Kazuki Izawa

Symposium 3 of the 53rd Annual Meeting of the Japanese Environmental Mutagen and Genome Society (JEMS), entitled “Potential for Computational Genotoxicity,” was held at Shujitsu University, Okayama, Japan, on December 8, 2024. The symposium discussed the application of advanced informatics technologies, such as (quantitative) structure-activity relationship ((Q)SAR) and error-corrected next-generation sequencing (ecNGS), to the field of genotoxicity within the framework of computational genotoxicity. In this symposium, we invited three scientists who are global leaders in the field of computational genotoxicity. This report summarizes the key discussions and presentations from the symposium. The organizers hope this summary will increase awareness of computational genotoxicity.

Background: Overcoming species differences in metabolism between humans and animals remains a critical challenge in toxicological studies. Rat liver S9 fraction has long been the gold standard for exogenous metabolic activation in in vitro genotoxicity tests. Experiences with human S9 or human primary hepatocytes have suggested that the human materials are unsuitable for standardized testing due to high variability. Nevertheless, there is growing interest in genotoxicity evaluation using metabolic systems that more closely mimic human physiology. Results: We developed an in-cell ELISA system to measure γH2AX as a DNA damage marker in stable human hepatocytes (γH2AX-SHE). HepaSH cells are consistently available human hepatocytes that stably express a range of metabolic enzymes and drug transporters in vitro. Due to their highly differentiated and non-proliferative nature, conventional genotoxicity endpoints such as micronuclei formation, chromosomal aberrations, or mutant colony assays are not applicable. We used γH2AX, a sensitive DNA damage marker, in this assay system. Indirect mutagens including benzo(a)pyrene, aristolochic acid, and 2-Amino-1-methyl-6-phenylimidazo(4,5-b)pyridine induced dose-dependent increases in γH2AX across all three HepaSH strains. Time-course analysis following benzo(a)pyrene exposure indicated that a treatment duration of 16 hours or longer was necessary to detect genotoxic responses. Prolonged exposure for 48 hours resulted in extensive cell death, which may interfere with γH2AX quantification. Conclusions: We demonstrated that γH2AX-SHE can serve as a valuable tool for detecting DNA damage under conditions that mimic human metabolic activity. Based on the findings in this study, we recommend the following assay conditions for γH2AX-SHE: a 24-hour treatment period, a DMSO concentration not exceeding 1%, and careful interpretation of positive responses observed at highly cytotoxic doses - defined as approximately less than 60% cell survival – as these may lack biological relevance.

Background: Mutagenicity, the ability of chemical agents to cause mutations and potentially lead to cancer, is a critical aspect of substance safety assessment for protecting human health and the environment. Metabolic enzymes activate multiple mutagens in living organisms, thus in vivo animal models provide highly important information for evaluating mutagenicity in human. Rats are considered suitable models as they share a similar metabolic pathway with humans for processing toxic chemical and exhibit higher responsiveness to chemical carcinogens than mice. To assess mutagenicity in rats, transgenic rodents (TGRs) are widely used for in vivo gene mutation assays. However, such assays are labor-intensive and could only detect transgene mutations inserted into the genome. Therefore, introducing a technology to directly detect in vivo mutagenicity in rats would be necessary. The next-generation sequencing (NGS) based error-corrected sequencing technique is a promising approach for such purposes. Results: We investigated the applicability of paired-end and complementary consensus sequencing (PECC-Seq), an error-corrected sequencing technique, for detecting in vivo mutagenicity in the rat liver samples. PECC-Seq allows for the direct detection of ultra-rare somatic mutations in the genomic DNA without being constrained by the genomic locus, tissue, or organism. We tested PECC-Seq feasibility in rats treated with diethylnitrosamine (DEN), a mutagenic compound. Interestingly, the mutation and mutant frequencies between PECC-Seq and the TGR assay displayed a promising correlation. Our results also demonstrated that PECC-Seq could successfully detect the A:T > T:A mutation in rat liver samples, consistent with the TGR assay. Furthermore, we calculated the trinucleotide mutation frequency and proved that PECC-Seq accurately identified the DEN treatment-induced mutational signatures. Conclusions: Our study provides the first evidence of using PECC-Seq for in vivo mutagenicity detection in rat liver samples. This approach could provide a valuable alternative to conventional TGR assays as it is labor- and time-efficient and eliminates the need for transgenic rodents. Error-corrected sequencing techniques, such as PECC-Seq, represent promising approaches for enhancing mutagenicity assessment and advancing regulatory science.

Metagenomic analysis, a technique used to comprehensively analyze microorganisms present in the environment, requires performing high-precision homology searches on large amounts of sequencing data, the size of which has increased dramatically with the development of next-generation sequencing. NCBI BLAST is the most widely used software for performing homology searches, but its speed is insufficient for the throughput of current DNA sequencers. In this paper, we propose a new, high-performance homology search algorithm that employs a two-step seed search strategy using multiple reduced amino acid alphabets to identify highly similar subsequences. Additionally, we evaluated the validity of the proposed method against several existing tools. Our method was faster than any other existing program for ≤120,000 queries, while DIAMOND, an existing tool, was the fastest method for >120,000 queries.

