Transcriptome analysis creates a new era of precision medicine for managing recurrent hepatocellular carcinoma

The high incidence of hepatocellular carcinoma (HCC) recurrence negatively impacts outcomes of patients treated with curative intent despite advances in surgical techniques and other locoregional liver-targeting therapies. Over the past few decades, the emergence of transcriptome analysis tools, including real-time quantitative reverse transcription PCR, microarrays, and RNA sequencing, has not only largely contributed to our knowledge about the pathogenesis of recurrent HCC but also led to the development of outcome prediction models based on differentially expressed gene signatures. In recent years, the single-cell RNA sequencing technique has revolutionized our ability to study the complicated crosstalk between cancer cells and the immune environment, which may benefit further investigations on the role of different immune cells in HCC recurrence and the identification of potential therapeutic targets. In the present article, we summarized the major findings yielded with these transcriptome methods within the framework of a causal model consisting of three domains: primary cancer cells; carcinogenic stimuli; and tumor microenvironment. We provided a comprehensive review of the insights that transcriptome analyses have provided into diagnostics, surveillance, and treatment of HCC recurrence.

RNA A-to-I editing, environmental exposure, and human diseases

Epigenetic modifications have gained attention since they can be potentially changed with environmental stimuli and can be associated with adverse health outcomes. Epitranscriptome field has begun to attract attention with several aspects since RNA modifications have been linked with critical biological processes and implicated in diseases. Several RNA modifications have been identified as reversible indicating the dynamic features of modification which can be altered by environmental cues. Currently, we know more than 150 RNA modifications in different organisms and on different bases which are modified by various chemical groups. RNA editing, which is one of the RNA modifications, occurs after transcription, which results in RNA sequence different from its corresponding DNA sequence. Emerging evidence reveals the functions of RNA editing as well as the association between RNA editing and diseases. However, the RNA editing field is beginning to grow up and needs more empirical evidence in regard to disease and toxicology. Thus, this review aims to provide the current evidence-based studies on RNA editing modifying genes for genotoxicity and cancer. The review presented the association between environmental xenobiotics exposure and RNA editing modifying genes and focused on the association between the expression of RNA editing modifying genes and cancer. Furthermore, we discussed the future directions of scientific studies in the area of RNA modifications, especially in the RNA editing field, and provided a knowledge-based framework for further studies.

In vivo assessment of respiratory burst inhibition by xenobiotic exposure using larval zebrafish

Currently, assessment of the potential immunotoxicity of a given agent involves a tiered approach for hazard identification and mechanistic studies, including observational studies, evaluation of immune function, and measurement of susceptibility to infectious and neoplastic diseases. These studies generally use costly low-throughput mammalian models. Zebrafish, however, offer an excellent alternative due to their rapid development, ease of maintenance, and homology to mammalian immune system function and development. Larval zebrafish also are a convenient model to study the innate immune system with no interference from the adaptive immune system. In this study, a respiratory burst assay (RBA) was utilized to measure reactive oxygen species (ROS) production after developmental xenobiotic exposure. Embryos were exposed to non-teratogenic doses of chemicals and at 96 h post-fertilization, the ability to produce ROS was measured. Using the RBA, 12 compounds with varying immune-suppressive properties were screened. Seven compounds neither suppressed nor enhanced the respiratory burst; five reproducibly suppressed global ROS production, but with varying potencies: benzo[a]pyrene, 17β-estradiol, lead acetate, methoxychlor, and phenanthrene. These five compounds have all previously been reported as immunosuppressive in mammalian innate immunity assays. To evaluate whether the suppression of ROS by these compounds was a result of decreased immune cell numbers, flow cytometry with transgenic zebrafish larvae was used to count the numbers of neutrophils and macrophages after chemical exposure. With this assay, benzo[a]pyrene was found to be the only chemical that induced a change in the number of immune cells by increasing macrophage but not neutrophil numbers. Taken together, this work demonstrates the utility of zebrafish larvae as a vertebrate model for identifying compounds that impact innate immune function at non-teratogenic levels and validates measuring ROS production and phagocyte numbers as metrics for monitoring how xenobiotic exposure alters the innate immune system.

Classifying polycyclic aromatic hydrocarbons by carcinogenic potency using in vitro biosignatures

One of the most difficult challenges for risk assessment is evaluation of chemicals that predominately co-occur in mixtures like polycyclic aromatic hydrocarbons (PAHs). We previously developed a classification model in which systems biology data collected from mice short-term after chemical exposure accurately predict tumor outcome. The present study demonstrates translation of this approach into a human in vitro model in which chemical-specific bioactivity profiles from 3D human bronchial epithelial cells (HBEC) classify PAHs by carcinogenic potency. Gene expression profiles were analyzed from HBEC exposed to carcinogenic and non-carcinogenic PAHs and classification accuracies were identified for individual pathway-based gene sets. Posterior probabilities of best performing gene sets were combined via Bayesian integration resulting in a classifier with four gene sets, including aryl hydrocarbon receptor signaling, regulation of epithelial mesenchymal transition, regulation of angiogenesis, and cell cycle G2-M. In addition, transcriptional benchmark dose modeling of benzo[a]pyrene (BAP) showed that the most sensitive gene sets to BAP regulation were largely dissimilar from those that best classified PAH carcinogenicity challenging current assumptions that BAP carcinogenicity (and subsequent mode of action) is reflective of overall PAH carcinogenicity. These results illustrate utility of using systems toxicology approaches to analyze global gene expression towards carcinogenic hazard assessment.

Environmental epitranscriptomics

Chemical modifications of RNA molecules have gained increasing attention since evidence emerged for their substantive roles in a range of biological processes, such as the stability and translation of mRNA transcripts. More than 150 modifications have been identified in different organisms to date, collectively known as the 'epitranscriptome', with 6-methyladenosine (m⁶A), 5-methylcytidine (m⁵C), pseudouridine and N1-methyladenosine (m¹A) the most extensively investigated. Although we are just beginning to elucidate the roles of these modifications in cellular functions, there is already evidence for their dysregulation in diseases such as cancer and neurodevelopmental disorders. There is currently more limited knowledge regarding how environmental exposures affect the epitranscriptome and how this may mediate disease risk, but evidence is beginning to emerge. Here, we review the current evidence for the impact of environmental exposures such as benzo[a]pyrene, bisphenol A, pesticides, metals and nanoparticles upon RNA modifications and the expression of their 'writers' (methyl transferases), 'erasers' (demethylases) and 'readers'. We discuss future directions of the field and identify areas of particular promise and consider the technical challenges that are faced.

