In silico tracing of the fate of single carbon atoms in different physiological states of E. coli

Noushin Hadadi, Jasmin Maria Hafner, Beatriz Lopes Pinto Basto Carreira
2018
Discussion par affiche

Résumé

Many applications of systems biology such as the simulation of isotope labeling experiments and pathway inference in metabolic engineering rely on atom-level representation of metabolism. Because of its challenging complexity and the difficulty of obtaining correct atom maps of biochemical reactions, the current understanding of the atomic level of metabolism cannot yet fully explain the outcome of stable-isotope labeling experiments. To our knowledge, iAM.NICE is the only computational tool for automatic mapping of single atoms in metabolic reactions, pathways and networks, that ensures the correctness of the mapping based on biochemical reaction mechanisms. iAM.NICE is an extension of BNICE.ch, which reconstructs biochemical reactions using expert curated, generalized biochemical reaction rules. However, inferring differential flux profiles from different atom-mapped metabolic pathways remains a challenge. In this study, we apply iAM.NICE to the core metabolism of E. coli to understand (i) the impact of different physiological states on the distribution of atom labels in the compounds of the central carbon metabolism, and (ii) the consecutive relation of labeled biomass precursor metabolites to the possible labeling distributions in the biomass building blocks (BBB). Our results show that different physiological states of a cell give rise to different distributions of carbon atoms in BBB precursor metabolites, information that is not only crucial for the design and the analysis of isotope labeling experiments, but also deepens our understanding of metabolism at the atomic level. Finally, our method can be easily extended to study in silico the conversion of elements other than carbon, and it can be applied to study organisms other than E. coli. The exhaustive results of our study can be consulted on our website (http://lcsb-databases.epfl.ch/pathways/LabelingList ). Access is freely available for academic purpose upon subscription.

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https://infoscience.epfl.ch/record/256574?ln=fr

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Noushin Hadadi, Jasmin Maria Hafner, Beatriz Lopes Pinto Basto Carreira
2018
Discussion par affiche

Résumé

Official source

https://infoscience.epfl.ch/record/256574?ln=fr

À propos de ce résultat

Concepts associés (16)

Concepts associés

Chargement

Publications associées (41)

Publications associées

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Modeling, predicting and mining metabolism at atom-level resolution

Jasmin Maria Hafner

Living organisms can catalyze many thousands of biochemical reactions that they use to convert energy and matter, which provides them with the essentials for life. The sum of these chemical reactions happening in an organism is called metabo-lism. Understanding metabolism is crucial to elucidate the fundamental principles of biology, and further to enable us to redesign it for the sustainable, biosynthetic pro-duction of bio-based fuels, commodity chemicals and medicines. Mathematical mod-els are essential to organize and understand the complexity of metabolism. They usu-ally represent metabolism as a network of reactions, but they tend to neglect the exact molecular structure of the metabolites. To redesign metabolic reactions, how-ever, a mechanistic understanding of metabolic reactions and their catalysts, proteins called enzymes, is essential. In this work, a mathematical description of enzymatic reaction mechanism, called generalized reaction rules, is applied to computationally simulate and predict meta-bolic processes at the level of atoms. Each reaction rule describes the catalytic activi-ty of an enzyme, or a group of enzymes, at the mechanistic level by encoding the re-arrangement of atoms in the reaction. The reaction rules are called âgeneralizedâ, because they mimic the ability of a single enzyme to catalyze multiple reactions by acting on a range of substrates. Using these reaction rules, we first developed a computational representation of me-tabolism that allowed tracking single atoms throughout complex metabolic reaction networks. The principle of atom-tracking was then used to develop a graph-theory based method to represent and analyze metabolic networks, and to reliably identify of metabolic pathways for the biosynthesis of chemicals. Next, we applied the gener-alized reaction rules to predict all possible novel, hypothetical reactions from known biological compounds, and we stored the five million generated novel reactions them in a database called ATLAS. Finally, the developed tools and resources were applied to specific engineering and research problems, such as the biosynthetic pathway design for the biofuel bisabolene and the plastic precursor 1,4-butanediol. We further pre-dicted a biosynthesis route for the pharmaceutical tetrahydropalmatine and engi-neered a yeast strain to produce it. Finally, we show that our tools can be used to mine available genome sequences to find organisms that can degrade xenobiotics. Our findings suggest that the atom-level representation of metabolism can greatly contribute to its understanding, exploration and prediction. Given the complexity of atom-level modeling of metabolic processes, we propose metrics that can approxi-mate the atom-level information to conserve the information at the level of big, hy-pothetical metabolic networks like ATLAS. This database plus the developed pathway search techniques form a valuable resource for scientist to help characterizing un-known biosynthesis pathways towards secondary metabolites, and for metabolic engi-neers for designing novel bioproduction pathways for chemicals. Hopefully, these considerations will contribute to a better understanding of metabolism, advance the exploration of the bioproduction of drugs and other valuable molecules, and acceler-ate metabolic engineering efforts to realize the switch from a petroleum-based chem-ical industry towards a more sustainable, bio-based production of societyâs chemical needs.

