From: Cost of Antibiotic Resistance and the Geometry of Adaptation

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From: Cost of Antibiotic Resistance and the Geometry of Adaptation FIG. 1. Mean selection coefficient of arising compensatory mutations relative to the distance to the optimum obtained by simulation (for details, see Materials and Methods). The mean effect of mutations at the optimum is E(s<sub>d</sub>) = −0.012 (filled symbols) or E (s<sub>d</sub>) = −0.03 (open symbols), as suggested for Escherichia coli (Kibota and Lynch 1996; Trindade et al. 2010, respectively). The mean effect of arising beneficial mutations is shown, assuming different values of the number of phenotypic traits, n, in FGM. Triangles n = 15 and squares n = 25. From: Cost of Antibiotic Resistance and the Geometry of Adaptation Mol Biol Evol. 2011;29(5):1417-1428. doi:10.1093/molbev/msr302 Mol Biol Evol | © The Author(s) 2011 Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

From: Cost of Antibiotic Resistance and the Geometry of Adaptation FIG. 2. Adaptation to a fitness peak. Fitness increased (relative to the ancestral strain) about 3% in the first 1,400 generations and reached a plateau thereafter. The clone used for the compensatory evolution was picked at generation 1,400. The error bars denote two times the SE. The mean at generation 184 is not significantly different from that at generation 0. From: Cost of Antibiotic Resistance and the Geometry of Adaptation Mol Biol Evol. 2011;29(5):1417-1428. doi:10.1093/molbev/msr302 Mol Biol Evol | © The Author(s) 2011 Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

From: Cost of Antibiotic Resistance and the Geometry of Adaptation FIG. 3. Dynamics of adaptation for compensation to the costs of antibiotic resistance. The changes in frequency across generations of the different microsatellite markers (number of GA repeats from 20 to 35). (a) Example of a population carrying resistance allele K43N where a strongly beneficial mutation is inferred; (b) Example of a population carrying resistance allele K43N where two beneficial mutations occurring in different genetic backgrounds are detected; (c) Example of a population carrying resistance allele K88E where the occurrence of at least two beneficial mutations in the same clone can be inferred (note that the two distinct frequency increases can be seen in the first 23 generations and then between generation 46 and 69). From: Cost of Antibiotic Resistance and the Geometry of Adaptation Mol Biol Evol. 2011;29(5):1417-1428. doi:10.1093/molbev/msr302 Mol Biol Evol | © The Author(s) 2011 Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

From: Cost of Antibiotic Resistance and the Geometry of Adaptation FIG. 4. The distribution of compensatory contending mutations, f(s). (a) Close to the optimum, starting genotype carrying resistance allele K43N; (b) far from the optimum, starting genotype carrying resistance allele K88E. The distributions are significantly different Kolmogorov–Smirnov test P < 0.001. From: Cost of Antibiotic Resistance and the Geometry of Adaptation Mol Biol Evol. 2011;29(5):1417-1428. doi:10.1093/molbev/msr302 Mol Biol Evol | © The Author(s) 2011 Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

From: Cost of Antibiotic Resistance and the Geometry of Adaptation FIG. 5. Distribution of numbers of contending mutations. Frequency of populations, out of 30, where a given number of mutations (1–6) were detected. Dark gray bars stand for the K43N mutation, whereas light gray bars represent the K88E mutation. From: Cost of Antibiotic Resistance and the Geometry of Adaptation Mol Biol Evol. 2011;29(5):1417-1428. doi:10.1093/molbev/msr302 Mol Biol Evol | © The Author(s) 2011 Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

From: Cost of Antibiotic Resistance and the Geometry of Adaptation FIG. 6. Estimation of the mutation rate to compensatory mutations. (a) Refers to K43N resistance background and (b) to K88E resistance background. The figure shows the range of U<sub>a</sub> compatible with the mean number of observed mutations and the variation of the scale parameter of the beta distribution obtained by maximum likelihood estimates (see table 2). The gray shading is proportional to the number of mutations calculated using Equation (2) in the Materials and Methods (the darker the shading the lower the number of mutations), ranging from 2.4 to 3.1 for the K43N mutation (which corresponds mean ± SE in fig. 5) and from 3.2 to 3.5 for K88E mutation (which corresponds mean ± SE in fig. 5). See Materials and Methods and Gerrish and Lenski (1998) and Gerrish (2001) for the details of the estimation procedure. From: Cost of Antibiotic Resistance and the Geometry of Adaptation Mol Biol Evol. 2011;29(5):1417-1428. doi:10.1093/molbev/msr302 Mol Biol Evol | © The Author(s) 2011 Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.