Environmental exposures and RNA N6-Methyladenosine modified long Non-Coding RNAs

Recent advances in the field of RNA modifications and long non-coding RNAs (lncRNAs) have provided substantial evidence on important biological functions. LncRNAs are defined as longer than 200 nucleotides which are not translated into proteins. The term "epitranscriptome" refers to all modifications in RNA types. Adenine-6 methylation (m⁶A) is the most common, dynamic and prominent modifications in coding and non-coding RNAs and has critical and previously unappreciated functional roles. Accumulation evidence indicated the association between RNA m6A modification and cancer and nonmalignant diseases. Recent studies reported that several lncRNAs including MALAT1, MEG3, XIST, GAS5, and KCNK15-AS1 are subject to m⁶A modification. It can be suggested that lncRNAs modified by m⁶A modification have substantive roles in diseases. Currently limited data are available regarding how environmental exposure affects m⁶A-modified lncRNAs. Furthermore, we do not know the interaction of environmental exposure and m⁶A-modified lncRNAs in development of adverse human health outcomes. Thus, in this systematic review, we aimed to present the data of the studies that reported a significant association between environmental exposure and expression/DNA methylation of m⁶A-modified long non-coding RNAs.

Transcriptomic effect marker patterns of genotoxins - a comparative study with literature data

Microarray approaches are frequently used experimental tools which have proven their value for example in the characterization of the molecular mode of action of toxicologically relevant compounds. In a regulatory context, omics techniques are still not routinely used, amongst others due to lacking standardization in experimental setup and data processing, and also due to issues with the definition of adversity. In order to exemplarily determine whether consensus transcript biomarker signatures for a certain toxicological endpoint can be derived from published microarray datasets, we here compared transcriptome data from human HepaRG hepatocarcinoma cells treated with different genotoxins, based on re-analyzed datasets extracted from the literature. Comparison of the resulting data show that even with similarly-acting compounds in the same cell line, considerable variation was observed with respect to the numbers and identities of differentially expressed genes. Greater concordance was observed when considering the whole data sets and biological functions associated with the genes affected. The present results highlight difficulties and possibilities in inter-experiment comparisons of omics data and underpin the need for future efforts towards improved standardization to facilitate the use of omics data in risk assessment. Existing omics datasets may nonetheless prove valuable in establishing biological context information essential for the development of adverse outcome pathways.

Evaluation of 12 mouse marker genes in rat toxicogenomics public data, Open TG-GATEs: Discrimination of genotoxic from non-genotoxic hepatocarcinogens

Previously, we proposed 12 marker genes (Aen, Bax, Btg2, Ccnf, Ccng1, Cdkn1a, Gdf15, Lrp1, Mbd1, Phlda3, Plk2 and Tubb4b) to discriminate mouse genotoxic hepatocarcinogens (GTHC) from non-genotoxic hepatocarcinogens (NGTHC). This was determined by qPCR and principal component analysis (PCA), as the aim of an in vivo short-term screening for genotoxic hepatocarcinogens. For this paper, we conducted an application study of the 12 mouse marker genes to rat data, Open TG-GATEs (public data). We analyzed five typical rat GTHC (2-acetamodofluorene, aflatoxin B1, 2-nitrofluorene, N-nitrosodiethylamine and N-nitrosomorpholine), and not only seven typical rat NGTHC (clofibrate, ethanol, fenofibrate, gemfibrozil, hexachlorobenzene, phenobarbital and WY-14643) but also 11 non-genotoxic non-hepatocarcinogens (NGTNHC; allyl alcohol, aspirin, caffeine, chlorpheniramine, chlorpropamide, dexamethasone, diazepam, indomethacin, phenylbutazone, theophylline and tolbutamide) from Open TG-GATEs. The analysis was performed at 3, 6, 9 and 24 h after a single administration and 4, 8, 15 and 29 days after repeated administrations. We transferred Open TG-GATEs DNA microarray data into log₂ data using the "R Project for Statistical Computing". GTHC-specific dose-dependent gene expression changes were observed and significance assessed with the Williams test. Similar significant changes were observed during 3-24 h and 4-29 days, assessed with Welch's t-test, except not for NGTHC or NGTNHC. Significant differential changes in gene expression were observed between GTHC and NGTHC in 11 genes (except not Tubb4b) and between GTHC and NGTNHC in all 12 genes at 24 h and 10 genes (except Ccnf and Mbd1) at 29 days, per Tukey's test. PCA successfully discriminated GTHC from NGTHC and NGTNHC at 24 h and 29 days. The results demonstrate that 12 previously proposed mouse marker genes are useful for discriminating rat GTHC from NGTHC and NGTNHC from Open TG-GATEs.

Comparative transcriptome analysis between the short-term stress and long-term adaptation of the Ruditapes philippinarum in response to benzo[a]pyrene

In order to monitor the pollution of polycyclic aromatic hydrocarbons (PAHs) in the seawater environment, screening biomarkers capable of monitoring PAHs is the focus of many studies. The transcriptomic profiles of the digestive gland tissue from the R. philippinarum groups after the exposure to BaP (4 μg/L) at four time points (0, 0.5, 6 and 15 days) were investigated to globally screen the key genes and pathways involved in the responses to short-term stress and long-term adaptation of BaP resistance. By comparative transcriptome analysis, 233, 282 and 58 differentially expressed genes (DEGs) were identified at 0.5 day, 6 day and 15 day (vs 0 day). The differential expression genes were related to stress response, detoxification metabolic process and innate immunity. DEGs of each group at different stages were clustered in six profiles based on gene expression pattern. Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analysis were used on all genes to determine the biological functions and processes. We selected Multidrug resistance protein 3 (MRP3), transcriptional regulator ATRX-like isoform X2 (ATRX) as biomarker indicator genes for short-term pollution monitoring and NADH dehydrogenase [ubiquinone] 1 (NQO1), Complement C1q-like protein 4 (C1q), Glutathione-S-transferase theta (GST), E3 ubiquitin-protein ligase (E3) for long-term pollution monitoring based on the different expression patterns and the function in detoxification and antioxidant defense system. Besides, the expression of seven genes was measured through Quantitative real-time PCR (qPCR) according to their gene expression patterns which was confirmed by the DGE analysis. Taken together, adoption of transcriptomic analysis to explore the bivalves' mRNA abundance changes and detoxification metabolic mechanism under the BaP stress at different time points can aid the development of sensitive and informed molecular endpoints for application towards ecotoxicogenomic monitoring of bivalves.

