing genome-scale network, <it>i</it>ND750, and includes 904 genes and 1,412 reactions. The network model was first validated by comparing 2,888 in silico single-gene deletion strain growth phenotype predictions to published experimental data. Extracellular metabolome data measured in response to environmental and genetic perturbations of ammonium assimilation pathways was then integrated with the <it>i</it>MM904 network in the form of relative overflow secretion constraints and a flux sampling approach was used to characterize candidate flux distributions allowed by these constraints. Predicted intracellular flux changes were consistent with published measurements on intracellular metabolite levels and fluxes. Patterns of predicted intracellular flux changes could also be used to correctly identify the regions of the metabolic network that were perturbed.</p> <p>Conclusion</p> <p>Our results indicate that integrating quantitative extracellular metabolomic profiles in a constraint-based framework enables inferring changes in intracellular metabolic flux states. Similar methods could potentially be applied towards analyzing biofluid metabolome variations related to human physiological and disease states.</p

Identification of Genome-Scale Metabolic Network Models Using Experimentally Measured Flux Profiles

Genome-scale metabolic network models can be reconstructed for well-characterized organisms using genomic annotation and literature information. However, there are many instances in which model predictions of metabolic fluxes are not entirely consistent with experimental data, indicating that the reactions in the model do not match the active reactions in the in vivo system. We introduce a method for determining the active reactions in a genome-scale metabolic network based on a limited number of experimentally measured fluxes. This method, called optimal metabolic network identification (OMNI), allows efficient identification of the set of reactions that results in the best agreement between in silico predicted and experimentally measured flux distributions. We applied the method to intracellular flux data for evolved Escherichia coli mutant strains with lower than predicted growth rates in order to identify reactions that act as flux bottlenecks in these strains. The expression of the genes corresponding to these bottleneck reactions was often found to be downregulated in the evolved strains relative to the wild-type strain. We also demonstrate the ability of the OMNI method to diagnose problems in E. coli strains engineered for metabolite overproduction that have not reached their predicted production potential. The OMNI method applied to flux data for evolved strains can be used to provide insights into mechanisms that limit the ability of microbial strains to evolve towards their predicted optimal growth phenotypes. When applied to industrial production strains, the OMNI method can also be used to suggest metabolic engineering strategies to improve byproduct secretion. In addition to these applications, the method should prove to be useful in general for reconstructing metabolic networks of ill-characterized microbial organisms based on limited amounts of experimental data