A novel approach to find the missing links in genome-scale metabolic models: The BridgeIt mrthod

Genome-scale metabolic reconstructions (GSMRs) are valuable resources in the analysis and understanding of cellular metabolism. They are based on genome sequence and annotation, and they are to develop bottom-up mathematical models of metabolic networks. These models are used in a wide variety of studies ranging from metabolic engineering to evolutionary studies. However, there are incomplete pathways and orphan metabolites in all GSMRs, even for the most well studied organisms. These knowledge gaps are due to the lack of experimental or homologous information, as current methods rely on a database of known reactions to generate possible pathways for bridging these gaps, and they fall short when there is no sequence homology. We present a novel computational framework called BridgIT that is able to generate hypothetical reactions and pathways that bridge gaps in reconstructed pathways. The novel reactions generated are based the third level of enzyme commission classification system (EC), which is consistent with known biochemical reactions, protein structures, genomic sequences, and enzyme properties that follow the EC classification. Within the BridgIT framework, we generate all biochemically plausible reactions and pathways, which can link two or more metabolites. These pathways are then ranked according to their length, thermodynamic feasibility, and network feasibility. We next use chemical similarity metrics to link the generated hypothetical reactions with known reactions through their substrate and product similarity. The protein and gene sequences of the linked known reactions are used to identify possible sequences within the GSMR to further refine and improve the annotation of the existing GSMR. We demonstrate the ability of this method to identify gaps that can be easily filled by known reactions and also gaps that require novel reactions which existing methods fail to do so.

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

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.

2014