Using RNA-Seq with 11 marker genes to evaluate 1,4-dioxane compared with typical genotoxic and non-genotoxic rat hepatocarcinogens

It has long been unclear whether 1,4-dioxane (DO) is a genotoxic hepatocarcinogen (GTHC). Therefore, the present study aimed to evaluate rat GTHCs and non-genotoxic hepatocarcinogens (NGTHCs) via selected gene expression patterns in the liver, as determined by next generation sequencing-targeted mRNA sequencing (RNA-Seq) and principal component analysis (PCA). Previously, we selected 11 marker genes (Aen, Bax, Btg2, Ccnf, Ccng1, Cdkn1a, Lrp1, Mbd1, Phlda3, Plk2, and Tubb4b) to discriminate GTHCs and NGTHCs. In the present study, we quantified changes in the expression of these genes following DO treatment, and compared them with treatment with two typical rat GTHCs, N-nitrosodiethylamine (DEN) and 3,3'-dimethylbenzidine·2HCl (DMB), and a typical rat NGTHC, di(2-ethylhexyl)phthalate (DEHP). RNA-Seq was conducted on liver samples from groups of five male, 10-week-old F344 rats after 4 weeks' feeding of chemicals in the water or the food. Rats in the control group were given water and a basal diet. Significant changes in gene expression in experimental groups compared with the control group were observed in eight genes (Aen, Bax, Btg2, Ccnf, Ccng1, Cdkn1a, Phlda3 and Plk2), as shown by Tukey's test. Gene expression profiles of the 11 genes under DO treatment differed significantly from those with DEN and DMB, as well as DEHP. Gene expression profiles with DO treatment differed partially from those with typical GTHCs for five genes (Bax, Btg2, Cdkn1a, Lrp1 and Plk2) and were substantially different from treatment with a typical NGTHC (DEHP) for nine genes (Aen, Bax, Btg2, Ccnf, Ccng1, Cdkn1a, Mbd1, Phlda3 and Tubb4b) as determined by Tukey's test. Finally, PCA successfully differentiated GTHCs from DEHP and DO with the 11 genes. The present results suggest that RNA-Seq and PCA are useful to evaluate rat typical GTHCs and typical NGTHCs. DO was suggested to result in a different intermediate gene expression profile from typical GTHCs and NGTHC.

Collaborative studies in toxicogenomics in rodent liver in JEMS·MMS; a useful application of principal component analysis on toxicogenomics

Toxicogenomics is a rapidly developing discipline focused on the elucidation of the molecular and cellular effects of chemicals on biological systems. As a collaborative study group of Toxicogenomics/JEMS·MMS, we conducted studies on hepatocarcinogens in rodent liver in which 100 candidate marker genes were selected to discriminate genotoxic hepatocarcinogens from non-genotoxic hepatocarcinogens. Differential gene expression induced by 13 chemicals were examined using DNA microarray and quantitative real-time PCR (qPCR), including eight genotoxic hepatocarcinogens [o-aminoazotoluene, chrysene, dibenzo[a,l]pyrene, diethylnitrosamine (DEN), 7,12-dimethylbenz[a]anthracene, dimethylnitrosamine, dipropylnitrosamine and ethylnitrosourea (ENU)], four non-genotoxic hepatocarcinogens [carbon tetrachloride, di(2-ethylhexyl)phthalate (DEHP), phenobarbital and trichloroethylene] and a non-genotoxic non-hepatocarcinogen [ethanol]. Using qPCR, 30 key genes were extracted from mouse livers at 4 h and 28 days following dose-dependent gene expression alteration induced by DEN and ENU: the most significant changes in gene expression were observed at 4 h. Next, we selected key point times at 4 and 48 h from changes in time-dependent gene expression during the acute phase following administration of chrysene by qPCR. We successfully showed discrimination of eight genotoxic hepatocarcinogens [2-acetylaminofluorene, 2,4-diaminotoluene, diisopropanolnitrosamine, 4-dimethylaminoazobenzene, 4-(methylnitsosamino)-1-(3-pyridyl)-1-butanone, N-nitrosomorpholine, quinoline and urethane] from four non-genotoxic hepatocarcinogens [1,4-dichlorobenzene, dichlorodiphenyltrichloroethane, DEHP and furan] using qPCR and principal component analysis. Additionally, we successfully identified two rat genotoxic hepatocarcinogens [DEN and 2,6-dinitrotoluene] from a nongenotoxic-hepatocarcinogen [DEHP] and a non-genotoxic non-hepatocarcinogen [phenacetin] at 4 and 48 h. The subsequent gene pathway analysis by Ingenuity Pathway Analysis extracted the DNA damage response, resulting from the signal transduction of a p53-class mediator leading to the induction of apoptosis. The present review of these studies suggests that application of principal component analysis on the gene expression profile in rodent liver during the acute phase is useful to predict genotoxic hepatocarcinogens in comparison to non-genotoxic hepatocarcinogens and/or non-carcinogenic hepatotoxins.

Exploiting microRNA and mRNA profiles generated in vitro from carcinogen-exposed primary mouse hepatocytes for predicting in vivo genotoxicity and carcinogenicity

