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E volution of minimal metabolic networks. WANG Chao April 11, 2006. The diversity of these evolved minimal gene sets may be the product of three fundamental processes: . D ifferences in initial genetic makeup; V ariation in selective forces within host cells;
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Evolution of minimal metabolic networks WANG Chao April 11, 2006
The diversityof these evolved minimal gene sets may be the product of threefundamental processes: • Differences in initial genetic makeup; • Variationin selective forces within host cells; • Differences in the orderof gene deletions, resulting in a choice between alternative cellularpathways.
Using the metabolic network of Escherichia coli K12as the model system has several advantages: • The best evidence for thepresence of alternative pathways within and across species comesfrom studies of metabolic networks. • Flux balance analysisprovides a rigorous modelling framework for studying the impact ofgene deletions; the method relies on optimizing the steady-state useof the metabolic network to produce biomass components. • Not only is the metabolic network of E.coli K12 one of the beststudied cellular subsystems, but this organism is also a close relativeof several endosymbiotic organisms, including Buchnera aphidicolaandWigglesworthia glossinidia.
A simple algorithm for simulatinggradual loss of metabolic enzymes. Remove a randomly chosengene from the network and calculate the impact of this deletion onthe production rate of biomass components (a proxy for fitness). Ifthis rate is nearly unaffected, the deletion is assumed to be viable andthe enzyme is considered to be permanently lost; otherwise, the geneis restored to the network. This procedure is repeated until no furtherenzymes can be deleted; that is, all remaining genes are essential forsurvival of the cell. This simulation was repeated 500 times, with eachrunproviding an independent evolutionary outcome.
The resulting networks share on average 77% of theirreactions,whereas only 25% would be shared by randomly deleting the samenumber of genes. This suggests that both selective constraintsand historical contingencies influence the reductive evolutionof metabolic networks.
Owing to alternative metabolic pathwaysin the original E. coli network, numerous functionally equivalentminimal networks are possible, even under identical selective conditions. Distribution of the number ofcontributing genes in simulated minimal networks. Minimal reaction networks contain, on average, 245±6.48reactions; however, only 134 of these genes (~55%) have apredicted fitness effect in the full original E. coli network (arrow).
To compare the predictions against real evolutionary outcomes, divide the E. coli enzymes into two mutually exclusive groups:enzymes ubiquitously present in the simulated minimal reaction sets(group A), and enzymes absent in some or all of the simulated sets(group B). As expected, the fraction of enzymes with ubiquitous presence inthe simulated minimal reaction sets (group A) is especially high in intracellular parasites andendosymbionts as compared with freelivingmicrobes.
To investigate further how accurately the model describes reductiveevolution in nature, focus the simulations on three fullysequenced genomes of B. aphidicola strainsand W. glossinidia.These are close relatives of E. coli with an evolved intracellular endosymbiotic lifestyle. Settingboundary conditions that mimic the relevant nutrient conditionsand selective forces, performsimulations as described above. Detailed physiological studies have shown that Buchnera supplytheir aphid hosts with riboflavin and essential amino acids that arelacking in their hosts’ diets.
To quantify the agreement between the predictions and the observed reductive evolution in Buchnera, whileconsidering gene-content variation in simulated minimal genomes, use a combined measure of sensitivity and specificity. For each of the Buchnera strains, the accuracy of the model is ~80% as compared with the 50%expected by chance. The model also accuratelypredicts several non-obvious features of Buchnera genomes: forexample, the retention of particular reactions involved in oxidativephosphorylation and in pyruvate metabolism.
Consistent with the notion that genes vary widely in their propensity to be lost during reductive evolution, we find a strong correlation between the frequency of a reaction’s presence in the simulated reduced networks and its retention in Buchnera.
Metabolic pathways differ widely in their variability across simulatedminimal sets. For example, it seems thatthere is only one way of producing some key cellular (biomass)components, including compounds for cell wall synthesis and someessential amino acids. By contrast, reactions involved in pyruvatemetabolism, nucleotide salvage pathways or transport processes varyin their retention across simulations. For example, there are twodistinct pathways by which E. coli can activate acetate to acetylcoenzymeA. These two pathways have been shown experimentallyto compensate for deletions in each other in E. coli, at leastunder some nutritional conditions. Consistent with this observation,the simulated minimal reaction sets always contain only one of thetwo pathways; accordingly, Buchnera strains have retained only oneof the two pathways.
To predict gene content of an organismwith much less information on lifestyle. Wigglesworthia, anotherendosymbiont and close relative of E. coli, is an obvious choice.
Under a given selection pressure, simulated minimal reactions setsshare 82% (Wigglesworthia) and 88% (Buchnera) of their reactions,respectively. This value drops to 65% when minimal gene sets acrossdifferent models are compared. This suggests that variability in genecontent among species reflects both variation in selection pressuresand chance events in the evolutionary history of the endosymbionts. Each loss of a reaction reduces the space available for furtherreductive evolution. This is most obvious for physiologically fullycoupled reactions (such as those in linear pathways), which can onlyfulfil their metabolic function together. As predicted, members ofpairs are either lost or retained together in the investigated endosymbiontsin 74–84% of cases, whereas only ~50–55% would beexpected by chance.
Deviations between the model predictions and gene content ofendosymbionts might be due to: Incomplete biochemical knowledgeor inaccuracies in modelling the types and relative amounts ofnutrient conditions and biosynthetic components. Hosts and endosymbiontsinteract in ways that are not completely understood, and biomassproduction may be only a rough proxy for endosymbiont fitness. It seems possible to take an organism’s ecology and to predictwhich genes it should have by in silico network analysis.Moreover, wefind that evolutionary paths are contingent on prior gene deletionevents, resulting in networks that generally do not represent the mosteconomical solution in terms of the number of genes retained. Thus,history and chance seem to have significant roles not only inadaptive but also in reductive evolution of genomes.
