Mutagenesis, Vol. 15, No. 4, 325-328,
July 2000
© 2000 UK Environmental Mutagen Society/Oxford University Press
A new approach to evaluate mechanistic relationships among genotoxic phenomena: validation
Department of Environmental and Occupational Health, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA
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Abstract |
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In order to determine its applicability for the study of genotoxicity, a recently developed method to probe for possible mechanistic relationships among toxicological phenomena was applied to the induction of mutations in Salmonella typhimurium. Since the basis of this phenomenon is understood, this would provide a test of the applicability of the new method to DNA-based mechanisms. The results presented indicate that significant relationships are indeed found among phenomena involving damage to or modification of DNA but not between them and non-genotoxic phenomena. The present results suggest that the newly developed approach could be applied to test mechanistic hypotheses involving genotoxic phenomena.
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Introduction |
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The ability to understand the mechanistic basis of toxicological phenomena is a daunting task, complicated by the fact that the same effect can be caused by a multiplicity of mechanisms. Thus, for example, the in vivo induction of micronuclei could result from DNA damage as well as from disturbance of microtubular integrity. In the first instance it may identify a possible health hazard [e.g. genotoxic carcinogenicity (Ashby and Morrod, 1991
; Ashby and Tennant, 1991)] while in the latter it might identify a target for cancer chemotherapy [e.g. the action of Taxol® and vincristine on tubulin (ter Haar et al., 1996a,b; Rosenkranz et al., 1998)]. Obviously, insight into the mechanistic basis of toxicological action is needed to assess the risks posed by chemicals. One of the approaches to the elucidation of this problem is to evaluate the congruence between the induction of different toxicological phenomena. Thus, if a group of chemicals induces a significantly higher than expected incidence of both chromosomal aberrations and developmental anomalies, then one might conclude that the two phenomena are related, possibly causally rather than just by chance.
In a recent study, we described a relatively simple method, designated the Chemical Diversity Approach (Pollack et al., 1999), to ascertain the mechanistic relatedness among toxicological phenomena. The approach has been applied recently to some of the dilemmas resulting from the High Production Volume Chemical Challenge Program (Libowitz, 1999; Stokstadt, 1999). It suggested the possibility of substituting surrogate in vitro assays for currently used standard animal-based toxicological procedures (Rosenkranz and Cunningham, 2000a,b). In that instance, the procedure was applied empirically without a previously conjectured hypothesis. The new method is based upon sound statistical principles (i.e. 
2 tests). However, prior to deploying it in the elucidation of mechanisms of genotoxicity, it seemed appropriate to apply it to a biological phenomenon the basis of which is known and thereby validate it. In the present study we apply this approach to the study of mutation induction in Salmonella typhimurium (MutSal). This phenomenon is known to occur as a consequence of DNA alterations that result in specific point mutations (i.e. base substitutions and frameshifts). In this investigation the relationship of the induction of point mutations to phenomena known to involve DNA damage/genotoxicity as well as to effects reflecting non-genotoxic mechanisms was investigated.
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Materials and methods |
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The Chemical Diversity Approach: rationale
This procedure is based upon the premise that the mechanistic relationship between biological phenomena can be derived from knowledge of the prevalence of chemicals which give identical responses in assays designed to determine that relationship. Thus, the electrophilic theory of cancer causation (Miller and Miller, 1977
) led to the recognition of a significant relationship between the induction of mutations and of cancers in rodents, i.e. `carcinogens are mutagens' (Ames et al., 1973). Further studies, however, found that attack on the DNA did not explain all chemically induced cancers (Zeiger, 1987; Ashby and Tennant, 1991). In fact, the mechanistic basis of most toxicological phenomena is not clearly elucidated, presumably because each can occur by a multiplicity of pathways. Still, based upon the above premise, we should be able to gain mechanistic insight by evaluating the concordance, or lack thereof, between the prevalences of different toxicological effects for the same population of chemicals. Thus, if we were to evaluate the toxicological profiles of a group of chemicals, we could determine, for example, how many of the chemicals are Salmonella mutagens and how many of them induce cancers in rodents. We would then also determine how many of them were mutagens and carcinogens [i.e. `genotoxic' carcinogens (Ashby and Tennant, 1991)]. The observed prevalence of `genotoxic' carcinogens can then be compared with the prevalence expected based upon unrelatedness (i.e. the null hypothesis). If the observed prevalence is significantly greater than the expected one, then it can be concluded that the two phenomena are related mechanistically to one another. (Similarly, if the observed prevalence is significantly lower than the expected one, this suggests that the two phenomena are antagonistic to one another, e.g. they could compete for a specific receptor.)