The well-defined battery of in vitro systems applied within chemical cancer risk assessment is often characterised by a high false-positive rate, thus repeatedly failing to correctly predict the in vivo genotoxic and carcinogenic properties of test compounds. Toxicogenomics, i.e. mRNA-profiling, has been proven successful in improving the prediction of genotoxicity in vivo and the understanding of underlying mechanisms. Recently, microRNAs have been discovered as post-transcriptional regulators of mRNAs. It is thus hypothesised that using microRNA response-patterns may further improve current prediction methods. This study aimed at predicting genotoxicity and non-genotoxic carcinogenicity in vivo, by comparing microRNA- and mRNA-based profiles, using a frequently applied in vitro liver model and exposing this to a range of well-chosen prototypical carcinogens. Primary mouse hepatocytes (PMH) were treated for 24 and 48h with 21 chemical compounds [genotoxins (GTX) vs. non-genotoxins (NGTX) and non-genotoxic carcinogens (NGTX-C) versus non-carcinogens (NC)]. MicroRNA and mRNA expression changes were analysed by means of Exiqon and Affymetrix microarray-platforms, respectively. Classification was performed by using Prediction Analysis for Microarrays (PAM). Compounds were randomly assigned to training and validation sets (repeated 10 times). Before prediction analysis, pre-selection of microRNAs and mRNAs was performed by using a leave-one-out t-test. No microRNAs could be identified that accurately predicted genotoxicity or non-genotoxic carcinogenicity in vivo. However, mRNAs could be detected which appeared reliable in predicting genotoxicity in vivo after 24h (7 genes) and 48h (2 genes) of exposure (accuracy: 90% and 93%, sensitivity: 65% and 75%, specificity: 100% and 100%). Tributylinoxide and para-Cresidine were misclassified. Also, mRNAs were identified capable of classifying NGTX-C after 24h (5 genes) as well as after 48h (3 genes) of treatment (accuracy: 78% and 88%, sensitivity: 83% and 83%, specificity: 75% and 93%). Wy-14,643, phenobarbital and ampicillin trihydrate were misclassified. We conclude that genotoxicity and non-genotoxic carcinogenicity probably cannot be accurately predicted based on microRNA profiles. Overall, transcript-based prediction analyses appeared to clearly outperform microRNA-based analyses.

Synergistic Gene Expression Signature Observed in TK6 Cells upon Co-Exposure to UVC-Irradiation and Protein Kinase C-Activating Tumor Promoters

Activation of stress response pathways in the tumor microenvironment can promote the development of cancer. However, little is known about the synergistic tumor promoting effects of stress response pathways simultaneously induced in the tumor microenvironment. Therefore, the purpose of this study was to establish gene expression signatures representing the interaction of pathways deregulated by tumor promoting agents and pathways induced by DNA damage. Human lymphoblastoid TK6 cells were pretreated with the protein kinase C activating tumor promoter 12-O-tetradecanoylphorbol-13-acetate (TPA) and exposed to UVC-irradiation. The time and dose-responsive effects of the co-treatment were captured with RNA-sequencing (RNA-seq) in two separate experiments. TK6 cells exposed to both TPA and UVC had significantly more genes differentially regulated than the theoretical sum of genes induced by either stress alone, thus indicating a synergistic effect on global gene expression patterns. Further analysis revealed that TPA+UVC co-exposure caused synergistic perturbation of specific genes associated with p53, AP-1 and inflammatory pathways important in carcinogenesis. The 17 gene signature derived from this model was confirmed with other PKC-activating tumor promoters including phorbol-12,13-dibutyrate, sapintoxin D, mezerein, (-)-Indolactam V and resiniferonol 9,13,14-ortho-phenylacetate (ROPA) with quantitative real-time PCR (QPCR). Here we show a novel gene signature that may represent a synergistic interaction in the tumor microenvironment that is relevant to the mechanisms of chemical induced tumor promotion.

Integrating toxicogenomics into human health risk assessment: lessons learned from the benzo[a]pyrene case study

The use of short-term toxicogenomic tests to predict cancer (or other health effects) offers considerable advantages relative to traditional toxicity testing methods. The advantages include increased throughput, increased mechanistic data, and significantly reduced costs. However, precisely how toxicogenomics data can be used to support human health risk assessment (RA) is unclear. In a companion paper ( Moffat et al. 2014 ), we present a case study evaluating the utility of toxicogenomics in the RA of benzo[a]pyrene (BaP), a known human carcinogen. The case study is meant as a proof-of-principle exercise using a well-established mode of action (MOA) that impacts multiple tissues, which should provide a best case example. We found that toxicogenomics provided rich mechanistic data applicable to hazard identification, dose-response analysis, and quantitative RA of BaP. Based on this work, here we share some useful lessons for both research and RA, and outline our perspective on how toxicogenomics can benefit RA in the short- and long-term. Specifically, we focus on (1) obtaining biologically relevant data that are readily suitable for establishing an MOA for toxicants, (2) examining the human relevance of an MOA from animal testing, and (3) proposing appropriate quantitative values for RA. We describe our envisioned strategy on how toxicogenomics can become a tool in RA, especially when anchored to other short-term toxicity tests (apical endpoints) to increase confidence in the proposed MOA, and emphasize the need for additional studies on other MOAs to define the best practices in the application of toxicogenomics in RA.

Nuclear Receptor Signaling Atlas: Opening Access to the Biology of Nuclear Receptor Signaling Pathways

Signaling pathways involving nuclear receptors (NRs), their ligands and coregulators, regulate tissue-specific transcriptomes in diverse processes, including development, metabolism, reproduction, the immune response and neuronal function, as well as in their associated pathologies. The Nuclear Receptor Signaling Atlas (NURSA) is a Consortium focused around a Hub website (www.nursa.org) that annotates and integrates diverse ‘omics datasets originating from the published literature and NURSA-funded Data Source Projects (NDSPs). These datasets are then exposed to the scientific community on an Open Access basis through user-friendly data browsing and search interfaces. Here, we describe the redesign of the Hub, version 3.0, to deploy “Web 2.0” technologies and add richer, more diverse content. The Molecule Pages, which aggregate information relevant to NR signaling pathways from myriad external databases, have been enhanced to include resources for basic scientists, such as post-translational modification sites and targeting miRNAs, and for clinicians, such as clinical trials. A portal to NURSA’s Open Access, PubMed-indexed journal Nuclear Receptor Signaling has been added to facilitate manuscript submissions. Datasets and information on reagents generated by NDSPs are available, as is information concerning periodic new NDSP funding solicitations. Finally, the new website integrates the Transcriptomine analysis tool, which allows for mining of millions of richly annotated public transcriptomic data points in the field, providing an environment for dataset re-use and citation, bench data validation and hypothesis generation. We anticipate that this new release of the NURSA database will have tangible, long term benefits for both basic and clinical research in this field.

