The Road Not Taken: exploring possible and impossible designs and strategies in a Long-Term Evolution Experiment

View all posters

Rohan Maddamsetti, Michael J. Wiser, J. Jeffrey Morris, Nathan I. Johns, and Richard E. Lenski

Michigan State University, United States

Adaptation by natural selection is constrained by the available supply of beneficial mutations. Tension exists between finding the best mutation accessible locally, and finding the best mutation existing globally. If a “design” is a suite of mutations making up a successful genotype, how far away are the designs realized in evolution from the best possible design? Populations may not evolve toward the same optimum if alternate strategies exist for continued survival. Furthermore, successful strategies may depend on the strategies played by other members of the population. This type of dynamic is called frequency-dependent selection. For instance, bacteria that specialize on a resource produced by another type of bacteria can co-exist indefinitely, if both types have an advantage over the other when rare. Researchers in our laboratory have evolved 12 lines of Escherichia coli in glucose-limited media for 25 years. This experiment spans over 57,000 generations of bacterial evolution. I present a result in which a nadR mutation that arose in a glucose specialist was engineered into the genome of a competing frequency-dependent strain. As nadR has mutated in many of the 12 lines, I ask whether this globally beneficial mutation can fail in the context of an alternate evolutionary strategy. Second, I present results in which we swapped three parallel mutations in the master regulator spoT among the lineages in which they evolved. Is there a globally superior spoT allele? If so, how far were the realized evolutionary designs from the best possible design? If not, how much do these beneficial mutations depend on the rest of the evolved genetic network? Answering these questions have implications both for designing novel genetic networks, as well as for understanding the variety of ways in which synthetic genetic networks may evolve either toward, or away from a given objective.