Perspectives in molecular mutagenesis.

Abstract

The models and paradigms that underlie a vigorously developing science may tend to stifle progress or may serve to sharpen the knife edge of paradox. Working out mutagenic mechanisms is a conceptually and technologically demanding task, and we are accumulating an increasingly uncomfortable number of experimental and theoretical inconsistencies. First, there continue to be widespread difficulties in specifying the chemical nature of mutagenic DNA alterations, both because of the multitude of DNA reaction products induced by many mutagens and because of the intrinsic rarity of most mutational responses. For instance, alkylation of the 0(6) position of guanine to generate adducts of modest dimensions is widely believed to form the basis for the mutagenic and carcinogenic actions of numerous chemicals. However, while this scheme is supported by in vitro evidence, it has failed to explain why bacteriophages can be thus alkylated in vitro by N-methyl-N'-nitro-N-nitrosoguanidine without the production of mutations, or why microbial eukaryotes alkylated by ethyl methanesulfonate or N-methyl-N'-nitro-N-nitrosoguanidine display no mutagenic response when their "error-prone repair systems" are mutationally inactivated. Second, a base pair is typically mutated at vastly different rates, and with different directional specificities, when it resides at different positions within a gene; whereas very little of this variability is explained by current theories that aim to describe the determinants of fidelity in DNA replication. (Some sizable portion of this variation now appears to depend not only upon neighboring base-pair influences but also upon much more subtle and distant effects). Third, experimental modifications of enzymatic fidelity by means of amino acid substitutions, and perhaps also cation replacements, lead to such a diversity of modified mutation rates as to seriously challenge the ability of any simple theory to organize the experimental observations into a coherent and predictive network.