Screening a mouse liver gene expression compendium identifies modulators of the aryl hydrocarbon receptor (AhR)

The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor that mediates the biological and toxic effects of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), dioxin-like compounds (DLC) as well as some drugs and endogenous tryptophan metabolites. Short-term activation of AhR can lead to hepatocellular steatosis, and chronic activation can lead to liver cancer in mice and rats. Analytical approaches were developed to identify biosets in a genomic database in which AhR activity was altered. A set of 63 genes was identified (the AhR gene expression biomarker) that was dependent on AhR for regulation after exposure to TCDD or benzo[a]pyrene and includes the known AhR targets Cyp1a1 and Cyp1b1. A fold-change rank-based test (Running Fisher's test; p-value ≤ 10(-4)) was used to evaluate the similarity between the AhR biomarker and a test set of 37 and 41 biosets positive or negative, respectively for AhR activation. The test resulted in a balanced accuracy of 95%. The rank-based test was used to identify factors that activate or suppress AhR in an annotated mouse liver/mouse primary hepatocyte gene expression database of ∼ 1850 comparisons. In addition to the expected activation of AhR by TCDD and DLC, AhR was activated by AP20189 and phenformin. AhR was suppressed by phenobarbital and 1,4-Bis[2-(3,5-dichloropyridyloxy)] benzene (TCPOBOP) in a constitutive activated receptor (CAR)-dependent manner and pregnenolone-16α-carbonitrile in a pregnane X receptor (PXR)-dependent manner. Inactivation of individual genes in nullizygous models led to AhR activation (Pxr, Ghrhr, Taf10) or suppression (Ahr, Ilst6st, Hnf1a). This study describes a novel screening strategy for identifying factors in mouse liver that perturb AhR in a gene expression compendium.

Of text and gene--using text mining methods to uncover hidden knowledge in toxicogenomics

Background

Toxicogenomics studies often profile gene expression from assays involving multiple doses and time points. The dose- and time-dependent pattern is of great importance to assess toxicity but computational approaches are lacking to effectively utilize this characteristic in toxicity assessment. Topic modeling is a text mining approach, but may be used analogously in toxicogenomics due to the similar data structures between text and gene dysregulation.

Results

Topic modeling was applied to a very large toxicogenomics dataset containing microarray gene expression data from >15,000 samples associated with 131 drugs tested in three different assay platforms (i.e., in vitro assay, in vivo repeated dose study and in vivo single dose experiment) with a design including multiple doses and time points. A set of “topics” which each consist of a set of genes was determined, by which the varying sensitivity of three assay systems was observed. We found that the drug-dependent effect was more pronounced in the two in vivo systems than the in vitro system, while the time-dependent effect was most strongly reflected in the in vitro system followed by the single dose study and lastly the repeated dose experiment. The dose-dependent effect was similar across three assay systems. Although the results indicated a challenge to extrapolate the in vitro results to the in vivo situation, we did notice that, for some drugs but not for all the drugs, the similarity in gene expression patterns was observed across all three assay systems, indicating a possibility of using in vitro systems with careful designs (such as the choice of dose and time point), to replace the in vivo testing strategy. Nonetheless, a potential to replace the repeated dose study by the single-dose short-term methodology was strongly implied.

Conclusions

The study demonstrated that text mining methodologies such as topic modeling provide an alternative method compared to traditional means for data reduction in toxicogenomics, enhancing researchers’ capabilities to interpret biological information.

Identification of biomarkers that distinguish chemical contaminants based on gene expression profiles

Background

High throughput transcriptomics profiles such as those generated using microarrays have been useful in identifying biomarkers for different classification and toxicity prediction purposes. Here, we investigated the use of microarrays to predict chemical toxicants and their possible mechanisms of action.

Results

In this study, in vitro cultures of primary rat hepatocytes were exposed to 105 chemicals and vehicle controls, representing 14 compound classes. We comprehensively compared various normalization of gene expression profiles, feature selection and classification algorithms for the classification of these 105 chemicals into14 compound classes. We found that normalization had little effect on the averaged classification accuracy. Two support vector machine (SVM) methods, LibSVM and sequential minimal optimization, had better classification performance than other methods. SVM recursive feature selection (SVM-RFE) had the highest overfitting rate when an independent dataset was used for a prediction. Therefore, we developed a new feature selection algorithm called gradient method that had a relatively high training classification as well as prediction accuracy with the lowest overfitting rate of the methods tested. Analysis of biomarkers that distinguished the 14 classes of compounds identified a group of genes principally involved in cell cycle function that were significantly downregulated by metal and inflammatory compounds, but were induced by anti-microbial, cancer related drugs, pesticides, and PXR mediators.

Conclusions

Our results indicate that using microarrays and a supervised machine learning approach to predict chemical toxicants, their potential toxicity and mechanisms of action is practical and efficient. Choosing the right feature and classification algorithms for this multiple category classification and prediction is critical.

Discovery of characteristic molecular signatures for the simultaneous prediction and detection of environmental pollutants

Gene expression data may be very promising for the classification of toxicant types, but the development and application of transcriptomic-based gene classifiers for environmental toxicological applications are lacking compared to the biomedical sciences. Also, simultaneous classification across a set of toxicant types has not been investigated extensively. In the present study, we determined the transcriptomic response to three types of ubiquitous toxicants exposure in two types of human cell lines (HepG2 and HL-60), which are useful in vitro human model for evaluation of toxic substances that may affect human hepatotoxicity (e.g., polycyclic aromatic hydrocarbon [PAH] and persistent organic pollutant [POP]) and human leukemic myelopoietic proliferation (e.g., volatile organic compound [VOC]). The findings demonstrate characteristic molecular signatures that facilitated discrimination and prediction of the toxicant type. To evaluate changes in gene expression levels after exposure to environmental toxicants, we utilized 18 chemical substances; nine PAH toxicants, six VOC toxicants, and three POP toxicants. Unsupervised gene expression analysis resulted in a characteristic molecular signature for each toxicant group, and combination analysis of two separate multi-classifications indicated 265 genes as surrogate markers for predicting each group of toxicants with 100 % accuracy. Our results suggest that these expression signatures can be used as predictable and discernible surrogate markers for detection and prediction of environmental toxicant exposure. Furthermore, this approach could easily be extended to screening for other types of environmental toxicants.

