Project description:A group of gram positive bacteria that share the characteristic of fermenting hexose sugars to lactic acid are generally referred to as lactic acid bacteria (LAB). Enterococcus faecalis is one of the widely studied LABs due to a multiutude of reasons. On the one hand, it plays an important role in dairy industry, being for example a starter in cheese cultures. On the other hand, it accounts for a large part of the infections caused by the LABs in hospital environments. During the past few years, it developed resistance against most of the major antibiotics. Here, in an attempt to study its adaptive metabolism, a glutamine synthetase mutant (∆glnA) of E. faecalis was subjected to pH shift and the results from the integrative analysis of its metabolic network were compared to those of the wild type. The proteome data generated in this study were used to constrain the genome-scale metabolic network at two pH level, aiming to reduce the solution space and improve the accuracy of model simulation. This data particularly helped to come up with a new design for the amino acid transport system in the genome-scale model, resulting in an accurate reproduction of the metabolic behaviour of E. faecalis.
Project description:Genome-scale models represent the link between an organism's genetic information and experimentally observable biological phenotypes. They facilitate metabolic engineering and the discovery of network properties such as the identification of novel drug targets. Most commonly, metabolite consumption data is used to limit the solution space, sometimes in combination with gene expression data. However, information about gene expression only poorly correlates with the abundance of the respective proteins within the cell. As such, we developed a method to map and integrate the whole-cell proteome into genome-scale models on the example of lactic acid bacteria (LAB). To the best of our knowledge, this work represents the first effort to integrate proteome data into genome-scale models on such a scale.
Project description:Genome-scale models represent the link between an organism's genetic information and experimentally observable biological phenotypes. They facilitate metabolic engineering and the discovery of network properties such as the identification of novel drug targets. Most commonly, metabolite consumption data is used to limit the solution space, sometimes in combination with gene expression data. However, information about gene expression only poorly correlates with the abundance of the respective proteins within the cell. As such, we developed a method to map and integrate the whole-cell proteome into genome-scale models on the example of lactic acid bacteria (LAB). To the best of our knowledge, this work represents the first effort to integrate proteome data into genome-scale models on such a scale .
Project description:Bacteria that live in the acidic environment face number of growth-related challenges from the intracellular pH changes. In order to survive under acidic environment, Lactic acid bacteria must employ multiple genes and proteins to regulate the relative pathways.
Project description:Bacteria that live in the acidic environment face number of growth-related challenges from the intracellular pH changes. In order to survive under acidic environment, Lactic acid bacteria must employ multiple genes and proteins to regulate the relative pathways.