EPFL2020

Chargement

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Genome-scale metabolic reconstructions capture the known metabolic capabilities of organisms and their analysis have provided many insights into complex biochemical networks. However, this approach relies on a database of known biochemical reactions and hence may fail to take into account unique novel metabolic reactions. Even for well-characterized model organisms like E. coli, new reactions remain to be discovered. Starting from the known biochemistry of E. coli metabolism, we investigated how many more reactions could be evolved and if these reactions could give extra potential to this organism for industrial application in the production of fuels and chemicals. We utilized the BNICE (Biochemical Network Integrated Computational Explorer) methodology which applies known biotransformation rules to generate a “super” network which captures all the possible reactions given a set of E. coli core metabolites and known biotransformation rules in E. coli. This super network is found to capture all the known E. coli reactions as well as novel pathways that can serve as potential novel biosynthesis pathways to valuable chemicals. In order to identify the role of these novel reactions in E. coli metabolic network, we embedded them within the genome scale model of E. coli and performed a thermodynamics flux balance analysis (TFBA) to investigate the feasibility of the novel pathways in the context of the genome scale model. By performing a Flux Variability Analysis (FVA), subject to thermodynamic constraints, the reliable sets of reactions that increase the yield through biomass were identified. To further investigate the feasibility of the novel proposed reactions, we used the BridgIT computational framework for the identification of candidate gene sequences. BridgIT has a database of all the known biochemical reactions and it makes associations between novel predicted reactions and known reactions by using a chemical similarity metrics and it links the novel reactions with known genes in genomes and organisms.

2014

Chargement

“ATLAS of Biochemistry”: A repository of all possible biochemical reactions for synthetic biology, metabolic engineering and metabolomics studies

Noushin Hadadi, Jasmin Maria Hafner, Vassily Hatzimanikatis, Adrian Shajkofci, Aikaterini Zisaki

Recent technical and analytical progress in the field of metabolomics has lead to the identification of vast amounts of new compounds in living organisms. However, the integration of these compounds into the context of known metabolism remains difficult. To address this challenge of incomplete knowledge, we propose a computational approach that identifies novel hypothetical reactions between known metabolites, integrates experimentally measured molecular structures into existing metabolic networks, and finally predicts chemical compounds that are probable to exist in metabolism. The computational framework BNICE.ch is used to exploit the known biochemistry contained in the Kyoto Encyclopedia of Genes and Genomes (KEGG). We summarize the vastly diverse functionalities of enzymatic reactions in a few hundred expert-curated reaction rules, each generalizing multiple biochemical reactions. We then apply these rules to all metabolites known to KEGG in order to create a database of all the biochemically plausible reactions between compounds reported to occur in living organism. This extrapolation of the known metabolism results in a network of more than 130’000 known and novel reactions, each connecting two or more KEGG compounds. The generated information has been organized into an online database, the “Atlas of Biochemistry”, and is available under http://lcsb-databases.epfl.ch/atlas/.

2016

Chargement

Modeling, predicting and mining metabolism at atom-level resolution

Jasmin Maria Hafner

EPFL2020

, ,

2014