Investigating the different mechanisms of genotoxic and non-genotoxic carcinogens by a gene set analysis

Based on the process of carcinogenesis, carcinogens are classified as either genotoxic or non-genotoxic. In contrast to non-genotoxic carcinogens, many genotoxic carcinogens have been reported to cause tumor in carcinogenic bioassays in animals. Thus evaluating the genotoxicity potential of chemicals is important to discriminate genotoxic from non-genotoxic carcinogens for health care and pharmaceutical industry safety. Additionally, investigating the difference between the mechanisms of genotoxic and non-genotoxic carcinogens could provide the foundation for a mechanism-based classification for unknown compounds. In this study, we investigated the gene expression of HepG2 cells treated with genotoxic or non-genotoxic carcinogens and compared their mechanisms of action. To enhance our understanding of the differences in the mechanisms of genotoxic and non-genotoxic carcinogens, we implemented a gene set analysis using 12 compounds for the training set (12, 24, 48 h) and validated significant gene sets using 22 compounds for the test set (24, 48 h). For a direct biological translation, we conducted a gene set analysis using Globaltest and selected significant gene sets. To validate the results, training and test compounds were predicted by the significant gene sets using a prediction analysis for microarrays (PAM). Finally, we obtained 6 gene sets, including sets enriched for genes involved in the adherens junction, bladder cancer, p53 signaling pathway, pathways in cancer, peroxisome and RNA degradation. Among the 6 gene sets, the bladder cancer and p53 signaling pathway sets were significant at 12, 24 and 48 h. We also found that the DDB2, RRM2B and GADD45A, genes related to the repair and damage prevention of DNA, were consistently up-regulated for genotoxic carcinogens. Our results suggest that a gene set analysis could provide a robust tool in the investigation of the different mechanisms of genotoxic and non-genotoxic carcinogens and construct a more detailed understanding of the perturbation of significant pathways.

Characterization of formaldehyde's genotoxic mode of action by gene expression analysis in TK6 cells

Gene expression analysis has been established as a tool for the characterization of genotoxic mechanisms of chemical mutagens. It has been suggested that expression analysis is capable of distinguishing compounds that cause DNA damage from those that interfere with mitotic spindle function. Formaldehyde (FA) is known to be a DNA-reactive substance which mainly induces chromosomal damage in cultured mammalian cells. However, there has been concern that FA might also induce leukemia-specific aneuploidies, although recent cytogenetic studies excluded a relevant aneugenic potential of FA. We now investigated whether gene expression profiling can be used as a molecular tool to further characterize FA's genotoxic mode of action and to differentiate between clastogenic and aneugenic activity. TK6 cells were exposed to FA for 4 and 24 h, and changes in gene expression were analyzed using a whole-genome human microarray. Results were compared to the expression profiles of two DNA-damaging clastogens (methyl methanesulfonate and ethyl methanesulfonate) and two aneugens (colcemid and vincristine). The genotoxic activity of FA, MMS and EMS under these conditions was confirmed by comet assay experiments. The gene expression profiles indicated that clastogens and aneugens induce discriminable gene expression patterns. Exposure of TK6 cells to FA led to a discrete gene expression pattern, and all toxicogenomics analyses revealed a closer relationship of FA with clastogens than with aneugens.

Screening for drug-induced hepatotoxicity in primary mouse hepatocytes using acetaminophen, amiodarone, and cyclosporin a as model compounds: an omics-guided approach

Drug-induced hepatotoxicity is a leading cause of attrition for candidate pharmaceuticals in development. New preclinical screening methods are crucial to predict drug toxicity prior to human studies. Of all in vitro hepatotoxicity models, primary human hepatocytes are considered as 'the gold standard.' However, their use is hindered by limited availability and inter-individual variation. These barriers may be overcome by using primary mouse hepatocytes. We used differential in gel electrophoresis (DIGE) to study large-scale protein expression of primary mouse hepatocytes. These hepatocytes were exposed to three well-defined hepatotoxicants: acetaminophen, amiodarone, and cyclosporin A. Each hepatotoxicant induces a different hepatotoxic phenotype. Based on the DIGE results, the mRNA expression levels of deregulated proteins from cyclosporin A-treated cells were also analyzed. We were able to distinguish cyclosporin A from controls, as well as acetaminophen and amiodarone-treated samples. Cyclosporin A induced endoplasmic reticulum (ER) stress and altered the ER-Golgi transport. Moreover, liver carboxylesterase and bile salt sulfotransferase were differentially expressed. These proteins were associated with a protective adaptive response against cyclosporin A-induced cholestasis. The results of this study are comparable with effects in HepG2 cells. Therefore, we suggest both models can be used to analyze the cholestatic properties of cyclosporin A. Furthermore, this study showed a conserved response between primary mouse hepatocytes and HepG2 cells. These findings collectively lend support for use of omics strategies in preclinical toxicology, and might inform future efforts to better link preclinical and clinical research in rational drug development.

RNA-Seq provides new insights in the transcriptome responses induced by the carcinogen benzo[a]pyrene

Whole-genome transcriptome measurements are pivotal for characterizing molecular mechanisms of chemicals and predicting toxic classes, such as genotoxicity and carcinogenicity, from in vitro and in vivo assays. In recent years, deep sequencing technologies have been developed that hold the promise of measuring the transcriptome in a more complete and unbiased manner than DNA microarrays. Here, we applied this RNA-seq technology for the characterization of the transcriptomic responses in HepG2 cells upon exposure to benzo[a]pyrene (BaP), a well-known DNA damaging human carcinogen. Based on EnsEMBL genes, we demonstrate that RNA-seq detects ca 20% more genes than microarray-based technology but almost threefold more significantly differentially expressed genes. Functional enrichment analyses show that RNA-seq yields more insight into the biology and mechanisms related to the toxic effects caused by BaP, i.e., two- to fivefold more affected pathways and biological processes. Additionally, we demonstrate that RNA-seq allows detecting alternative isoform expression in many genes, including regulators of cell death and DNA repair such as TP53, BCL2 and XPA, which are relevant for genotoxic responses. Moreover, potentially novel isoforms were found, such as fragments of known transcripts, transcripts with additional exons, intron retention or exon-skipping events. The biological function(s) of these isoforms remain for the time being unknown. Finally, we demonstrate that RNA-seq enables the investigation of allele-specific gene expression, although no changes could be observed. Our results provide evidence that RNA-seq is a powerful tool for toxicology, which, compared with microarrays, is capable of generating novel and valuable information at the transcriptome level for characterizing deleterious effects caused by chemicals.

