Transcriptomic fingerprints in human peripheral blood mononuclear cells indicative of genotoxic and non-genotoxic carcinogenic exposure

Human functional genomics reveals toxicological mechanism underlying genotoxicants-induced inflammatory responses under low doses exposure

Understanding the toxicological mechanisms of chemicals is essential for accurate assessments of environmental health risks. Inflammation could play a critical role in the adverse health outcomes caused by genotoxicants; however, the toxicological mechanisms underlying genotoxicants-induced inflammatory response are still limited. Here, functional genomics CRISPR screens were performed to enhance the mechanistic understanding of the genotoxicants-induced inflammatory response at low doses exposure. Key genes and pathways associated with the activities of immune cells and the production of cytokines were identified by CRISPR screens of 6 model genotoxicants. Gene network analysis revealed that three genes (TLR10, HCAR2 and TRIM6) were involved in the regulation of neutrophil apoptosis and cytokine release, and TLR10 shared a similar functional pattern with HCAR2 and TRIM6. Furthermore, adverse outcome pathway (AOP) network analysis revealed that TLR10 was involved in the molecular initiating events (MIEs) or key events (KEs) in the inflammatory response AOPs of all the 6 genotoxicants, which provided mechanistic links between TLR10 and genotoxicants-induced inflammation and respiratory diseases. Finally, functional validation tests demonstrated that TLR10 exhibited inhibitory effects on genotoxicants-induced inflammatory responses in both epithelial and immune cells. This study highlights the powerful utility of the integration of CRISPR screen and AOP network analysis in illuminating the toxicological causal mechanisms of environmental chemicals.

Comparative Analysis of Transcriptional Responses to Genotoxic and Non-Genotoxic Agents in the Blood Cell Model TK6 and the Liver Model HepaRG

Transcript signatures are a promising approach to identify and classify genotoxic and non-genotoxic compounds and are of interest as biomarkers or for future regulatory application. Not much data, however, is yet available about the concordance of transcriptional responses in different cell types or tissues. Here, we analyzed transcriptomic responses to selected genotoxic food contaminants in the human p53-competent lymphoblastoid cell line TK6 using RNA sequencing. Responses to treatment with five genotoxins, as well as with four non-genotoxic liver toxicants, were compared with previously published gene expression data from the human liver cell model HepaRG. A significant overlap of the transcriptomic changes upon genotoxic stress was detectable in TK6 cells, whereas the comparison with the HepaRG model revealed considerable differences, which was confirmed by bioinformatic data mining for cellular upstream regulators or pathways. Taken together, the study presents a transcriptomic signature for genotoxin exposure in the human TK6 blood cell model. The data demonstrate that responses in different cell models have considerable variations. Detection of a transcriptomic genotoxin signature in blood cells indicates that gene expression analyses of blood samples might be a valuable approach to also estimate responses to toxic exposure in target organs such as the liver.

ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets

In the past few decades, major initiatives have been launched around the world to address chemical safety testing. These efforts aim to innovate and improve the efficacy of existing methods with the long-term goal of developing new risk assessment paradigms. The transcriptomic and toxicological profiling of mammalian cells has resulted in the creation of multiple toxicogenomic datasets and corresponding tools for analysis. To enable easy access and analysis of these valuable toxicogenomic data, we have developed ToxicoDB (toxicodb.ca), a free and open cloud-based platform integrating data from large in vitro toxicogenomic studies, including gene expression profiles of primary human and rat hepatocytes treated with 231 potential toxicants. To efficiently mine these complex toxicogenomic data, ToxicoDB provides users with harmonized chemical annotations, time- and dose-dependent plots of compounds across datasets, as well as the toxicity-related pathway analysis. The data in ToxicoDB have been generated using our open-source R package, ToxicoGx (github.com/bhklab/ToxicoGx). Altogether, ToxicoDB provides a streamlined process for mining highly organized, curated, and accessible toxicogenomic data that can be ultimately applied to preclinical toxicity studies and further our understanding of adverse outcomes.

The promise of toxicogenomics for genetic toxicology: past, present and future

Toxicogenomics, the application of genomics to toxicology, was described as 'a new era' for toxicology. Standard toxicity tests typically involve a number of short-term bioassays that are costly, time consuming, require large numbers of animals and generally focus on a single end point. Toxicogenomics was heralded as a way to improve the efficiency of toxicity testing by assessing gene regulation across the genome, allowing rapid classification of compounds based on characteristic expression profiles. Gene expression microarrays could measure and characterise genome-wide gene expression changes in a single study and while transcriptomic profiles that can discriminate between genotoxic and non-genotoxic carcinogens have been identified, challenges with the approach limited its application. As such, toxicogenomics did not transform the field of genetic toxicology in the way it was predicted. More recently, next generation sequencing (NGS) technologies have revolutionised genomics owing to the fact that hundreds of billions of base pairs can be sequenced simultaneously cheaper and quicker than traditional Sanger methods. In relation to genetic toxicology, and thousands of cancer genomes have been sequenced with single-base substitution mutational signatures identified, and mutation signatures have been identified following treatment of cells with known or suspected environmental carcinogens. RNAseq has been applied to detect transcriptional changes following treatment with genotoxins; modified RNAseq protocols have been developed to identify adducts in the genome and Duplex sequencing is an example of a technique that has recently been developed to accurately detect mutation. Machine learning, including MutationSeq and SomaticSeq, has also been applied to somatic mutation detection and improvements in automation and/or the application of machine learning algorithms may allow high-throughput mutation sequencing in the future. This review will discuss the initial promise of transcriptomics for genetic toxicology, and how the development of NGS technologies and new machine learning algorithms may finally realise that promise.

