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

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/.

ATLAS of Biochemistry: A Repository of All Possible Biochemical Reactions for Synthetic Biology and Metabolic Engineering Studies

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

Because the complexity of metabolism cannot be intuitively understood or analyzed, computational methods are indispensable for studying biochemistry and deepening our understanding of cellular metabolism to promote new discoveries. We used the computational framework BNICE.ch along with cheminformatic tools to assemble the whole theoretical reactome from the known metabolome through expansion of the known biochemistry presented in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. We constructed the ATLAS of Biochemistry, a database of all theoretical biochemical reactions based on known biochemical principles and compounds. ATLAS includes more than 130 000 hypothetical enzymatic reactions that KEGG connect two or more KEGG metabolites through novel enzymatic reactions that have never been reported to occur in living organisms. Moreover, ATLAS reactions integrate 42% of KEGG metabolites that are not currently present in any KEGG reaction into one or more novel enzymatic reactions. The generated repository of information is organized in a Web-based database (http://lcsb-databases.epfl.ch/atlas/) that allows the user to search for all possible routes from any substrate compound to any product. The resulting pathways involve known and novel enzymatic steps that may indicate unidentified enzymatic activities and provide potential targets for protein engineering. Our approach of introducing novel biochemistry into pathway design and associated databases will be important for synthetic biology and metabolic engineering.

Amer Chemical Soc2016

Exploring chemodiversity in metabolism towards the selective integration of chemistry into biology

Noushin Hadadi, Jasmin Maria Hafner, Vassily Hatzimanikatis

The availability of different levels of omics data helps us to observe cells with higher resolution and from different perspectives. Consequently, the computational exploration of metabolism gained more importance in the last decade to make sense of newly available data from genomics, transcriptomics and metabolomics. However, complete understanding of metabolism lags behind in explaining the chemodiversity observed in living organisms – the known reactome does not account for the appearance of many metabolites. Integrating experimentally measured metabolites into existing metabolic knowledge is a challenge we address here. We extrapolate the known metabolism towards the chemical knowledge space and we selectively integrate chemical compounds and their associated reactions into an overall network of known and potential metabolism we call the “ATLAS of Biochemistry.” We apply the computational tool BNICE.ch to generate known and novel reactions and compounds using expert curated, generalized enzyme reaction rules, and we created the first released version of ATLAS which contains all possible reactions (known and hypothetical) between known biological compounds. We further demonstrate that the selective integration of chemicals into metabolic networks is the key to complete the mechanism of poorly characterized reactions and to integrate orphan metabolites into metabolic networks. Starting with 16’000 biological compounds, we found biochemical reactions which include 60’000 unique PubChem compounds one reaction step away from known metabolism, and 140’000 PubChem compounds two reaction steps away. We organized our findings in an online database (http://lcsb-databases.epfl.ch/atlas) which is equipped with additional data analysis tools. As an example, results from a pathway search can propose previously unidentified enzymatic activities, bridge gaps in metabolic models and provide potential targets for protein and metabolic engineering. The data can further be used to create hypotheses about the origin of experimentally measured compounds and, in general, serve as a tool for metabolic engineers, synthetic biologists and other scientists working with metabolomics and secondary metabolism.

2017

Kinetic reconstruction and analysis of sphingolipid metabolism

Vassily Hatzimanikatis, Howard Riezman, Georgios Savoglidis

Lipids are major constituents of the cell. They are responsible for major properties of the cellular membranes: hydrophobicity, selective permeability and being the scaffold of signaling proteins. Many diseases are associated with alterations in the lipid distribution in the cell and the composition of membrane domains. Metabolic syndrome, obesity, atherosclerosis, as well as Alzheimer’s, Huntington’s diseases and cancer, have an impact in the levels of lipids, with observed alterations in their concentrations compared to the healthy state. Applying computational techniques along with systematic modeling of lipid metabolism can provide insights that can guide biomedical research and develop potential strategies for prevention and cure. In the present study we developed a comprehensive model of the sphingolipid biosynthesis in the yeast Saccharomyces cerevisiae. Sphingolipids are one of the four major lipid categories, along with (glycero)phospholipids, sterols and fatty acids synthesized in the yeast S. cerevisiae. The importance of sphingolipids present in any higher eukaryote has been demonstrated in many recent studies. For this study, S. cerevisiae has been chosen as a model organism due to its high homology of cellular processes with mammalian cells. The developed model will be an essential part towards the construction of a detailed kinetic model of the whole lipidome of the cell. We first constructed a stoichiometric model that contains all the currently known reactions for the biosynthesis of ceramides and complex sphingolipids in the yeast. Additionally, we have accounted for all five reported hydroxylation states, along with the reactions synthesizing the necessary precursor metabolites from other lipid pathways (i.e. palmitate-CoA from fatty acid synthesis and phosphatidylinositol from the phospholipids metabolism). We next developed a kinetic model and we used a large number of lipidomic measurements of wild type yeast to consistently calibrate our model. Curation of the kinetic information of the model came from the comprehensive mining of references for operation of the enzymes as well as ranges of kinetic parameters from online databases. These datasets created the pool for a sampling technique that accounts for the uncertainty in the parameters of the model. This lead to a robust dynamic model, containing mass balances for all the components of the biochemical network as well as terms that accounted for the dillution of these molecules in the cell due to growth. We performed a thorough kinetic analysis of the system by examining the impact of different assumptions in enzyme operation on the levels of ceramides and complex sphingolipids (e.g. substrate competition, (un)competitive inhibition). By applying the principles of Metabolic Control Analysis (MCA) we were able to quantify the effect of enzyme activities on the lipid profiles and we identified enzymes of the biochemical reaction network as efficient targets for metabolic engineering towards a desired state. We also applied a reverse engineering method and we were able to identify enzyme perturbations responsible for an observed altered state compared to the wild type cellular lipidomic profile. Such an approach could lead to the identification of genetic mutations by imploring the information containd within metabolomic measurements. Although we demonstrate the application of the method in models of lipid metabolism it is possible to be applied to biochemical systems of various parts of metabolism and sizes creating a modular platform for kinetic analysis of cellular operations.