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Abstract
Genome-scale metabolic models (GEMs) are comprehensive descriptions of cell metabolism and have been extensively used to understand biological responses in health and disease. One such application is in determining metabolic adaptation to the absence of a gene or reaction, i.e., essentiality analysis. However, current methods do not permit efficiently and accurately quantifying reaction/gene essentiality. Here, we present Essentiality Score Simulator (ESS), a tool for quantification of gene/reaction essentialities in GEMs. ESS quantifies and scores essentiality of each reaction/gene and their combinations based on the stoichiometric balance using synthetic lethal analysis. This method provides an option to weight metabolic models which currently rely mostly on topologic parameters, and is potentially useful to investigate the metabolic pathway differences between different organisms, cells, tissues, and/or diseases. We benchmarked the proposed method against multiple network topology parameters, and observed that our method displayed higher accuracy based on experimental evidence. In addition, we demonstrated its application in the wild-type and ldh knock-out E. coli core model, as well as two human cell lines, and revealed the changes of essentiality in metabolic pathways based on the reactions essentiality score. ESS is available without any limitation at https://sourceforge.net/projects/essentiality-score-simulator.
Highlights
Genome-scale metabolic models (GEMs) are congregations of biochemical reactions that occur in an organism or cell/tissue (Mardinoglu and Nielsen, 2015)
To demonstrate the advantage of Essentiality Score Simulator (ESS) compared to topology based method, we firstly employed the core metabolic model of E. coli with 95 reactions as a proof of concept case
There is another reaction, RPI, that is missed by betweenness (BC = 0), and this reaction has been already experimentally validated as essential in E. coli in previous study (Neidhardt and Curtiss, 1999) which further proves the advantage of ESS compared to betweenness
Summary
Genome-scale metabolic models (GEMs) are congregations of biochemical reactions that occur in an organism or cell/tissue (Mardinoglu and Nielsen, 2015). Essentiality Scoring Method for GEMs (Uhlén et al, 2015, 2017) efforts, respectively. These resources may provide information for investigating the metabolic capability with species or individual-tumor resolution, and raise the challenge of comparing and stratifying GEMs in largescale studies. EA only provides binary information about the essentiality of the reaction, and this is insufficient for complex GEMs since there would be too little essential reactions In this case, a higher degree of EA such as Synthetic Lethality Analysis (SLA) is necessary to provide additional information about non-essential reactions/genes. An option for interpretation of SLA result is using Degree of Essentiality (DoE) (Suthers et al, 2009), but DoE scores ignore the difference between reactions/genes involves in single and multiple SL combinations
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