Abstract
BackgroundAutomatically generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Further compounding the problem, complex interlinking pathways in genome-scale metabolic models, and the need for extensive gapfilling to support complex biomass reactions, often results in predicting unrealistic yields or unrealistic physiological flux profiles.ResultsTo overcome this challenge, we developed methods and tools (http://coremodels.mcs.anl.gov) to build high quality core metabolic models (CMM) representing accurate energy biosynthesis based on a well studied, phylogenetically diverse set of model organisms. We compare these models to explore the variability of core pathways across all microbial life, and by analyzing the ability of our core models to synthesize ATP and essential biomass precursors, we evaluate the extent to which the core metabolic pathways and functional ETCs are known for all microbes. 6,600 (80 %) of our models were found to have some type of aerobic ETC, whereas 5,100 (62 %) have an anaerobic ETC, and 1,279 (15 %) do not have any ETC. Using our manually curated ETC and energy biosynthesis pathways with no gapfilling at all, we predict accurate ATP yields for nearly 5586 (70 %) of the models under aerobic and anaerobic growth conditions. This study revealed gaps in our knowledge of the central pathways that result in 2,495 (30 %) CMMs being unable to produce ATP under any of the tested conditions. We then established a methodology for the systematic identification and correction of inconsistent annotations using core metabolic models coupled with phylogenetic analysis.ConclusionsWe predict accurate energy yields based on our improved annotations in energy biosynthesis pathways and the implementation of diverse ETC reactions across the microbial tree of life. We highlighted missing annotations that were essential to energy biosynthesis in our models. We examine the diversity of these pathways across all microbial life and enable the scientific community to explore the analyses generated from this large-scale analysis of over 8000 microbial genomes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2887-8) contains supplementary material, which is available to authorized users.
Highlights
Generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC)
core metabolic models (CMM) were constructed based on a core model template (CMT) that consists of a highly curated set of biochemical reactions derived from a diverse set of model organisms
Unlike the complexity of genome scale models, CMMs are simpler, offering a quick and accurate way of determining: (i) respiration type(s) (Additional file 1: Figure S5 and Additional file 2: Table S8) and ATP yield predictions (Fig. 3), (ii) electron acceptors that can be reduced during anaerobic respiration (Additional file 2: Table S5), (iii) ability to produce useful fermentation products (Additional file 1: Figure S3 and Additional file 2: Table S4), (iv) presence/absence of functional pathways in central metabolism (Additional file 1: Figure S4 and Additional file 2: Table S4) and (v) evaluate ability to produce key pathway intermediates in central metabolism which are precursors of essential biomass compounds (Additional file 2: Table S7)
Summary
Generated bacterial metabolic models, and even some curated models, lack accuracy in predicting energy yields due to poor representation of key pathways in energy biosynthesis and the electron transport chain (ETC). Cellular energy generation in microbes is a crucial aspect of metabolic modeling, which depends on environmental factors such as carbon source, electron donor, fermentation capability, presence of electron acceptors, and variations in the electron transport chain (ETC). Metabolic models provide a valuable means for simulating and understanding energy metabolism based on annotated genome sequences [2]. Tools such as the Model SEED [3,4,5] have emerged to automate the generation of draft metabolic models to keep pace with the ever growing set of sequenced genomes.
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