Optimality and evolution of transcriptionally regulated gene expression

<p>Abstract</p> <p>Background</p> <p>How transcriptionally regulated gene expression evolves under natural selection is an open question. The cost and benefit of gene expression are the driving factors. While the former can be determined by gratuitous induction, the latter is difficult to measure directly.</p> <p>Results</p> <p>We addressed this problem by decoupling the regulatory and metabolic function of the <it>Escherichia coli lac </it>system, using an inducer that cannot be metabolized and a carbon source that does not induce. Growth rate measurements directly identified the induced expression level that maximizes the metabolism benefits minus the protein production costs, without relying on models. Using these results, we established a controlled mismatch between sensing and metabolism, resulting in sub-optimal transcriptional regulation with the potential to improve by evolution. Next, we tested the evolutionary response by serial transfer. Constant environments showed cells evolving to the predicted expression optimum. Phenotypes with decreased expression emerged several hundred generations later than phenotypes with increased expression, indicating a higher genetic accessibility of the latter. Environments alternating between low and high expression demands resulted in overall rather than differential changes in expression, which is explained by the concave shape of the cross-environmental tradeoff curve that limits the selective advantage of altering the regulatory response.</p> <p>Conclusions</p> <p>This work indicates that the decoupling of regulatory and metabolic functions allows one to directly measure the costs and benefits that underlie the natural selection of gene regulation. Regulated gene expression is shown to evolve within several hundreds of generations to optima that are predicted by these costs and benefits. The results provide a step towards a quantitative understanding of the adaptive origins of regulatory systems.</p

Effective polyploidy causes phenotypic delay and influences bacterial evolvability

Whether mutations in bacteria exhibit a noticeable delay before expressing their corresponding mutant phenotype was discussed intensively in the 1940s to 1950s, but the discussion eventually waned for lack of supportive evidence and perceived incompatibility with observed mutant distributions in fluctuation tests. Phenotypic delay in bacteria is widely assumed to be negligible, despite the lack of direct evidence. Here, we revisited the question using recombineering to introduce antibiotic resistance mutations into E. coli at defined time points and then tracking expression of the corresponding mutant phenotype over time. Contrary to previous assumptions, we found a substantial median phenotypic delay of three to four generations. We provided evidence that the primary source of this delay is multifork replication causing cells to be effectively polyploid, whereby wild-type gene copies transiently mask the phenotype of recessive mutant gene copies in the same cell. Using modeling and simulation methods, we explored the consequences of effective polyploidy for mutation rate estimation by fluctuation tests and sequencing-based methods. For recessive mutations, despite the substantial phenotypic delay, the per-copy or per-genome mutation rate is accurately estimated. However, the per-cell rate cannot be estimated by existing methods. Finally, with a mathematical model, we showed that effective polyploidy increases the frequency of costly recessive mutations in the standing genetic variation (SGV), and thus their potential contribution to evolutionary adaptation, while drastically reducing the chance that de novo recessive mutations can rescue populations facing a harsh environmental change such as antibiotic treatment. Overall, we have identified phenotypic delay and effective polyploidy as previously overlooked but essential components in bacterial evolvability, including antibiotic resistance evolution

Divergence Process, Fitness Criterion, and Mutational Dataset of Repression Values

<div><p>(A) Diagram of the studied divergence process: after a duplication event, a new regulatory interaction can be formed by mutating the two operators, O1 and O2, and two repressors, R1 and R2.</p><p>(B) Duplication and divergence yields heterodimers, which can all bind to the operator. The (initially symmetric) operators and repressors are based on the <i>lac</i> sequence, as indicated. Base pairs that are key to altering specificity (colored red and blue) can be mutated to arbitrary sequence.</p><p>(C) The selective pressure for independent regulation follows from four input conditions that contribute to the total fitness. When, e.g., R1 is high and R2 low, this implies that X should be low and Y high. Out of all interaction parameters of the network, in this case only <i>F</i>_O1R1 and (<i>F</i>_O2R1)⁻¹ are relevant and need to be optimized. When R1 and R2 are high, both X and Y should be low, regardless of which repressor-dimer causes repression. Therefore max(<i>F</i>_O1) (the strongest interaction with O1 by either homodimers of R1 or R2 or by the heterodimer of R1 and R2) and max(<i>F</i>_O2) need to be be optimized. When both R1 and R2 are low, no parameters need to be optimized.</p><p>(D) Resulting repression value landscape, showing repression values based on actual measurements of mutants.</p></div

