Barbara E. Wright

Stress-directed adaptive mutations and evolution

Comparative biochemistry demonstrates that the metabolites, complex biochemical networks, enzymes and regulatory mechanisms essential to all living cells are conserved in amazing detail throughout evolution. Thus, in order to evolve, an organism must overcome new adverse conditions without creating different but equally dangerous alterations in its ongoing successful metabolic relationship with its environment. Evidence suggests that stable long‐term acquisitive evolution results from minor increases in mutation rates of genes related to a particular stress, with minimal disturbance to the balanced and resilient metabolism critical for responding to an unpredictable environment. Microorganisms have evolved specific biochemical feedback mechanisms that direct mutations to genes derepressed by starvation or other stressors in their environment. Transcription of the activated genes creates localized supercoiling and DNA secondary structures with unpaired bases vulnerable to mutation. The resulting mutants provide appropriate variants for selection by the stress involved, thus accelerating evolution with minimal random damage to the genome. This model has successfully predicted mutation frequencies in genes of E. coli and humans. Stressed cells observed in the laboratory over hundreds of generations accumulate mutations that also arise by this mechanism. When this occurs in repair‐deficient mutator strains with high rates of random mutation, the specific stress‐directed mutations are also enhanced.

A Biochemical Mechanism for Nonrandom Mutations and Evolution

As this minireview is concerned with the importance of the environment in directing evolution, it is appropriate to remember that Lamarck was the first to clearly articulate a consistent theory of gradual evolution from the simplest of species to the most complex, culminating in the origin of

Predicting mutation frequencies in stem-loop structures of derepressed genes: implications for evolution.

This work provides evidence that, during transcription, the mutability (propensity to mutate) of a base in a DNA secondary structure depends both on the stability of the structure and on the extent to which the base is unpaired. Zukers DNA folding computer program reveals the most probable stem–loop structures (SLSs) and negative energies of folding (–ΔG) for any given nucleotide sequence. We developed an interfacing program that calculates (i) the percentage of folds in which each base is unpaired during transcription; and (ii) the mutability index (MI) for each base, expressed as an absolute value and defined as follows: MIu2003=u2003(% total folds in which the base is unpaired)u2003×u2003(highest –ΔG of all folds in which it is unpaired). Thus, MIs predict the relative mutation or reversion frequencies of unpaired bases in SLSs. MIs for 16 mutable bases in auxotrophs, selected during starvation in derepressed genes, are compared with 70 background mutations in lacI and ebgR that were not derepressed during mutant selection. All the results are consistent with the location of known mutable bases in SLSs. Specific conclusions are: (i) Of 16 mutable bases in transcribing genes, 87% have higher MIs than the average base of the sequence analysed, compared with 50% for the 70 background mutations. (ii) In 15 of the mutable bases of transcribing genes, the correlation between MIs and relative mutation frequencies determined experimentally is good. There is no correlation for 35 mutable bases in the lacI gene. (iii) In derepressed auxotrophs, 100% of the codons containing the mutable bases are within one codons length of a stem, compared with 53% for the background mutable bases in lacI. (iv) The data suggest that environmental stressors may cause as well u200au200au200au200au200aasu200au200au200au200a selectu200au200au200au200a mutations u200au200au200au200ainu200au200au200au200a derepressed u200au200au200au200agenes. The implications of these results for evolution are discussed.

Mechanisms by which transcription can regulate somatic hypermutation

Mechanisms for somatic hypermutation (SHM) have proven elusive. An actively transcribed substrate was analyzed to elucidate the role of stem–loop structures (SLSs) in SHM. Analysis with a new computer algorithm indicates that the location and mutability of a base are regulated by: (a) the extent to which it is unpaired, (b) the degree to which it is exposed by stabilization of SLSs containing and flanking it, and (c) the level of transcription that drives supercoiling, which creates and stabilizes SLSs containing unpaired bases vulnerable to mutation. New mechanisms are described by which transcription can differentially stabilize SLSs harboring targeted bases and establish specific base exposure patterns. Assuming that transcription levels correlate with the magnitude of superhelicity induced and the lengths of ssDNA forming SLSs, this analysis accounts for the location of all mutable bases during SHM.

The effect of promoter strength, supercoiling and secondary structure on mutation rates in Escherichia coli

Four mutations resulting in opal stop codons were individually engineered into a plasmid‐borne chloramphenicol‐resistance (cat) gene driven by the lac promoter. These four mutations were located at different sites in secondary structures. The mutations were analysed with the computer program mfg, which predicted their relative reversion frequencies. Reversion frequencies determined experimentally correlated with the mutability of the bases as predicted by mfg. To examine the effect of increased transcription on reversion frequencies, the lac promoter was replaced with the stronger tac promoter, which resulted in 12‐ to 30‐fold increases in reversion rates. The effect of increased and decreased supercoiling was also investigated. The cat mutants had higher reversion rates in a topA mutant strain with increased negative supercoiling compared with wild‐type levels, and the cat reversion rates were lower in a topA gyrB mutant strain with decreased negative supercoiling, as predicted.

