How is mutation different from transformation

Download preview PDF. Skip to main content. This service is more advanced with JavaScript available. Advertisement Hide. However, because this estimate assumes a constant mutation rate during the generations of experimental evolution, rather than a changing rate due to the fixation of mutator alleles, mutators emerging at the end of experimental evolution may not be identified.

To address this limitation, we first searched for mutations in genes diagnostic for bacterial mutators e. We focused here on non-synonymous mutations because these are more likely to cause functional defects in the relevant genes. Next we determined the mutation rate of all evolved lineages relative to their respective ancestor using a phenotypic assay designed to detect the frequency of spontaneous mutants to resistance to either rifampicin or streptomycin [26].

Using the first approach, we detected significantly more non-synonymous mutations in DNA repair genes in non-competent than in competent populations Fig. Next, using a phenotypic assay, we found that the mutation frequency of non-competent lineages was significantly higher than competent populations Fig.

In contrast to re-sequencing results, these assays found no overall effect of stress on the mutation frequency Fig.

Thus both at the genetic and phenotypic levels, our data support a model where competence reduces mutation fixation and limits the emergence of mutator phenotypes, but this conservatism comes possibly at the expense of reduced adaptation under benign growth conditions.

Transformation can dramatically benefit S. However, these benefits in pathogenic bacterial lineages under strong antibiotic selection tell only part of the story, and may not reflect the effects of transformation more broadly. Using an experimental evolution approach, we found that competence benefited cells by reducing the mutation load and limiting the emergence of mutators Fig.

Additionally, competent populations reached higher fitness when evolving in the presence of periodic stress; equally, exposure to periodic stress decreased the rate of evolution of non-competent populations Fig. Although we applied an extremely mild stress in our experiment Figure S1 , it is notable that the kanamycin concentration we used is sufficient to induce competence in wild-type strains [20].

It is therefore possible that benefits to competence in populations that experienced drug-stress was the result of increased recombination, which could have off-set the cost of transformation in a benign environment by slightly increasing their rate of adaptation. By contrast, non-competent cells exposed to kanamycin may face greater costs because kanamycin causes an inability to repair ribosomal decoding errors, which can subsequently lead to DNA damage and increase the mutation rate [24].

These stress-dependent benefits of competence may be particularly important in the human nasopharynx, where S. Transformation is predicted to benefit bacterial species with high mutation rates by reducing their mutation load [12].

Using complete genome sequences, we estimate that the average mutation rate in S. Despite these high rates of mutation we were surprised to find that some of the non-competent strains evolved even higher rates of mutation than their ancestor during this long-term experiment Figs.

These genotypic results were confirmed phenotypically Fig. Although we are uncertain what caused the difference in mutation rates between competent and non-competent lineages to arise, one strong possibility is that transformation separates mutator alleles from the mutations they cause. Thus while mutations in DNA repair genes leading to mutators may arise equally in both competent and non-competent cells, they are lost before they become common in competent lineages [34]. Accordingly, competent lineages fix fewer mutations overall.

Under benign conditions this may limit adaptation while causing minimal harm to non-competent populations. However, non-competent cells suffer to a greater degree when faced with stress, because they cannot revert to a less loaded state, and because stress may exacerbate the negative fitness effects of new mutations [35] , [36]. In a similar recent study with the yeast Saccharomyces cerevisiae sex neutralised the deleterious effects of hyper mutation on the rate of adaptation [19].

The neutralisation of potentially deleterious mutations, e. Similar effects are inferred in the naturally transformable bacterial genus Neisseria where the number of species-specific DNA uptake sequences i. Although S. Bacteria in nature face unpredictable patterns of stress and mutation. Our results suggest that these conditions, together with an intrinsically high mutation rate, favour the maintenance of transformation while infrequent stress may facilitate its loss.

Notably, surveys of naturally competent species such as H. Similar variation exists in S. In summary, we conclude that competence in S. Strains used in this study were derived from Rx1 and its isogenic non-competent derivative FP5, which is unable to secrete the competence stimulating peptide, CSP [42].

This environment supported high levels of transformation Figure S3. Chemostat cultures were inoculated and maintained as described previously [21] , and sampled every 50 generations of growth.

The replicates in each treatment were equally split between the two differently marked versions of Rx1 competent strain and Fp5 non-competent strain. Half of the populations were exposed twice a week to low doses of kanamycin introduced directly into the chemostat to simulate short periods of stress. This concentration of kanamycin had no effect on the growth rate of cells Figure S1 , but is sufficient to cause ribosomal decoding errors during protein production, which promotes the induction of competence [20] , [44].

Each strain was evolved independently, thereby avoiding potential effects of cross-induction of competence or competence-induced cell-lysis [45] , [46]. Every week, after approximately 50 generations, a 1 mL sample was taken from each population and tested for the presence of the correct marker and absence of the opposite marker. Populations were maintained for 20 weeks, which corresponds to about 1, generations. Fitness was determined by comparing the change in relative densities of two reciprocally marked evolved populations in a chemostat in mixed culture over a hour span.

This time period was chosen because it is within the period that the periodically stressed populations spend in the benign environment between doses of kanamycin.

Competition assays were initiated by inoculating chemostats with equal densities of each competitor. Chemostats were sampled immediately and then again after 32 hours to determine the relative densities of each competitor.

The Malthusian parameters per hour were then calculated for each strain based on the density of each strain at the start and end of the competition, as described previously [47]. The selection rate constant was then calculated as the difference between Malthusian parameters as described previously [23]. First, we tested for a significant fitness difference between competitors for each treatment by comparing a restricted maximum likelihood mixed model against an intercept of zero, corresponding to equal fitness.