Periodontitis is an inflammation of tooth-supporting tissues, which is caused by bacteria in the subgingival plaque (biofilm) and the host immune response. Traditionally, subgingival pathogens have been investigated using methods such as culturing, DNA probes, or PCR. The development of next-generation sequencing made it possible to investigate the whole microbiome in the subgingival plaque. Previous studies have implicated dysbiosis of the subgingival microbiome in the etiology of periodontitis. However, details are still lacking. In this study, we conducted a metagenomic analysis of subgingival plaque samples from a group of Japanese individuals with and without periodontitis. In the taxonomic composition analysis, genus Bacteroides and Mycobacterium demonstrated significantly different compositions between healthy sites and sites with periodontal pockets. The results from the relative abundance of functional gene categories, carbohydrate metabolism, glycan biosynthesis and metabolism, amino acid metabolism, replication and repair showed significant differences between healthy sites and sites with periodontal pockets. These results provide important insights into the shift in the taxonomic and functional gene category abundance caused by dysbiosis, which occurs during the progression of periodontal disease.

Fungal community analyses in homes have been attracting attention because fungi are now generally considered to be allergens. Currently, these analyses are generally conducted using the culture method, although fungal communities in households often contain species that are difficult to culture. In contrast, next-generation sequencing (NGS) represents a comprehensive, labor- and time-saving approach that can facilitate species identification. However, the reliability of the NGS method has not been compared to that of the culture method. In this study, in an attempt to demonstrate the reliability of this application, we used the NGS method to target the internal transcribed spacer 1 (ITS1) in the fungal genome, conducted fungal community analyses for 18 house-dust samples and analyzed fungal community structures. The NGS method positively correlated with the culture method regarding the relative abundance of Aspergillus, Penicillium, Cladosporium and yeasts, which represent the major fungal components found in houses. Furthermore, several genera, such as Malassezia, could be sensitively detected. Our results imply that the reliability of the NGS method is comparable to that of the culture method and indicates that easily available databases may require modifications, including the removal of registrations that have not been sufficiently classified at the genus level.

Citreoviridin (CTV) is a mycotoxin that is produced by Aspergillus terreus, Eupenicillium ochrosalmoneum and Penicillium citreonigrum, and CTV has been detected in a wide range of cereal grains throughout the world. Furthermore, it is especially a serious problem in regions where rice is consumed as a staple food. Moreover, CTV is a well-known yellow rice toxin, and outbreaks of beriberi have occurred due to consumption of rice that is contaminated by CTV even in the recent years. Although CTV biosynthetic genes of A. terreus have been described, those of P. citreonigrum remain unclear, which is concerning since P. citreonigrum is the main cause of CTV contamination in rice. In the present study, we determined the draft genome of the P. citreonigrum strain IMI92228 and revealed the presence of all four genes that form a gene cluster and that are homologous to the CTV biosynthesis genes of A. terreus. The expression of these four homologous genes were highly correlated with CTV production, suggesting that they may play an important role in CTV biosynthesis in P. citreonigrum. We concluded that the gene cluster is a CTV biosynthesis cluster of P. citreonigrum. The findings will contribute to the understanding of the biosynthetic pathway of CTV and will ultimately lead to improvements in the CTV management of agricultural products.

Metagenome techniques allow analyses of microorganisms and their genes present in a given environment without isolation and culture. Thus, metagenomics has become a broadly applied tool to study various environments and elucidate the relationship between diseases and the host microbiota. With continuous improvement in the performance of genome sequencers, the number of sequence reads generated has increased exponentially; thus, methods for the efficient processing of such large numbers of sequences are required. To this end, we developed the pipeline system, GHOSTMEGAN, to speed up the processing of large-scale whole genome shotgun metagenome analysis, which integrates the sequence homology search tool GHOSTZ-GPU and the analyzing tool MEGAN. Assuming a cluster-type computer with a job scheduling system, the multi-node parallel processing of GHOSTZ-GPU and MEGAN was pipelined. Performance evaluation of GHOSTMEGAN with a whole genome sequence dataset, the oral metagenome demonstrated that execution of 128 nodes in parallel, which required 15 h on a single node, could be completed in only 20 min, thereby achieving about 45 times faster calculation. This pipeline is expected to greatly accelerate the field of metagenomics and broaden its application potential.

Sequence similarity searches have been widely used in the analyses of metagenomic sequencing data. Finding homologous sequences in a reference database enables the estimation of taxonomic and functional characteristics of each query sequence. Because current metagenomic sequencing data consist of a large number of nucleotide sequences, the time required for sequence similarity searches account for a large proportion of the total time. This time-consuming step makes it difficult to perform large-scale analyses. To analyze large-scale metagenomic data, such as those found in the human oral microbiome, we developed GHOST-MP (Genome-wide HOmology Search Tool on Massively Parallel system), a parallel sequence similarity search tool for massively parallel computing systems. This tool uses a fast search algorithm based on suffix arrays of query and database sequences and a hierarchical parallel search to accelerate the large-scale sequence similarity search of metagenomic sequencing data. The parallel computing efficiency and the search speed of this tool were evaluated. GHOST-MP was shown to be scalable over 10,000 CPU (Central Processing Unit) cores, and achieved over 80-fold acceleration compared with mpiBLAST using the same computational resources. We applied this tool to human oral metagenomic data, and the results indicate that the oral cavity, the oral vestibule, and plaque have different characteristics based on the functional gene category.