Modeling central metabolism and energy biosynthesis across microbial life

Abstract

Background: Automatically 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.

Results: To 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.

Conclusions: We 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.

Fig. 1

Core metabolic model construction pipeline. The pipeline starts with gene annotations provided by RAST annotation pipeline of assembled microbial genomes. Next, the CMMs are constructed based on a manually curated CMT that consists of biochemical reactions derived from phylogenetically diverse set of model organisms including Escherichia coli, Bacillus. subtilis, Pseudomonas aeroginosa, Clostridium acetobutylicum, and Paracococcus denitrificans. In the final step, FBA is performed optimizing the biomass or ATP hydrolysis as the objective function

Fig. 2

Phylogenetic distribution of CMM pathways and pathway co-occurrence in central metabolism. Presence and absence of 12 key pathways related to energy metabolism including glucose oxidation pathways (glycolysis, ED, TCA cycle, and pentose phosphate) and fermentation pathways (lactate, acetate, formate, ethanol, 2,3-butanediol, butyrate, butanol, and acetone) computed using Boolean rules. Taxonomic groups that are displayed in the horizontal axis of the graph were sorted sequentially as they appear in a 16 s rRNA based phylogenetic tree. The distribution patterns of these key pathways among major phylogenetic groups and pairwise comparisons of pathway presence or absence shows that most pathways are positively correlated. Blue pie slices show comparisons with positive correlations in the clockwise direction while red pie slices show negative relationships in the counterclockwise direction. Relationships shown in pies outlined in bold were consistent across all size classes. Increasing strength of correlation is denoted by increased pie slice size as well as color intensity. Empty pies are relationships that are not significant at p < 0.05. Filled pies are self-comparisons

Fig. 3

Predictions of ATP yields using FBA on selected core models. The ATP yield predictions were simulated in presence of aerobic, anaerobic electron acceptors (nitrate, TMAO) and without any electron acceptors. Glucose or glycerol was used as the carbon source. Labeled bars show the mmol of ATP/mmol of glucose/glycerol for Escherichia coli K12 and Clostridium botulinum A str. ATCC 3502. ATP hydrolysis is used as the OF for FBA simulations

Fig. 4

Number of gapfilled reactions that are required in CMMs in order to produce all biomass precursors. Blue bars represent the gene-associated reactions and the red bars represent the gapfilled reactions for all CMMs used in this study. The height of the bars represents the number of reactions. CMMs are grouped according to taxonomical groups