Phenotypic innovation through recombination in genome-scale metabolic networks

Hosseini, Sayed-Rzgar; Martin, O. C.; Wagner, Andreas

doi:10.1098/rspb.2016.1536

Cited by 13 publications

(11 citation statements)

References 67 publications

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“…EGCs not only affect the accurate representation of existing metabolic systems. They will be particularly problematic in evolutionary simulations that involve the incorporation of foreign metabolic reactions from other species [ 28 – 30 ]. Such mixing of reactions from disparate model reconstructions may easily introduce EGCs, and may thus lead to erroneous phenotype predictions.…”

Section: Resultsmentioning

confidence: 99%

Erroneous energy-generating cycles in published genome scale metabolic networks: Identification and removal

et al. 2017

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Energy metabolism is central to cellular biology. Thus, genome-scale models of heterotrophic unicellular species must account appropriately for the utilization of external nutrients to synthesize energy metabolites such as ATP. However, metabolic models designed for flux-balance analysis (FBA) may contain thermodynamically impossible energy-generating cycles: without nutrient consumption, these models are still capable of charging energy metabolites (such as ADP→ATP or NADP+→NADPH). Here, we show that energy-generating cycles occur in over 85% of metabolic models without extensive manual curation, such as those contained in the ModelSEED and MetaNetX databases; in contrast, such cycles are rare in the manually curated models of the BiGG database. Energy generating cycles may represent model errors, e.g., erroneous assumptions on reaction reversibilities. Alternatively, part of the cycle may be thermodynamically feasible in one environment, while the remainder is thermodynamically feasible in another environment; as standard FBA does not account for thermodynamics, combining these into an FBA model allows erroneous energy generation. The presence of energy-generating cycles typically inflates maximal biomass production rates by 25%, and may lead to biases in evolutionary simulations. We present efficient computational methods (i) to identify energy generating cycles, using FBA, and (ii) to identify minimal sets of model changes that eliminate them, using a variant of the GlobalFit algorithm.

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Section: Resultsmentioning

confidence: 99%

Erroneous energy-generating cycles in published genome scale metabolic networks: Identification and removal

et al. 2017

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“…First, recombination is a ubiquitous force of genetic change, not only in eukaryotes but also in prokaryotes whose genomes are being continually reorganized through horizontal gene transfer. Second, in contrast to smaller-scale genetic change, such as point mutations, recombination causes larger-scale genetic change with greater potential to create novel phenotypes (27)(28)(29)(30)(31)(32). Thus, if we found that phenotypic evolution was constrained when recombination causes genotypic change, it would be even more constrained if point mutations caused such change.…”

Section: Introductionmentioning

confidence: 87%

“…transferred from donor to recipient, and that can lead to metabolic innovation. For this analysis, it is relevant that the majority of metabolic innovations is caused by the transfer of a single key reaction (32). We analyzed transferable reactions in greater depth, focusing on all 1000 parental donor metabolic networks viable on a given carbon source C i , and on the (D/2 ¼ 50) reactions that are present in the donor metabolic network, but are absent in the recipient, and so can potentially be transferred from the donor to the recipient.…”

Section: On the Underlying Causes Of Constraints And Contingenciesmentioning

confidence: 99%

Constraint and Contingency Pervade the Emergence of Novel Phenotypes in Complex Metabolic Systems

Hosseini

Wagner

2017

Biophysical Journal

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An evolutionary constraint is a bias or limitation in phenotypic variation that a biological system produces. We know examples of such constraints, but we have no systematic understanding about their extent and causes for any one biological system. We here study metabolisms, genomically encoded complex networks of enzyme-catalyzed biochemical reactions, and the constraints they experience in bringing forth novel phenotypes that allow survival on novel carbon sources. Our computational approach does not limit us to analyzing constrained variation in any one organism, but allows us to quantify constraints experienced by any metabolism. Specifically, we study metabolisms that are viable on one of 50 different carbon sources, and quantify how readily alterations of their chemical reactions create the ability to survive on a novel carbon source. We find that some metabolic phenotypes are much less likely to originate than others. For example, metabolisms viable on D-glucose are 1835 times more likely to give rise to metabolisms viable on D-fructose than on acetate. Likewise, we observe that some novel metabolic phenotypes are more contingent on parental phenotypes than others. Biochemical similarities among carbon sources can help explain the causes of these constraints. In addition, we study metabolisms that can be produced by recombination among 55 metabolisms of different bacterial strains or species, and show that their novel phenotypes are also contingent on and constrained by parental genotypes. To our knowledge, our analysis is the first to systematically quantify the incidence of constrained evolution in a broad class of biological system that is central to life and its evolution.

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“…While an experimental assessment of the landscape of possible variants of metabolism with different coenzyme systems would be extremely challenging, one can leverage theoretical and computational approaches to help address these questions. The recent development and application of metabolic modeling has brought a quantitative and model-driven approach to the study of metabolic evolution (17)(18)(19)(20)(21), and has led to uncovering optimality criteria that may have shaped universal features of core metabolism, such as the emergence of both NAD(H) and NADP(H) (22). Although recent quantitative work has explored the logic for why NAD(P)H emerged as a prominent coenzyme in biochemistry (23), the forces that drove the emergence of both NAD(H) and NADP(H), rather than just a single coenzyme, remain unclear.…”

Section: Introductionmentioning

confidence: 99%

Protein cost minimization promotes the emergence of coenzyme redundancy

Goldford

George

Flamholz

et al. 2021

Preprint

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Coenzymes redistribute a variety of resources (e.g., electrons, phosphate groups, methyl groups) throughout cellular metabolism. For a variety of reactions requiring acceptors or donors of specific resources, there often exist degenerate sets of molecules (e.g., NAD(H) and NADP(H)) that carry out similar functions. Several hypotheses can explain the persistence of coenzyme degeneracy, but none have been tested quantitatively. Here, we use genome-wide metabolic modeling approaches to decompose the selective pressures driving enzymatic specificity for coenzymes in the metabolic network of Escherichia coli. Flux balance modeling predicts that only two enzymes (encoded by leuB and pdxB) are thermodynamically constrained to use NAD(H) over NADP(H). In contrast, structural and sequence analyses reveal widespread conservation of residues that retain selectivity for NAD(H), suggesting that additional forces drive enzyme specificity. Using a model accounting for the cost of oxidoreductase enzyme expression, we find that coenzyme redundancy universally reduces the minimal amount of protein required to catalyze coenzyme-coupled reactions, inducing individual reactions to strongly prefer one coenzyme over another when reactions are near thermodynamic equilibrium. We propose that partitioning of flux across multiple coenzyme pools could be a generic phenomenon of cellular metabolism, and hypothesize that coenzymes typically thought to exist in a single pool (e.g., CoA) may exist in more than one form (e.g., dephospho-CoA).

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Phenotypic innovation through recombination in genome-scale metabolic networks

Cited by 13 publications

References 67 publications

Erroneous energy-generating cycles in published genome scale metabolic networks: Identification and removal

Erroneous energy-generating cycles in published genome scale metabolic networks: Identification and removal

Constraint and Contingency Pervade the Emergence of Novel Phenotypes in Complex Metabolic Systems

Protein cost minimization promotes the emergence of coenzyme redundancy

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