II. Correlations between secondary structure stability and mutation frequency during somatic hypermutation

The role of secondary structures and base mutability at different levels of transcription and supercoiling is analyzed in variable region antibody genes VH5, VH94 and VH186.2. The data are consistent with a model of somatic hypermutation in which increasing levels of transcription and secondary structure stability correlate with the initial formation of successive mutable sites. Encoded differences exist in stem length and the number of GC pairs at low versus high levels of transcription in CDRs. These circumstances simplify the complexities of coordinating mutagenesis by confining this process to each mutable site successively, as they form in response to increasing levels of transcription during affinity maturation.

I. VH gene transcription creates stabilized secondary structures for coordinated mutagenesis during somatic hypermutation

During the adaptive immune response, antigen challenge triggers a million-fold increase in mutation rates in the variable-region antibody genes. The frequency of mutation is causally and directly linked to transcription, which provides ssDNA and drives supercoiling that stabilizes secondary structures containing unpaired, intrinsically mutable bases. Simulation analysis of transcription in VH5 reveals a dominant 65nt secondary structure in the non-transcribed strand containing six sites of mutable ssDNA that have also been identified independently in human B cell lines and in primary mouse B cells. This dominant structure inter-converts briefly with less stable structures and is formed repeatedly during transcription, due to periodic pauses and backtracking. In effect, this creates a stable yet dynamic mutability platform consisting of ever-changing patterns of unpaired bases that are simultaneously exposed and therefore able to coordinate mutagenesis. Such a complex of secondary structures may be the source of ssDNA for enzyme-based diversification, which ultimately results in high affinity antibodies.

The roles of transcription and genotoxins underlying p53 mutagenesis in vivo

Transcription drives supercoiling which forms and stabilizes single-stranded (ss) DNA secondary structures with loops exposing G and C bases that are intrinsically mutable and vulnerable to non-enzymatic hydrolytic reactions. Since many studies in prokaryotes have shown direct correlations between the frequencies of transcription and mutation, we conducted in silico analyses using the computer program, mfg, which simulates transcription and predicts the location of known mutable bases in loops of high-stability secondary structures. Mfg analyses of the p53 tumor suppressor gene predicted the location of mutable bases and mutation frequencies correlated with the extent to which these mutable bases were exposed in secondary structures. In vitro analyses have now confirmed that the 12 most mutable bases in p53 are in fact located in predicted ssDNA loops of these structures. Data show that genotoxins have two independent effects on mutagenesis and the incidence of cancer: Firstly, they activate p53 transcription, which increases the number of exposed mutable bases and also increases mutation frequency. Secondly, genotoxins increase the frequency of G-to-T transversions resulting in a decrease in G-to-A and C mutations. This precise compensatory shift in the fate of G mutations has no impact on mutation frequency. Moreover, it is consistent with our proposed mechanism of mutagenesis in which the frequency of G exposure in ssDNA via transcription is rate limiting for mutation frequency in vivo.

Evolution of coordinated mutagenesis and somatic hypermutation in VH5.

The VH5 human antibody gene was analyzed using a computer program (mfg) which simulates transcription, to better understand transcription-driven mutagenesis events that occur during phase 1 of somatic hypermutation. Results show that the great majority of mutations in the non-transcribed strand occur within loops of two predicted high-stability stem-loop structures, termed SLSs 14.9 and 13.9. In fact, 89% of the 2505 mutations reported are within the encoded complementarity-determining region (CDR) and occur in loops of these high-stability structures. In vitro studies were also done and verified the existence of SLS 14.9. Following the formation of SLSs 14.9 and 13.9, a sustained period of transcriptional activity occurs within a window size of 60-70 nucleotides. During this period, the stability of these two SLSs does not change, and may provide the substrate for base exchanges and mutagenesis. The data suggest that many mutable bases are exposed simultaneously at pause sites, allowing for coordinated mutagenesis.

Mechanisms of genotoxin-induced transcription and hypermutation in p53

It is widely assumed that genotoxin-induced damage (e.g., G-to-T transversions) to the tumor suppressor gene, p53, is a direct cause of cancer. However, genotoxins also induce the stress response, which upregulates p53 transcription and the formation of secondary structures from ssDNA. Since unpaired bases are thermodynamically unstable and intrinsically mutable, increased transcription could be the cause of hypermutation, and thus cancer. Support for this hypothesis has been obtained by analyzing 6662 mutations in all types of cancer compared to lung and colon cancers, using the p53 mutation database. The data suggest that genotoxins have two independent effects: first, they induce p53 transcription, which increases the number of mutable bases that determine the incidence of cancer. Second, genotoxins may alter the fate, or ultimate mutation of a mutable base, for example, by causing more of the available mutable Gs to mutate to T, leaving fewer to mutate to A. Such effects on the fate of mutable bases have no impact on the incidence of cancer, as both types of mutations lead to cancer.