In implementing such an approach, it quickly becomes obvious that there is a dearth of experimental data on the same chemicals across a variety of toxicological end points. Hence, the significance of the observed joint prevalences cannot be ascertained. The current approach was devised to overcome this shortcoming. It is based upon the availability of characterized and validated models describing structure–activity relationships (SAR) (Rosenkranz et al., 1999). Moreover, while reliable databases of toxicological models, when available, are usually limited to 200–300 chemicals, the approach used here predicts the toxicological profiles of 10 000 chemicals representative of the `universe of chemicals' (National Academy of Sciences, 1984). While no SAR model is perfectly predictive, when applied to a population of 10 000 chemicals, as long as the number of false positive and false negative predictions are approximately equal (i.e. the model's sensitivity and specificity are similar), we can expect that the overall projected prevalence will reflect the true distribution. Thus it allows determination of the significance of observed joint prevalences.
The approach can be used to confirm specific hypotheses (e.g. the electrophilic theory of cancer causation) as well as to generate new hypotheses driven solely by the data (i.e. knowledge-based) and the availability of appropriate SAR models.
SAR methodology
For these studies we used the CASE/MULTICASE SAR expert systems described previously (Klopman and Rosenkranz, 1994, 1995). Application of this methodology results in the development of four submodels, each of which is derived from a different algorithm and is useful for investigating different aspects of the biological phenomena under consideration (Rosenkranz et al., 1999). The projections of the four individual submodels were integrated into a single prediction based upon Bayes' theorem (Chankong et al., 1985; Zhang et al., 1996; Macina et al., 1998). In each instance the cut-offs used to predict the activity of the 10 000 chemicals (Pollack et al., 1999) were set to ensure that the positive (or negative) predictive power of the test was optimal.
Each of the SAR models used herein has been characterized (Rosenkranz et al., 1999) with respect to its ability to predict the activity of chemicals external to the model. They were then used to predict the activity of the 10 000 chemicals representing the `universe of chemicals'.
SAR models
The validated SAR models used for these studies have been described previously: inhibition of gap junctional intercellular communication (GJIC) (Rosenkranz et al., 1997), mutagenicity in Salmonella (Liu et al., 1996; Zeiger et al., 1996) and in cultured mouse lymphoma cells (Grant et al., 1999), induction of SOS DNA repair (i.e. the Chromotest) (Mersch-Sundermann et al., 1994, 1996), carcinogenicity in rodents [a combination of results of bioassays conducted by the US National Toxicology Program (Ashby and Tennant, 1991) and of those contained in the Carcinogenic Potency Data Base (CPDB) (Gold et al., 1984, 1986, 1987, 1990, 1993; Zhang et al., 1996; Macina et al., 1998)]. The SAR models for carcinogenicity in rats and mice were derived from the CPDB (Cunningham et al., 1998a,b). The SAR model of 
2µ-globulin-associated nephropathy was based upon data kindly supplied by Dr L.D.Lehman-McKeenan (Procter and Gamble Co.). The SAR model for toxicity to cultured HeLa cells (Zhu et al., 1999) was based upon Ekwall (1980). SAR models for the induction of sister chromatid exchanges (SCEs) and chromosomal aberrations in cultured CHO cells (Rosenkranz et al., 1990), of SCEs and of micronuclei in vivo (Yang et al., 1992; Labbauf et al., 1997), of unscheduled DNA synthesis (UDS) (Zhang et al., 1994) and of toxicity to cultured BALB/c-3T3 cells (Rosenkranz et al., 1992) were also described previously.
The performance characteristics of the SAR models are summarized in Table I.
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Results and discussion |
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Quantitatively, the greatest relationship was seen between the induction of MutSal and SOS DNA repair (Table II
, analysis 1). This is not surprising; as both phenomena reflect DNA damage in prokaryotic species. In fact, some have suggested that MutSal and the induction of SOS DNA repair can be used interchangeably to detect genotoxic carcinogens (Mersch-Sundermann et al., 1994). Moreover, the induction of mutations in Salmonella, based upon the experimental procedure used herein, does involve a component of SOS DNA repair (McCann et al., 1975).
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Similarly, there was also a significant correlation between MutSal and the induction of UDS in rat hepatocytes (Table II
, analysis 2). This, too, was to be expected, given the fact that UDS is a reflection of DNA damage and its repair, albeit that SOS DNA repair is measured in prokaryotes and UDS in eukaryotes. That finding, however, suggests that in deploying a battery of short-term assays predictive of carcinogenicity, it may not be necessary to include systems of increasing phylogenetic complexity, if the site of the effect and its mechanism are similar. There was also significant correlation between MutSal and the induction of cancers in rodents (Table II, analysis 3). This is not unexpected, as we know that ~60% of rodent carcinogens are mutagens/genotoxicants (Ashby and Tennant, 1991). In fact, this is considered one of the bases of carcinogenicity (i.e. mutagenic activation of oncogenes or inactivation of suppressor genes). Moreover, the development of screening procedures for carcinogens is based upon the early paradigm that `carcinogens are mutagens' (Ames et al., 1973).
