Toxicogenomics technology defines toxicity gene expression signatures for early predictions and hypotheses generation for mechanistic studies, which are important approaches for evaluating toxicity of drug candidate compounds. A large gene expression database built using cDNA microarrays and liver samples treated with over one hundred paradigm compounds was mined to determine gene expression signatures for nongenotoxic carcinogens (NGTCs). Data were obtained from male rats treated for 24 h. Training/testing sets of 24 NGTCs and 28 noncarcinogens were used to select genes. A semiexhaustive, nonredundant gene selection algorithm yielded six genes (nuclear transport factor 2, NUTF2; progesterone receptor membrane component 1, Pgrmc1; liver uridine diphosphate glucuronyltransferase, phenobarbital-inducible form, UDPGTr2; metallothionein 1A, MT1A; suppressor of lin-12 homolog, Sel1h; and methionine adenosyltransferase 1, alpha, Mat1a), which identified NGTCs with 88.5% prediction accuracy estimated by cross-validation. This six genes signature set also predicted NGTCs with 84% accuracy when samples were hybridized to commercially available CodeLink oligo-based microarrays. To unveil molecular mechanisms of nongenotoxic carcinogenesis, 125 differentially expressed genes (P<0.01) were selected by Student's t-test. These genes appear biologically relevant, of 71 well-annotated genes from these 125 genes, 62 were overrepresented in five biochemical pathway networks (most linked to cancer), and all of these networks were linked by one gene, c-myc. Gene expression profiling at early time points accurately predicts NGTC potential of compounds, and the same data can be mined effectively for other toxicity signatures. Predictive genes confirm prior work and suggest pathways critical for early stages of carcinogenesis.

BackgroundIn this commentary we present the findings from an international consortium on fish toxicogenomics sponsored by the U.K. Natural Environment Research Council (Fish Toxicogenomics—Moving into Regulation and Monitoring, held 21–23 April 2008 at the Pacific Environmental Science Centre, Vancouver, BC, Canada).ObjectivesThe consortium from government agencies, academia, and industry addressed three topics: progress in ecotoxicogenomics, regulatory perspectives on roadblocks for practical implementation of toxicogenomics into risk assessment, and dealing with variability in data sets.DiscussionParticipants noted that examples of successful application of omic technologies have been identified, but critical studies are needed to relate molecular changes to ecological adverse outcome. Participants made recommendations for the management of technical and biological variation. They also stressed the need for enhanced interdisciplinary training and communication as well as considerable investment into the generation and curation of appropriate reference omic data.ConclusionsThe participants concluded that, although there are hurdles to pass on the road to regulatory acceptance, omics technologies are already useful for elucidating modes of action of toxicants and can contribute to the risk assessment process as part of a weight-of-evidence approach.

The mean size and percentage of budded cells of a wild-type haploid strain of Saccharomyces cerevisiae grown in batch culture over a wide range of doubling times (tau) have been measured using microscopic measurements and a particle size analyzer. Mean size increased over a 2.5-fold range with increasing growth rate (from tau = 450 min to tau = 75 min). Mean size is principally a function of growth rate and not of a particular carbon source. The duration of the budded phase increased at slow growth rates according to the empirical equation, budded phase = 0.5 tau + 27 (all in minutes). Using a recent model of the cell cycle in which division is thought to be asymmetric, equations have been derived for mean cell age and mean cell volume. The data are consistent with the notion that initiation of the cell cycle occurs at "start" after attainment of a critical cell size, and this size is dependent on growth rate, being, at slow growth rates, 63% of the volume of fast growth rates. Previous reports are reanalyzed in the light of the unequal division model and associated population equations.

The unequal division model proposed for budding yeast (L. H. Hartwell and M. W. Unger, J. Cell Biol. 75:422-435, 1977) was tested by bud scar analyses of steady-state exponential batch cultures of Saccharomyces cerevisiae growing at 30 degrees C at 19 different rates, which were obtained by altering the carbon source. The analyses involved counting the number of bud scars, determining the presence or absence of buds on at least 1,000 cells, and independently measuring the doubling times (gamma) by cell number increase. A number of assumptions in the model were tested and found to be in good agreement with the model. Maximum likelihood estimates of daughter cycle time (D), parent cycle time (P), and the budded phase (B) were obtained, and we concluded that asymmetrical division occurred at all growth rates tested (gamma, 75 to 250 min). D, P, and B are all linearly related to gamma, and D, P, and gamma converge to equality (symmetrical division) at gamma = 65 min. Expressions for the genealogical age distribution for asymmetrically dividing yeast cells were derived. The fraction of daughter cells in steady-state populations is e-alpha P, and the fraction of parent cells of age n (where n is the number of buds that a cell has produced) is (e-alpha P)n-1(1-e-alpha P)2, where alpha = IN2/gamma; thus, the distribution changes with growth rate. The frequency of cells with different numbers of bud scars (i.e., different genealogical ages) was determined for all growth rates, and the observed distribution changed with the growth rate in the manner predicted. In this haploid strain new buds formed adjacent to the previous buds in a regular pattern, but at slower growth rates the pattern was more irregular. The median volume of the cells and the volume at start in the cell cycle both increased at faster growth rates. The implications of these findings for the control of the cell cycle are discussed.

The value of genomic approaches in hypothesis generation is being realized as a tool for understanding toxicity and consequently contributing to an assessment of drug and chemical safety. In 1999 the membership of the International Life Sciences Institute Health and Environmental Sciences Institute formed a committee to develop a collaborative scientific program to address issues, challenges, and opportunities afforded by the emerging field of toxicogenomics. Experts and advisors from academia and government laboratories participate on the committee, along with approximately 30 corporate member organizations from the pharmaceutical, agrochemical, chemical, and consumer products industries. The committee has designed, conducted, and analyzed numerous toxicogenomic experiments within the broad fields of hepatotoxicity, nephrotoxicity, and genotoxicity. The considerable body of data generated by these programs has been instrumental in increasing understanding of sources of biological and technical variability in the alignment of toxicant-induced transcription changes with the accepted mechanism of action of these agents and the challenges in the consistent analysis and sharing of the voluminous data sets generated by these approaches. Recognizing the importance of standardized microarray data formats and public repository databases as the mechanism by which microarray data can be compared and interpreted by the scientific community, the committee has partnered with the European Bioinformatics Institute to develop a database to house the data generated by its collaborative research.