Chance and necessity in the evolution of minimal metabolic networks.
It is possible to infer aspects of an organism's lifestyle from its gene content. Can the reverse also be done? Here we consider this issue by modelling evolution of the reduced genomes of endosymbiotic bacteria. The diversity of gene content in these bacteria may reflect both variation in selective forces and contingency-dependent loss of alternative pathways. Using an in silico representation of the metabolic network of Escherichia coli, we examine the role of contingency by repeatedly simulating the successive loss of genes while controlling for the environment. The minimal networks that result are variable in both gene content and number. Partially different metabolisms can thus evolve owing to contingency alone. The simulation outcomes do preserve a core metabolism, however, which is over-represented in strict intracellular bacteria. Moreover, differences between minimal networks based on lifestyle are predictable: by simulating their respective environmental conditions, we can model evolution of the gene content in Buchnera aphidicola and Wigglesworthia glossinidia with over 80% accuracy. We conclude that, at least for the particular cases considered here, gene content of an organism can be predicted with knowledge of its distant ancestors and its current lifestyle.
Pál
Csaba
C
European Molecular Biology Laboratory, Meyerhofstrasse 1, D-69012 Heidelberg, Germany.
Papp
Balázs
B
Lercher
Martin J
MJ
Csermely
Péter
P
Oliver
Stephen G
SG
Hurst
Laurence D
LD
D005075:Biological Evolution; D020635:Buchnera / Q000235:genetics; D019295:Computational Biology; D048168:Escherichia coli K12 / Q000201:enzymology / Q000235:genetics* / Q000378:metabolism*; D029968:Escherichia coli Proteins / Q000235:genetics / Q000378:metabolism; D022761:Gene Transfer, Horizontal / Q000235:genetics; D005798:Genes, Bacterial / Q000235:genetics; D016680:Genome, Bacterial; D013559:Symbiosis / Q000235:genetics; D044106:Wigglesworthia / Q000235:genetics
D029968:Escherichia coli Proteins