Second, we used the restricted maximum likelihood REML mixed model, again with replicate fitness assays as a random factor within treatments, to test for fitness differences between treatments periodic stress or competence as a fixed factor. All analyses were done in R with package LME4. P-values were estimated by MCMC simulation with 10,fold replication using the p. Clonal isolates from each of the 16 evolved populations as well as all four ancestral strains were sequenced using the SOLiD4 platform at the University of Manchester genomics facility.

Genomic DNA was obtained using phenol-chloroform isolation and ethanol precipitation [48]. The normalisation equalized the size of the datasets to the strain with the lowest number of reads thereby normalising the quality of the consensus sequences.

The normalised reads were then mapped against the fully sequenced reference strain S. Subsequently, variant tables extracted from Geneious were used in the Galaxy online tool set [52] , [53] to identify mutations for each evolved clone compared to its ancestor. Parallel changes were then double checked by hand in the UCSC microbial genome browser [54] to eliminate false positives.

The resulting mutation tables were used for further analysis. To determine the effect of periodic stress and competence the total numbers of mutations were compared in a GLM model with a Poisson distribution using R.

Here, the rare form of cytosine binds to the common form of adenine instead of guanine. The rare form of guanine binds to the common form of thymine instead of cytosine. Genetics: A Conceptual Approach , 2nd ed.

All rights reserved. Figure Detail. Today, scientists suspect that most DNA replication errors are caused by mispairings of a different nature: either between different but nontautomeric chemical forms of bases e.

This type of mispairing is known as wobble. It occurs because the DNA double helix is flexible and able to accommodate slightly misshaped pairings Crick, Figure 2: Wobble in mismatched nucleotide base pairs. A shift in the position of nucleotides causes a wobble between a normal thymine and normal guanine.

An additional proton on adenine causes a wobble in an adenine-cytosine base-pair. Genetics: A Conceptual Approach, 2nd ed. Replication errors can also involve insertions or deletions of nucleotide bases that occur during a process called strand slippage.

Sometimes, a newly synthesized strand loops out a bit, resulting in the addition of an extra nucleotide base Figure 3. Other times, the template strand loops out a bit, resulting in the omission, or deletion, of a nucleotide base in the newly synthesized, or primer , strand. Regions of DNA containing many copies of small repeated sequences are particularly prone to this type of error. DNA polymerase enzymes are amazingly particular with respect to their choice of nucleotides during DNA synthesis, ensuring that the bases added to a growing strand are correctly paired with their complements on the template strand i.

Nonetheless, these enzymes do make mistakes at a rate of about 1 per every , nucleotides. That might not seem like much, until you consider how much DNA a cell has.

In humans, with our 6 billion base pairs in each diploid cell, that would amount to about , mistakes every time a cell divides! Fortunately, cells have evolved highly sophisticated means of fixing most, but not all, of those mistakes. Some of the mistakes are corrected immediately during replication through a process known as proofreading , and some are corrected after replication in a process called mismatch repair.

During proofreading, DNA polymerase enzymes recognize this and replace the incorrectly inserted nucleotide so that replication can continue. After replication, mismatch repair reduces the final error rate even further. Incorrectly paired nucleotides cause deformities in the secondary structure of the final DNA molecule. During mismatch repair, enzymes recognize and fix these deformities by removing the incorrectly paired nucleotide and replacing it with the correct nucleotide.

Incorrectly paired nucleotides that still remain following mismatch repair become permanent mutations after the next cell division. This is because once such mistakes are established, the cell no longer recognizes them as errors. Consider the case of wobble-induced replication errors.

When these mistakes are not corrected, the incorrectly sequenced DNA strand serves as a template for future replication events, causing all the base-pairings thereafter to be wrong. For instance, in the lower half of Figure 2, the original strand had a C-G pair; then, during replication, cytosine C is incorrectly matched to adenine A because of wobble. In this example, wobble occurs because A has an extra hydrogen atom.

In the next round of cell division, the double strand with the C-A pairing would separate during replication, each strand serving as a template for synthesis of a new DNA molecule.

At that particular spot, C would pair with G, forming a double helix with the same sequence as its original i. This type of mutation is known as a base, or base-pair, substitution.

Base substitutions involving replacement of one purine for another or one pyrimidine for another e. Likewise, when strand-slippage replication errors are not corrected, they become insertion and deletion mutations.

Much of the early research on strand-slippage mutations was conducted by George Streisinger in the s. Streisinger, a professor at the University of Oregon and a fish hobbyist, is known by some as the "founding father of zebrafish research.

Streisinger used this virus to show that most nucleotide insertion and deletion mutations occur in areas of DNA that contain many repeated sequences also called tandem repeats , and he formulated the strand-slippage hypothesis to explain why this was the case Streisinger et al.

In Figure 3, notice the series of repeat T's on the template strand where the slippage has occurred. When slippage takes place, the presence of nearby duplicate bases stabilizes the slippage so that replication can proceed. During the next round of replication, when the two strands separate, the insertion or deletion on either the template or primer strand, respectively, will be perpetuated as a permanent mutation. Scientists have collected enough evidence to confirm Streisinger's strand-slippage hypothesis, and this type of mutagenesis remains an active field of scientific research.

The difference in frequency between structural mutations and transformations was about 10 2 3 and it appears statistically extremely significant. These results seem to indicate an absolute difference between structural mutations and transformations.