Likewise, the analysis revealed a mechanistic similarity between MutSal and the induction of genotoxic, genomic and chromosomal events (Table II, analyses 4–8) (i.e. chromosomal aberrations, the induction of SCEs in vivo as well as in vitro, the in vivo induction of micronuclei and the induction of mutations at the tk+/– locus of cultured mouse lymphoma cells) (Grant et al., 1999). These relationships were not unforeseen, as for these, also, alteration of the DNA is on the path to expression of the ultimate phenomena under investigation.
Based upon the present analysis, there was no mechanistic similarity between inhibition of GJIC (Table II, analysis 11), an epigenetic event par excellence (Trosko et al., 1998) and MutSal. Furthermore, there was antagonism (Table II, analysis 12) between MutSal and the induction of 2µ-globulin-associated nephropathy (in male rats), which subsequently may develop into kidney tumors. Electrophilicity/mutagenicity is obviously an event which is not involved in the phenomenon. This antagonism suggests that electrophilicity actually competes with it, possibly by reaction of electrophilic/mutagenic species with 2µ-globulin, which is the target for this phenomenon, and thereby prevent it from being deposited in the kidneys and causing pathogenicity (Swenberg et al., 1989). Obviously, this is a testable hypothesis, and it illustrates the ability of the method to generate new relationships.
In order to further evaluate the specificity of the method, we repeated the analyses using a phenomenon suspected of being unrelated to mutagenicity/DNA modification. Specifically, we used a model for the inhibition of cytochrome P4502D6 (CYP2D6). This isozyme metabolizes a number of pharmacologically active chemicals. Inhibition of this enzyme might inhibit biotransformation of a putative mutagen to a DNA-reactive metabolite, hence this blockage would not be causally related to the induction of mutations. In fact, no indication of a mechanistic relationship between inhibition of CYP2D6 and mutagenicity, genotoxicity and chromosomal phenomena (carcinogenesis, mutagenicity in Salmonella, SOS chromotest, chromosomal aberration, the induction of SCEs both in vitro and in vivo, induction of micronuclei and UDS) was observed (Table II). On the contrary, there was actual antagonism between some of these phenomena and inhibition of CYP2D6. Possibly, this antagonism reflects the fact that this enzyme [as present in either cells or in the exogenous metabolic activation mixture (S9) present in the MutSal assay or SOS DNA repair test] is inhibited and therefore activation of some promutagens/procarcinogens is blocked. However, there was a strong correlation between inhibition of CYP2D6 and induction of 2µ-globulin-associated nephropathy. This is an unexpected finding that obviously will require further elaboration as it suggests a role of this isozyme in the biotransformation of agents capable of causing this nephropathy.
In previous studies it had been shown that chemicals that exhibited trans-species carcinogenicity were also very likely to be genotoxic (Ashby and Tennant, 1991; Gold et al., 1989). Accordingly, we investigated, using this new method, the relationship between MutSal and the ability to induce cancers in the mouse, the rat or both species. Thus, both mouse carcinogens as well as rat carcinogens were very likely to be mutagens (Table III, analyses 1 and 3) and substances carcinogenic to both species had a greatly enhanced likelihood of being mutagens as well (Table III, analysis 2). On the other hand, chemicals carcinogenic in only one species and non-carcinogenic in the other were unlikely to be Salmonella mutagens (Table III, analyses 4 and 5). In fact, there was an antagonistic relationship, as evidenced by the negativity of the difference between the observed and expected values. Thus, the analysis confirmed the experimental observation that chemicals that were trans-species carcinogens were indeed also likely to be mutagens/genotoxicants (Ashby and Tennant, 1991; Gold et al., 1989).
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Conclusions
These findings suggest that the Chemical Diversity Approach reflects mechanistic relationships among toxicological phenomena. Accordingly, it could be used to analyze data as well as to generate hypotheses relating to mechanisms of action of toxicological phenomena that have a genotoxic component. However, it must be recognized that the method is limited by the availability of validated models. As these are expanded further and analyzed, as described herein, additional testable hypotheses may be generated.
With respect to the applicability of the method to phenomena that involve a genotoxic component, the present study provides the justification for its use in a recent project (Rosenkranz and Cunningham, 2000b) that was part of the current international High Production Volume Chemical Challenge Program (EPA, 1999; Stokstadt, 1999). As part of that program it had been suggested that for hazard identification the in vivo micronucleus assay did not have to be used in addition to the Salmonella mutagenicity test. The approach described herein supports that suggestion.
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Acknowledgments |
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The SAR method used herein (CASE/MULTICASE) was made available free of charge by MULTICASE Inc. (25825 Science Park Drive 100, Beachwood, OH). That company was founded and is partly owned by Case Western Reserve University, Gilles Klopman and Herbert S.Rosenkranz. The support of the Vira Heinz Endowment and the US Army Medical Research and Materiel Command Breast Cancer Research Project is gratefully acknowledged.
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Notes |
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1 To whom correspondence should be addressed. Tel: +1 412 624 3001; Fax: +1 412 624 3309; Email: rsnkranz{at}pitt.edu
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References |
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Received on December 1, 1999; accepted on February 25, 2000.
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