Typical Divergence Pathway: Network Changes, Fitness, and Sequence

<div><p>(A) Evolving interaction network, where line thickness denotes binding strength between repressor monomer and operator-half. Dotted lines denote negligible repression. Yellow crosses indicate repressor and operator mutations, which are positioned at the top and bottom of the interaction lines respectively.</p><p>(B) Fitness trajectory (black) and corresponding repression of each repressor on its operator (red and blue). Fitness is normalized to the maximum value (~1 ×10¹⁰).</p><p>(C) Sequences for each round. Mutated positions are colored white.</p></div

Divergence Success Ratio and Path Length Distributions

<div><p>(A) Fraction of starting sequences (numbering 132 in total) that successfully diverge, as a function of the number of networks carried to the next round <i>(L).</i> Dashed line, idem, but with the additional requirement of continued tight binding (<i>F</i> ≥ 100) for both repressors.</p><p>(B) Distribution of path lengths until divergence. Red color map, optimal co-divergence pathways. Blue color map, pathways with the additional requirement of <i>F</i> ≥ 100 for both repressors. Note that a vertical summation of the color maps yields the lines in (A).</p></div

Analysis of Pathway Detours and Local Environment of Fitness Optima

<div><p>(A) Histogram showing the number of detour mutations of the divergence pathways. The Hamming distance <i>d_H</i> of two sequences is defined as the number of positions at which they have different base pairs. Paths that are longer than <i>d_H</i> arrive at an optimum after a detour.</p><p>(B) Histogram of the Hamming distance between the optimum that is found and the closest optimum. If this measure is zero, a path leads to the closest optimum.</p><p>(C) Median fitness value as a function of the Hamming distance from a global optimum (solid line). Gray levels indicate the spread of the fitness values.</p></div

Short-range interactions govern the dynamics and functions of microbial communities

ISSN:2397-334

Data from: Effective polyploidy causes phenotypic delay and influences bacterial evolvability

Whether mutations in bacteria exhibit a noticeable delay before expressing their corresponding mutant phenotype was discussed intensively in the 1940s to 1950s, but the discussion eventually waned for lack of supportive evidence and perceived incompatibility with observed mutant distributions in fluctuation tests. Phenotypic delay in bacteria is widely assumed to be negligible, despite the lack of direct evidence. Here, we revisited the question using recombineering to introduce antibiotic resistance mutations into E. coli at defined time points and then tracking expression of the corresponding mutant phenotype over time. Contrary to previous assumptions, we found a substantial median phenotypic delay of three to four generations. We provided evidence that the primary source of this delay is multifork replication causing cells to be effectively polyploid, whereby wild-type gene copies transiently mask the phenotype of recessive mutant gene copies in the same cell. Using modeling and simulation methods, we explored the consequences of effective polyploidy for mutation rate estimation by fluctuation tests and sequencing-based methods. For recessive mutations, despite the substantial phenotypic delay, the per-copy or per-genome mutation rate is accurately estimated. However, the per-cell rate cannot be estimated by existing methods. Finally, with a mathematical model, we showed that effective polyploidy increases the frequency of costly recessive mutations in the standing genetic variation (SGV), and thus their potential contribution to evolutionary adaptation, while drastically reducing the chance that de novo recessive mutations can rescue populations facing a harsh environmental change such as antibiotic treatment. Overall, we have identified phenotypic delay and effective polyploidy as previously overlooked but essential components in bacterial evolvability, including antibiotic resistance evolution