Effect of chemical mutagens and carcinogens on gene expression profiles in human TK6 cells

Characterization of toxicogenomic signatures of carcinogen exposure holds significant promise for mechanistic and predictive toxicology. In vitro transcriptomic studies allow the comparison of the response to chemicals with diverse mode of actions under controlled experimental conditions. We conducted an in vitro study in TK6 cells to characterize gene expression signatures of exposure to 15 genotoxic carcinogens frequently used in European industries. We also examined the dose-responsive changes in gene expression, and perturbation of biochemical pathways in response to these carcinogens. TK6 cells were exposed at 3 dose levels for 24 h with and without S9 human metabolic mix. Since S9 had an impact on gene expression (885 genes), we analyzed the gene expression data from cells cultures incubated with S9 and without S9 independently. The ribosome pathway was affected by all chemical-dose combinations. However in general, no similar gene expression was observed among carcinogens. Further, pathways, i.e. cell cycle, DNA repair mechanisms, RNA degradation, that were common within sets of chemical-dose combination were suggested by clustergram. Linear trends in dose–response of gene expression were observed for Trichloroethylene, Benz[a]anthracene, Epichlorohydrin, Benzene, and Hydroquinone. The significantly altered genes were involved in the regulation of (anti-) apoptosis, maintenance of cell survival, tumor necrosis factor-related pathways and immune response, in agreement with several other studies. Similarly in S9+ cultures, Benz[a]pyrene, Styrene and Trichloroethylene each modified over 1000 genes at high concentrations. Our findings expand our understanding of the transcriptomic response to genotoxic carcinogens, revealing the alteration of diverse sets of genes and pathways involved in cellular homeostasis and cell cycle control.

A transcriptomics-based in vitro assay for predicting chemical genotoxicity in vivo

The lack of accurate in vitro assays for predicting in vivo toxicity of chemicals together with new legislations demanding replacement and reduction of animal testing has triggered the development of alternative methods. This study aimed at developing a transcriptomics-based in vitro prediction assay for in vivo genotoxicity. Transcriptomics changes induced in the human liver cell line HepG2 by 34 compounds after treatment for 12, 24, and 48 h were used for the selection of gene-sets that are capable of discriminating between in vivo genotoxins (GTX) and in vivo nongenotoxins (NGTX). By combining transcriptomics with publicly available results for these chemicals from standard in vitro genotoxicity studies, we developed several prediction models. These models were validated by using an additional set of 28 chemicals. The best prediction was achieved after stratification of chemicals according to results from the Ames bacterial gene mutation assay prior to transcriptomics evaluation after 24h of treatment. A total of 33 genes were selected for discriminating GTX from NGTX for Ames-positive chemicals and 22 for Ames-negative chemicals. Overall, this method resulted in 89% accuracy and 91% specificity, thereby clearly outperforming the standard in vitro test battery. Transcription factor network analysis revealed HNF3a, HNF4a, HNF6, androgen receptor, and SP1 as main factors regulating the expression of classifiers for Ames-positive chemicals. Thus, the classical bacterial gene mutation assay in combination with in vitro transcriptomics in HepG2 is proposed as an upgraded in vitro approach for predicting in vivo genotoxicity of chemicals holding a great promise for reducing animal experimentations on genotoxicity.

Discrimination of genotoxic and non-genotoxic hepatocarcinogens by statistical analysis based on gene expression profiling in the mouse liver as determined by quantitative real-time PCR

The general aim of the present study is to discriminate between mouse genotoxic and non-genotoxic hepatocarcinogens via selected gene expression patterns in the liver as analyzed by quantitative real-time PCR (qPCR) and statistical analysis. qPCR was conducted on liver samples from groups of 5 male, 9-week-old B6C3F(1) mice, at 4 and 48h following a single intraperitoneal administration of chemicals. We quantified 35 genes selected from our previous DNA microarray studies using 12 different chemicals: 8 genotoxic hepatocarcinogens (2-acetylaminofluorene, 2,4-diaminotoluene, diisopropanolnitrosamine, 4-dimethylaminoazobenzene, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone, N-nitrosomorpholine, quinoline and urethane) and 4 non-genotoxic hepatocarcinogens (1,4-dichlorobenzene, dichlorodiphenyltrichloroethane, di(2-ethylhexyl)phthalate and furan). A considerable number of genes exhibited significant changes in their gene expression ratios (experimental group/control group) analyzed statistically by the Dunnett's test and Welch's t-test. Finally, we distinguished between the genotoxic and non-genotoxic hepatocarcinogens by statistical analysis using principal component analysis (PCA) of the gene expression profiles for 7 genes (Btg2, Ccnf, Ccng1, Lpr1, Mbd1, Phlda3 and Tubb2c) at 4h and for 12 genes (Aen, Bax, Btg2, Ccnf, Ccng1, Cdkn1a, Gdf15, Lrp1, Mbd1, Phlda3, Plk2 and Tubb2c) at 48h. Seven major biological processes were extracted from the gene ontology analysis: apoptosis, the cell cycle, cell proliferation, DNA damage, DNA repair, oncogenes and tumor suppression. The major, biologically relevant gene pathway suggested was the DNA damage response pathway, resulting from signal transduction by a p53-class mediator leading to the induction of apoptosis. Eight genes (Aen, Bax, Btg2, Ccng1, Cdkn1a, Gdf15, Phlda3 and Plk2) that are directly associated with Trp53 contributed to the PCA. The current findings demonstrate a successful discrimination between genotoxic and non-genotoxic hepatocarcinogens, using qPCR and PCA, on 12 genes associated with a Trp53-mediated signaling pathway for DNA damage response at 4 and 48 h after a single administration of chemicals.

Comparison of hepatocarcinogen-induced gene expression profiles in conventional primary rat hepatocytes with in vivo rat liver

At present, substantial efforts are focused on the development of in vitro assays coupled with "omics" technologies for the identification of carcinogenic substances as an alternative to the classical 2-year rodent carcinogenicity bioassay. A prerequisite for the eventual regulatory acceptance of such assays, however, is the in vivo relevance of the observed in vitro findings. In the current study, hepatocarcinogen-induced gene expression profiles generated after the exposure of conventional cultures of primary rat hepatocytes to three non-genotoxic carcinogens (methapyrilene hydrochloride, piperonyl butoxide, and Wy-14643), three genotoxic carcinogens (aflatoxin B1, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone, and 2-nitrofluorene), and two non-carcinogens (nifedipine and clonidine) are compared with previously obtained in vivo data after oral administration for up to 14 days of the same hepatocarcinogens to rats. In addition to the comparison of deregulated genes and functions per compound between in vivo and in vitro models, the major discriminating cellular pathways found in vivo in livers of exposed rats were examined for deregulation in vitro. Further, in vivo-derived gene signatures for the identification of genotoxic versus non-genotoxic carcinogens are used to classify in vitro-tested hepatocarcinogens and non-carcinogens. In the primary hepatocyte cultures, two out of the three tested genotoxic carcinogens mimicked the in vivo-relevant DNA damage response and were correctly assessed. Exposure to the non-genotoxic hepatocarcinogens, however, triggered a relatively weak response in the in vitro system, with no clear similarities to in vivo. This study contributes to the further optimization of toxicogenomics predictive tools when applied in in vitro settings.

