Abstract
Computational predictions of double gene knockout effects by flux balance analysis (FBA) have been used to characterized genome-wide patterns of epistasis in microorganisms. However, it is unclear how in silico predictions are related to in vivo epistasis, as FBA predicted only a minority of experimentally observed genetic interactions between non-essential metabolic genes in yeast. Here, we perform a detailed comparison of yeast experimental epistasis data to predictions generated with different constraint-based metabolic modeling algorithms. The tested methods comprise standard FBA; a variant of MOMA, which was specifically designed to predict fitness effects of non-essential gene knockouts; and two alternative implementations of FBA with macro-molecular crowding, which account approximately for enzyme kinetics. The number of interactions uniquely predicted by one method is typically larger than its overlap with any alternative method. Only 20% of negative and 10% of positive interactions jointly predicted by all methods are confirmed by the experimental data; almost all unique predictions appear to be false. More than two thirds of epistatic interactions are undetectable by any of the tested methods. The low prediction accuracies indicate that the physiology of yeast double metabolic gene knockouts is dominated by processes not captured by current constraint-based analysis methods.
Highlights
Epistasis measures the extent to which the consequences of a mutation in one gene depend on mutations in another gene[1]
For each pair of non-essential genes contained in the metabolic model, we calculated Epistasis (Eq (1)) based on four methods: (i) standard flux balance analysis[35,36] (FBA); (ii) a linear version of minimization of metabolic adjustment[37] that finds the knockout flux distribution most similar to the parsimonious FBA (pFBA) prediction for the wildtype flux vector; (iii) metabolic modelling with enzyme kinetics[43], an implementation of FBA with molecular crowding that approximately accounts for enzyme kinetics (MOMENT); and (iv) a modified implementation of MOMENT with a more realistic consideration of multifunctional enzymes[51,52]
The consideration of molecular crowding seems to have a strong effect on the epistasis predictions, as the results seem to fall into two clusters. 49% of negative and 83% of positive epistasis predictions by FBA are predicted by lMOMA, while 60% of negative and 65% of positive interactions predicted by MOMENT are predicted by Cost-constrained FBA (ccFBA); the remaining pairs of methods show much smaller agreement (Fig. 2a,b)
Summary
Epistasis measures the extent to which the consequences of a mutation in one gene depend on mutations in another gene[1]. Www.nature.com/scientificreports minimize the difference between wild-type and knockout distributions of metabolic reaction rates (minimization of metabolic adjustment, MOMA37) Several studies used these simulation methods to perform large-scale characterizations of epistasis in silico. Segrè et al first used FBA to study the spectrum of epistatic interactions between metabolic genes in S. cerevisiae[27] These authors introduced a new concept of epistasis between functional modules rather than between individual genes, intended to describe functional relationships among metabolic pathways. They found that modules interact with each other ‘monochromatically’, i.e., epistatic interactions between two specific modules are either largely positive or largely negative[27]. Examining the metabolic networks of E. coli and S. cerevisiae, He et al.[28] found negative epistatic interactions largely among nonessential reactions with overlapping functions; in contrast, positive interactions were found predominantly between reactions without overlapping functions, and these were frequently essential[28]
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