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UV-sensitive syndrome (UV(S)S) is an autosomal recessive disorder characterized by photosensitivity and deficiency in transcription-coupled repair (TCR), a subpathway of nucleotide-excision repair that rapidly removes transcription-blocking DNA damage. Cockayne syndrome is a related disorder with defective TCR and consists of two complementation groups, Cockayne syndrome (CS)-A and CS-B, which are caused by mutations in ERCC8 (CSA) and ERCC6 (CSB), respectively. UV(S)S comprises three groups, UV(S)S/CS-A, UV(S)S/CS-B and UV(S)S-A, caused by mutations in ERCC8, ERCC6 and an unidentified gene, respectively. Here, we report the cloning of the gene mutated in UV(S)S-A by microcell-mediated chromosome transfer. The predicted human gene UVSSA (formerly known as KIAA1530)(7) corrects defective TCR in UV(S)S-A cells. We identify three nonsense and frameshift UVSSA mutations in individuals with UV(S)S-A, indicating that UVSSA is the causative gene for this syndrome. The UVSSA protein forms a complex with USP7 (ref. 8), stabilizes ERCC6 and restores the hypophosphorylated form of RNA polymerase II after UV irradiation.

3-nitrobenzanthrone (3-NBA) is an extremely potent mutagen in the Salmonella reversion assay and a suspected human carcinogen identified in diesel exhaust and in ambient airborne particulate matter. To evaluate the in vivo mutagenicity of 3-NBA, we analyzed the mutant frequency (MF) in the cII gene of various organs (lung, liver, kidney, bladder, colon, spleen, and testis) in lambda/lacZ transgenic mice (Muta Mouse) after intraperitoneal treatment with 3-NBA (25 mg/kg body weight injected once a week for 4 weeks). Increases in MF were found in colon, liver, and bladder, with 7.0-, 4.8-, and 4.1-fold increases above the control value, respectively, whereas no increase in MF was found in lung, kidney, spleen, and testis. Simultaneously, induction of micronuclei in peripheral blood reticulocytes was observed. The sequence alterations in the cII gene recovered from 41 liver mutants from 3-NBA-treated mice were compared with 32 spontaneous mutants from untreated mice. Base substitution mutations predominated for both the 3-NBA-treated (80%) and the untreated (81%) groups. However, the proportion of G:C-->T:A transversions in the mutants from 3-NBA-treated mice was higher (49% vs. 6%) and the proportion of G:C-->A:T transitions was lower than those from untreated mice (10% vs. 66%). The increase in MF in the liver was associated with strong DNA binding by 3-NBA, whereas in lung, in which there was no increase in MF, a low level of DNA binding was observed (268.0-282.7 vs. 8.8-15.9 adducts per 10(8) nucleotides). DNA adduct patterns with multiple adduct spots, qualitatively similar to those formed in vitro after activation of 3-NBA with nitroreductases and in vivo in rats, were observed in all tissues examined. Using high-pressure liquid cochromatographic analysis, we confirmed that all major 3-NBA-DNA adducts produced in vivo in mice are derived from reductive metabolites bound to purine bases (70-80% with deoxyguanosine and 20-30% with deoxyadenosine in liver). These results suggest that G:C-->T:A transversions induced by 3-NBA are caused by misreplication of adducted guanine residues through incorporation of adenine opposite the adduct (A-rule).

The International Conference on Harmonization (ICH) M7 guideline allows the use of in silico approaches for predicting Ames mutagenicity for the initial assessment of impurities in pharmaceuticals. This is the first international guideline that addresses the use of quantitative structure–activity relationship (QSAR) models in lieu of actual toxicological studies for human health assessment. Therefore, QSAR models for Ames mutagenicity now require higher predictive power for identifying mutagenic chemicals. To increase the predictive power of QSAR models, larger experimental datasets from reliable sources are required. The Division of Genetics and Mutagenesis, National Institute of Health Sciences (DGM/NIHS) of Japan recently established a unique proprietary Ames mutagenicity database containing 12140 new chemicals that have not been previously used for developing QSAR models. The DGM/NIHS provided this Ames database to QSAR vendors to validate and improve their QSAR tools. The Ames/QSAR International Challenge Project was initiated in 2014 with 12 QSAR vendors testing 17 QSAR tools against these compounds in three phases. We now present the final results. All tools were considerably improved by participation in this project. Most tools achieved >50% sensitivity (positive prediction among all Ames positives) and predictive power (accuracy) was as high as 80%, almost equivalent to the inter-laboratory reproducibility of Ames tests. To further increase the predictive power of QSAR tools, accumulation of additional Ames test data is required as well as re-evaluation of some previous Ames test results. Indeed, some Ames-positive or Ames-negative chemicals may have previously been incorrectly classified because of methodological weakness, resulting in false-positive or false-negative predictions by QSAR tools. These incorrect data hamper prediction and are a source of noise in the development of QSAR models. It is thus essential to establish a large benchmark database consisting only of well-validated Ames test results to build more accurate QSAR models.

The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information.