Sorting out mutation rates

The population genetics of ploidy change in unicellular fungi

Changes in ploidy are a significant type of genetic variation, describing the number of chromosome sets per cell. Ploidy evolves in natural populations, clinical populations, and lab experiments, particularly in fungi. Despite a long history of theoretical work on this topic, predicting how ploidy will evolve has proven difficult, as it is often unclear why one ploidy state outperforms another. Here, we review what is known about contemporary ploidy evolution in diverse fungal species through the lens of population genetics. As with typical genetic variants, ploidy evolution depends on the rate that new ploidy states arise by mutation, natural selection on alternative ploidy states, and random genetic drift. However, ploidy variation also has unique impacts on evolution, with the potential to alter chromosomal stability, the rate and patterns of point mutation, and the nature of selection on all loci in the genome. We discuss how ploidy evolution depends on these general and unique factors and highlight areas where additional experimental evidence is required to comprehensively explain the ploidy transitions observed in the field and the lab.

Determinants of Genetic Diversity of Spontaneous Drug Resistance in Bacteria

Any pathogen population sufficiently large is expected to harbor spontaneous drug-resistant mutants, often responsible for disease relapse after antibiotic therapy. It is seldom appreciated, however, that while larger populations harbor more mutants, the abundance distribution of these mutants is expected to be markedly uneven. This is because a larger population size allows early mutants to expand for longer, exacerbating their predominance in the final mutant subpopulation. Here, we investigate the extent to which this reduction in evenness can constrain the genetic diversity of spontaneous drug resistance in bacteria. Combining theory and experiments, we show that even small variations in growth rate between resistant mutants and the wild type result in orders-of-magnitude differences in genetic diversity. Indeed, only a slight fitness advantage for the mutant is enough to keep diversity low and independent of population size. These results have important clinical implications. Genetic diversity at antibiotic resistance loci can determine a population's capacity to cope with future challenges (i.e., second-line therapy). We thus revealed an unanticipated way in which the fitness effects of antibiotic resistance can affect the evolvability of pathogens surviving a drug-induced bottleneck. This insight will assist in the fight against multidrug-resistant microbes, as well as contribute to theories aimed at predicting cancer evolution.

Estimating mutation rates in low-replication experiments

Since mutation rate is a key biological parameter, its proper estimation has received great attention for decades. However, instead of the mutation rate, many authors opt for reporting the average mutant frequency, a less meaningful quantity. This is because the standard methods to estimate the mutation rate, derived from the Luria and Delbrück's fluctuation analysis, ideally require high-replication experiments to be applied; a requirement often unattainable due to constraints of time, budget or sample availability. But the main problem with mutant frequency, apart from being less informative, is its poor reproducibility; an especially marked defect when the chosen average is the arithmetic mean. Several authors tried to avoid this by employing other averages (such as the median or the geometric mean) or discarding outliers, though as far as we know nobody has evaluated which method performs best under low-replication settings. Here we use computer simulations to compare the performance of different methods used in low-replication experiments (≤4 cultures). Besides the customary averages of mutant frequency, we also tested two well-known fluctuation methods. Contrary to common practice, our results support that fluctuation methods should be applied in such circumstances, as they perform as well as or better than any average of mutant frequency. In particular, experimentalists will benefit from using MSS maximum likelihood in low-replication experiments because it: (i) provides more reproducible results, (ii) allows for direct estimation of mutation rate and (iii) allows for the application of conventional statistics.

Easy detection of GFP-positive mutants following forward mutations at specific gene locus in cultured human cells

We have generated a new mutation assay system using HT1080 human fibrosarcoma cells, which consists of a combination of tetracycline-operator dependent GFP gene (TetO-EGFP) and tetracycline repressor (TetR) genes, where the expression of GFP gene is under strict control of TetR protein, and the TetR gene is located within the endogenous HPRT gene. In this system, any inactivating mutation at the TetR gene or large deletions including the gene itself results in high expression of GFP gene (>200-fold increase) in the cells, which can be readily scored not only by a flow cytometer but also under a fluorescent microscope. With this new cell line, we show that the spontaneous mutation rate at the TetR locus was 2.8-3.4×10(-6)/cell division, slightly lower than the rate at the endogenous HPRT gene of HT1080 cells, and has a dose response to X rays as a mutagen. We also isolated variant clones with elevated spontaneous mutation rate (i.e., genetically unstable cells) following X irradiation. Spontaneous GFP-positive mutants were predominantly base-change mutations at the TetR gene while those obtained after X irradiation often contained large deletions which spanned up to 6Mb. The results indicate that the bacterial TetR/TetO regulatory units work extremely well as a mutation detection system in human cells, and any part of the human genome may be tested for mutation sensitivity following targeted insertion of the TetR gene in a stably expressing gene.

Proliferation model dependence in fluctuation analysis: the neutral case

We discuss the evaluation of Luria-Delbrück fluctuation experiments under Bellman-Harris models of cell proliferation. It is shown that under certain very natural assumptions concerning the life-time distributions and the offspring distributions of mutant and non-mutant cells, the suitably normed and centered number of mutants contained in a large culture of bacteria (or the like) converges to a certain stable random variable with index 1. The result obtains under the assumption that the mutation under consideration is "neutral" in the sense that on average and in the long run, mutant cells produce the same number of offspring as non-mutant cells.

A note on the evaluation of fluctuation experiments

Fluctuation analysis has emerged as a valuable tool for the measurement of mutation rates in single-cell populations. In this paper, we show how to make fuller use of the information supplied by the outcome of a fluctuation experiment. We shall extend Lea and Coulson's theory of the Luria-Delbrück distribution so that it accounts for residual mutation, reduced plating efficiency of mutants, and phenotypic lag, and establish a unifying method for the evaluation of fluctuation experiments in these cases and discuss its limitations. It will be proved that not all factors that might influence the distribution of mutant colonies in a fluctuation experiment can, in effect, be determined simultaneously. Nevertheless, it will be shown that the fluctuation-analytic approach to the measurement of mutation rates may retain its value in comparison with (or may even be superior to) alternative methods. Finally, we give some numerical examples to illustrate our results.

Environmentally directed mutations and their impact on industrial biotransformation and fermentation processes

Microbial adaptation plays an important role in the selection of improved strains for biotechnological processes and for the maintenance and stability of the selected production strains. Most of the knowledge about adaptation processes and environmentally directed mutations originates from environmental microbiology and from studies on biological evolution. The increasing information on the molecular mechanisms of adapted mutations and on the development of methods frequently used in environmental and evolutionary microbiology, such as the selection in semi-continuous cultures or chemostats, can be used as input and tools for the improvement of industrial production organisms.