Human embryonic stem cell derived hepatocyte-like cells as a tool for in vitro hazard assessment of chemical carcinogenicity

Hepatocyte-like cells derived from the differentiation of human embryonic stem cells (hES-Hep) have potential to provide a human relevant in vitro test system in which to evaluate the carcinogenic hazard of chemicals. In this study, we have investigated this potential using a panel of 15 chemicals classified as noncarcinogens, genotoxic carcinogens, and nongenotoxic carcinogens and measured whole-genome transcriptome responses with gene expression microarrays. We applied an ANOVA model that identified 592 genes highly discriminative for the panel of chemicals. Supervised classification with these genes achieved a cross-validation accuracy of > 95%. Moreover, the expression of the response genes in hES-Hep was strongly correlated with that in human primary hepatocytes cultured in vitro. In order to infer mechanistic information on the consequences of chemical exposure in hES-Hep, we developed a computational method that measures the responses of biochemical pathways to the panel of treatments and showed that these responses were discriminative for the three toxicity classes and linked to carcinogenesis through p53, mitogen-activated protein kinases, and apoptosis pathway modules. It could further be shown that the discrimination of toxicity classes was improved when analyzing the microarray data at the pathway level. In summary, our results demonstrate, for the first time, the potential of human embryonic stem cell--derived hepatic cells as an in vitro model for hazard assessment of chemical carcinogenesis, although it should be noted that more compounds are needed to test the robustness of the assay.

Deregulation of cancer-related pathways in primary hepatocytes derived from DNA repair-deficient Xpa-/-p53+/- mice upon exposure to benzo[a]pyrene

The current method to predict carcinogenicity of chemicals or drugs is the chronic 2-year rodent bioassay, which has disadvantages in duration, animal use, and specificity. An attractive alternative is the DNA repair-deficient Xpa(-/-)p53(+/-) mouse model that is sensitive to both genotoxic and nongenotoxic carcinogens. A next step in alternative carcinogenicity testing is the development of reliable in vitro systems. We investigated the use of primary hepatocytes, isolated from wild-type (WT) and Xpa(-/-)p53(+/-) mice, in combination with transcriptome analyses for their usefulness to predict carcinogenic features of compounds. As a proof of principle, we studied the response of hepatocytes to the genotoxic carcinogen benzo[a]pyrene (B[a]P). Upon treatment, both WT and Xpa(-/-)p53(+/-) hepatocytes appeared to be metabolically active. However, Xpa(-/-)p53(+/-) hepatocytes were more sensitive than WT hepatocytes to B[a]P treatment in terms of cell survival. In B[a]P-treated WT hepatocytes, DNA repair and cell cycle control genes were transcriptionally activated. Xpa(-/-)p53(+/-) hepatocytes were more responsive to B[a]P exposure, resulting in the downregulation of cancer-related pathways. Deregulation of mitogen-activated protein kinase signaling seems to play an essential role in this and might be the underlying reason for the increased susceptibility of Xpa(-/-)p53(+/-) mice toward carcinogens. Our conclusion is that primary hepatocytes combined with transcriptomics are promising to identify the carcinogenic features of chemicals. Furthermore, these cells seem suitable to gain further insight into the molecular mechanisms of the increased sensitivity of Xpa(-/-)p53(+/-) mice toward both genotoxic and nongenotoxic carcinogens.

New Short Term Prediction Method for Chemical Carcinogenicity by Hepatic Transcript Profiling following 28-Day Toxicity Tests in Rats

We have previously shown the hepatic gene expression profiles of carcinogens in 28-day toxicity tests were clustered into three major groups (Group-1 to 3). Here, we developed a new prediction method for Group-1 carcinogens which consist mainly of genotoxic rat hepatocarcinogens. The prediction formula was generated by a support vector machine using 5 selected genes as the predictive genes and predictive score was introduced to judge carcinogenicity. It correctly predicted the carcinogenicity of all 17 Group-1 chemicals and 22 of 24 non-carcinogens regardless of genotoxicity. In the dose-response study, the prediction score was altered from negative to positive as the dose increased, indicating that the characteristic gene expression profile emerged over a range of carcinogen-specific doses. We conclude that the prediction formula can quantitatively predict the carcinogenicity of Group-1 carcinogens. The same method may be applied to other groups of carcinogens to build a total system for prediction of carcinogenicity.

A niche for infectious disease in environmental health: rethinking the toxicological paradigm

OBJECTIVE

In this review we highlight the need to expand the scope of environmental health research, which now focuses largely on the study of toxicants, to incorporate infectious agents. We provide evidence that environmental health research would be strengthened through finding common ground with the tools and approaches of infectious disease research.

DATA SOURCES AND EXTRACTION

We conducted a literature review for examples of interactions between toxic agents and infectious diseases, as well as the role of these interactions as risk factors in classic "environmental" diseases. We investigated existing funding sources and research mandates in the United States from the National Science Foundation and the National Institutes of Health, particularly the National Institute of Environmental Health Sciences.

DATA SYNTHESIS

We adapted the toxicological paradigm to guide reintegration of infectious disease into environmental health research and to identify common ground between these two fields as well as opportunities for improving public health through interdisciplinary research.

CONCLUSIONS

Environmental health encompasses complex disease processes, many of which involve interactions among multiple risk factors, including toxicant exposures, pathogens, and susceptibility. Funding and program mandates for environmental health studies should be expanded to include pathogens in order to capture the true scope of these overlapping risks, thus creating more effective research investments with greater relevance to the complexity of real-world exposures and multifactorial health outcomes. We propose a new model that integrates the toxicology and infectious disease paradigms to facilitate improved collaboration and communication by providing a framework for interdisciplinary research. Pathogens should be part of environmental health research planning and funding allocation, as well as applications such as surveillance and policy development.