Rationale and Roadmap for Developing Panels of Hotspot Cancer Driver Gene Mutations as Biomarkers of Cancer Risk

Cancer driver mutations (CDMs) are necessary and causal for carcinogenesis and have advantages as reporters of carcinogenic risk. However, little progress has been made toward developing measurements of CDMs as biomarkers for use in cancer risk assessment. Impediments for using a CDM-based metric to inform cancer risk include the complexity and stochastic nature of carcinogenesis, technical difficulty in quantifying low-frequency CDMs, and lack of established relationships between cancer driver mutant fractions and tumor incidence. Through literature review and database analyses, this review identifies the most promising targets to investigate as biomarkers of cancer risk. Mutational hotspots were discerned within the 20 most mutated genes across the 10 deadliest cancers. Forty genes were identified that encompass 108 mutational hotspot codons overrepresented in the COSMIC database; 424 different mutations within these hotspot codons account for approximately 63,000 tumors and their prevalence across tumor types is described. The review summarizes literature on the prevalence of CDMs in normal tissues and suggests such mutations are direct and indirect substrates for chemical carcinogenesis, which occurs in a spatially stochastic manner. Evidence that hotspot CDMs (hCDMs) frequently occur as tumor subpopulations is presented, indicating COSMIC data may underestimate mutation prevalence. Analyses of online databases show that genes containing hCDMs are enriched in functions related to intercellular communication. In its totality, the review provides a roadmap for the development of tissue-specific, CDM-based biomarkers of carcinogenic potential, comprised of batteries of hCDMs and can be measured by error-correct next-generation sequencing. Environ. Mol. Mutagen. 61:152-175, 2020. Published 2019. This article is a U.S. Government work and is in the public domain in the USA. Environmental and Molecular Mutagenesis published by Wiley Periodicals, Inc. on behalf of Environmental Mutagen Society.

Effect of aflatoxin B1, benzo[a]pyrene, and methapyrilene on transcriptomic and epigenetic alterations in human liver HepaRG cells

The increasing number of man-made chemicals in the environment that may pose a carcinogenic risk highlights the need for developing reliable time- and cost-effective approaches for carcinogen detection and identification. To address this issue, we investigated the utility of high-throughput microarray gene expression and next-generation genome-wide DNA methylation sequencing for the in vitro identification of genotoxic and non-genotoxic carcinogens. Terminally differentiated and metabolically competent human liver HepaRG cells were treated at minimally cytotoxic concentrations of (i) the genotoxic human liver carcinogen aflatoxin B₁ (AFB1) and its structural non-carcinogenic analog aflatoxin B₂ (AFB2); (ii) the genotoxic human lung carcinogen benzo[a]pyrene (B[a]P) and its non-carcinogenic isomer benzo[e]pyrene (B[e]P); and (iii) the non-genotoxic liver carcinogen methapyrilene for 72 h and transcriptomic and DNA methylation profiles were examined. Treatment of HepaRG cells with the liver carcinogens AFB1 and methapyrilene generated distinct gene-expression profiles, whereas B[a]P had only a slight effect on gene expression. In contrast to transcriptomic alterations, treatment of HepaRG cells with the carcinogenic and non-carcinogenic chemicals resulted in profound changes in the DNA methylation footprint; however, the correlation between gene-specific DNA methylation and gene expression changes was minimal. Among the carcinogen-altered genes, transferrin (TF) emerged as sensitive marker for an initial screening of chemicals for their potential liver carcinogenicity. Potential liver carcinogens (i.e., chemicals causing altered TF gene expression) could then be subjected to gene-expression analyses to differentiate genotoxic from non-genotoxic liver carcinogens. This approach may substantially enhance the identification and assessment of potential liver carcinogens.

Comparison of toxicogenomics and traditional approaches to inform mode of action and points of departure in human health risk assessment of benzo[a]pyrene in drinking water

Toxicogenomics is proposed to be a useful tool in human health risk assessment. However, a systematic comparison of traditional risk assessment approaches with those applying toxicogenomics has never been done. We conducted a case study to evaluate the utility of toxicogenomics in the risk assessment of benzo[a]pyrene (BaP), a well-studied carcinogen, for drinking water exposures. Our study was intended to compare methodologies, not to evaluate drinking water safety. We compared traditional (RA1), genomics-informed (RA2) and genomics-only (RA3) approaches. RA2 and RA3 applied toxicogenomics data from human cell cultures and mice exposed to BaP to determine if these data could provide insight into BaP's mode of action (MOA) and derive tissue-specific points of departure (POD). Our global gene expression analysis supported that BaP is genotoxic in mice and allowed the development of a detailed MOA. Toxicogenomics analysis in human lymphoblastoid TK6 cells demonstrated a high degree of consistency in perturbed pathways with animal tissues. Quantitatively, the PODs for traditional and transcriptional approaches were similar (liver 1.2 vs. 1.0 mg/kg-bw/day; lungs 0.8 vs. 3.7 mg/kg-bw/day; forestomach 0.5 vs. 7.4 mg/kg-bw/day). RA3, which applied toxicogenomics in the absence of apical toxicology data, demonstrates that this approach provides useful information in data-poor situations. Overall, our study supports the use of toxicogenomics as a relatively fast and cost-effective tool for hazard identification, preliminary evaluation of potential carcinogens, and carcinogenic potency, in addition to identifying current limitations and practical questions for future work.