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Transgenic systems for measuring mammalian mutagenesis often use recoverable viral vectors. We hypothesize that mutations in these transgenic systems can arise from three different origins of DNA damage and replication errors and that these three origins of mutations (in vivo, ex vivo, and in vitro) can be differentiated in the PhiX174 am3, cs70 single burst assay (SBA) on the basis of burst size (BS). In vivo mutations are fixed in the animal, ex vivo mutations are fixed in bacterial cells during recovery of the phage, and in vitro revertants arise during the first replications of nonmutant phages under selective conditions. PX-2 cells, derived from a homozygous embryo of a PhiX174 transgenic mouse, were treated with vehicle or N-ethyl-N-nitrosourea (ENU). An algorithm was developed to estimate the BS that resulted in the highest induced revertant frequency; the estimate was 56. In vivo revertants were defined as having BS >55, ex vivo revertants as having a BS of 13-56, and in vitro revertants as having a BS of <14. The frequencies of in vivo revertants at 0, 100, and 200 mg/kg ENU were 0.06, 0.36, and 4.10 x 10(-6) (dose response, P = 0.004); ex vivo revertants were 0.36, 0.46, and 0.41 x 10(-6) (P = 0.37), and in vitro revertants were 0.39, 0.46, and 0.41 x 10(-6) (P = 0.55), respectively. These results show that only in vivo revertants reflect mutagen treatment. They also provide a basis for identifying PhiX174 am3 revertants induced in vivo and may increase the sensitivity of the assay for in vivo mutation.

The sensitivity of in vivo transgenic mutation assays benefits from the sequencing of mutations, although the large number of possible mutations hinders high throughput sequencing. A forward mutational assay exists for Phi X174 that requires an altered, functional Phi X174 protein and therefore should have fewer targets (sense, base-pair substitutions) than forward assays that inactivate a protein. We investigated this assay to determine the number of targets and their suitability for detecting a known mutagen, N-ethyl-N-nitrosourea (ENU). We identified 25 target sites and 33 different mutations in Phi X174 gene A after sequencing over 350 spontaneous and ENU-induced mutants, mostly from mouse embryonic cell line PX-2 isolated from mice transgenic for Phi X174 am3, cs70 (line 54). All six types of base-pair substitution were represented among both the spontaneous and ENU-treated mutant spectra. The mutant spectra from cells treated with 200 and 400 microg/ml ENU were both highly different from the spontaneous spectrum (P < 0.000001) but not from each other. The dose trend was significant (P < 0.0001) for a linear regression of mutant frequencies (R(2) = 0.79), with a ninefold increase in mutant frequency at the 400 microg/ml dose. The spontaneous mutant frequency was 1.9 x 10(-5) and the spontaneous spectrum occurred at 11 target base pairs with 15 different mutations. Thirteen mutations at 12 targets were identified only from ENU-treated cells. Seven mutations had highly significant increases with ENU treatment (P < 0.0001) and 15 showed significant increases. The results suggest that the Phi X174 forward assay might be developed into a sensitive, inexpensive in vivo mutagenicity assay.

Genotypic selection methods detect rare sequence changes in populations of DNA molecules. These methods have been used to investigate the chemical induction of mutation and for the detection and diagnosis of cancer. The possible use of genotypic selection for improving current risk assessment practices is based on the premise that the frequency of somatic mutation is of critical importance in understanding and modeling carcinogenesis. If genotypic selection can measure the induction of specific mutations that disrupt normal cell/tissue homeostasis, then it could provide key mechanistic information for cancer risk assessment. For example, genotypic selection data might support a particular low-dose extrapolation method or characterize the relationship between rodent and human cancer risk. Strategies for evaluating the use of genotypic selection in cancer risk assessment include the concept of developing a battery of targets that detect a range of agent-specific effects. Ideal targets to examine by genotypic selection are the oncogene and tumor suppressor gene mutations frequently detected in human tumors because these are thought to represent tumor-initiating events. The most commonly occurring basepair (bp) substitutions within the ras and p53 genes are identified. Also, the battery of genotypic selection methods is defined in terms of the most important mutational specificities to include. In theory, the major basepair substitution mutations induced by 29 of 31 chemical carcinogens could be detected by analyzing three different mutations: G:C-->T:A, G:C-->A:T, and A:T-->T:A. Genotypic selection will have the greatest impact on risk assessment if measurement of spontaneous mutation is possible. Data from phenotypic selection assays suggest this corresponds to detection of mutant fractions of approximately 10(-7), and this would necessitate examining DNA samples containing >10(7) target molecules. Despite its apparent potential, considerable development and validation is needed before genotypic selection data can be applied to cancer risk assessment.

The Poisson distribution is a fundamental probability model for count data, and is a natural model for the observed plaque counts in mutation assays using animals with lambda or PhiX174 transgenes. The Poisson likelihood for observed counts is a function of the mutant fraction, and it is straightforward to derive the associated maximum likelihood estimate of the mutant fraction and its variance. The estimate is easy to calculate, and if not the same, very similar to ad hoc estimates in current use. The model indicates the proper way to combine data from a number of plates, possibly prepared with different sample dilutions. The estimator of the mutant fraction is biased as a consequence of dividing by a random variable, the plaque count used to calculate the total recovered plaque-forming units. Fortunately, the bias becomes negligible as this count becomes large. On the other hand, increasing this count can increase the variance by decreasing the amount of sample assayed for mutant phages. Concurrent heed to the bias and the variance provides some guidance as to the optimum allocation of a sample into portions assayed for mutant phages and total recovered phages. The distribution of the estimate of the mutant fraction is related to the binomial distribution. This relationship implies a binomial distribution for the mutant count conditional on an overall count (either the sum of mutant and counted total plaques or the sum of counted mutant and non-mutant plaques). A special but important case occurs when each plate can be evaluated for mutant plaques and non-mutant plaques. Then, the observed proportion of mutants estimates the mutant fraction. More generally, the relationship to a binomial distribution provides a procedure for calculating a confidence interval., (Copyright 1999 Elsevier Science B.V.)

A test procedure is described for detecting germ cell mutations which alter activity of enzymes in in vivo mammalian tissues. The objective is to identify those individuals, among a population of F1 animals from treated parents, that are heterozygous for a mutation at an unspecified locus which results in an altered activity of one or more of the enzymes monitored. Animals are evaluated through three analysis screens with emphasis on identification of mutant individuals. In the first screen, activity values of a battery of enzymes are compared to their respective normal ranges to identify those which are aberrant. The mean activities and coefficients of variation useful in defining the normal ranges for 19 enzymes in C57BL/6J Nctr mice are presented along with a set of theoretical normal animal misclassification probabilities for each of these enzymes. The second screen is a Michaelis-Menten kinetic analysis of aberrant enzymes, the goal of which is to correct misclassifications of normal animals made in the first screen. A third screen of aberrant enzyme activities in a subsequent generation is used to demonstrate inheritance and confirm the mutation. The advantages and the limitations, as well as possible variations of the basic procedure are discussed.