Comparison of Gene Expression Responses in the Small Intestine of Mice Following Exposure to 3 Carcinogens Using the S1500+ Gene Set Informs a Potential Common Adverse Outcome Pathway

The role of hypoxia-inducible factor 1 alpha (HIF-1α) modulation in heavy metal toxicity

Hypoxia-inducible factor 1 (HIF-1) is an oxygen-sensing transcriptional regulator orchestrating a complex of adaptive cellular responses to hypoxia. Several studies have demonstrated that toxic metal exposure may also modulate HIF-1α signal transduction pathway, although the existing data are scarce. Therefore, the present review aims to summarize the existing data on the effects of toxic metals on HIF-1 signaling and the potential underlying mechanisms with a special focus on prooxidant effect of the metals. The particular effect of metals was shown to be dependent on cell type, varying from down- to up-regulation of HIF-1 pathway. Inhibition of HIF-1 signaling may contribute to impaired hypoxic tolerance and adaptation, thus promoting hypoxic damage in the cells. In contrast, its metal-induced activation may result in increased tolerance to hypoxia through increased angiogenesis, thus promoting tumor growth and contributing to carcinogenic effect of heavy metals. Up-regulation of HIF-1 signaling is mainly observed upon Cr, As, and Ni exposure, whereas Cd and Hg may both stimulate and inhibit HIF-1 pathway. The mechanisms underlying the influence of toxic metal exposure on HIF-1 signaling involve modulation of prolyl hydroxylases (PHD2) activity, as well as interference with other tightly related pathways including Nrf2, PI3K/Akt, NF-κB, and MAPK signaling. These effects are at least partially mediated by metal-induced ROS generation. Hypothetically, maintenance of adequate HIF-1 signaling upon toxic metal exposure through direct (PHD2 modulation) or indirect (antioxidant) mechanisms may provide an additional strategy for prevention of adverse effects of metal toxicity.

Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research

Research in environmental health is becoming increasingly reliant upon data science and computational methods that can more efficiently extract information from complex datasets. Data science and computational methods can be leveraged to better identify relationships between exposures to stressors in the environment and human disease outcomes, representing critical information needed to protect and improve global public health. Still, there remains a critical gap surrounding the training of researchers on these in silico methods. We aimed to address this gap by developing the inTelligence And Machine lEarning (TAME) Toolkit, promoting trainee-driven data generation, management, and analysis methods to “TAME” data in environmental health studies. Training modules were developed to provide applications-driven examples of data organization and analysis methods that can be used to address environmental health questions. Target audiences for these modules include students, post-baccalaureate and post-doctorate trainees, and professionals that are interested in expanding their skillset to include recent advances in data analysis methods relevant to environmental health, toxicology, exposure science, epidemiology, and bioinformatics/cheminformatics. Modules were developed by study coauthors using annotated script and were organized into three chapters within a GitHub Bookdown site. The first chapter of modules focuses on introductory data science, which includes the following topics: setting up R/RStudio and coding in the R environment; data organization basics; finding and visualizing data trends; high-dimensional data visualizations; and Findability, Accessibility, Interoperability, and Reusability (FAIR) data management practices. The second chapter of modules incorporates chemical-biological analyses and predictive modeling, spanning the following methods: dose-response modeling; machine learning and predictive modeling; mixtures analyses; -omics analyses; toxicokinetic modeling; and read-across toxicity predictions. The last chapter of modules was organized to provide examples on environmental health database mining and integration, including chemical exposure, health outcome, and environmental justice indicators. Training modules and associated data are publicly available online (https://uncsrp.github.io/Data-Analysis-Training-Modules/). Together, this resource provides unique opportunities to obtain introductory-level training on current data analysis methods applicable to 21st century science and environmental health.

Crypt and Villus Transcriptomic Responses in Mouse Small Intestine Following Oral Exposure to Hexavalent Chromium

Oral exposure to hexavalent chromium (Cr(VI)) induces tumors in the mouse duodenum. Previous microarray-based transcriptomic analyses of homogenized mouse duodenal tissue have demonstrated Cr(VI)-induced alterations in various cellular pathways and processes. However, X-ray fluorescence microscopy indicates that chromium localizes primarily to the duodenal villi following exposure to Cr(VI), suggesting that previous transcriptomic analyses of homogenized tissue provide an incomplete picture of transcriptomic responses in the duodenum. Herein, transcriptomic analyses were conducted separately on crypt and villus tissue from formalin-fixed paraffin-embedded transverse duodenal sections from the same study in which microarray-based analyses were previously conducted. A total of 28 groups (7 doses × 2 timepoints × 2 tissue compartments) were analyzed for differential gene expression, dose-response, and gene set enrichment. Tissue compartment isolation was confirmed by differences in expression of typical markers of crypt and villus compartments. Fewer than 21 genes were altered in the crypt compartment of mice exposed to 0.1-5 ppm Cr(VI) for 7 or 90 days, which increased to hundreds or thousands of genes at ≥20 ppm Cr(VI). Consistent with histological evidence for crypt proliferation, a significant, dose-dependent increase in genes that regulate mitotic cell cycle was prominent in the crypt, while subtle in the villus, when compared with samples from time-matched controls. Minimal transcriptomic evidence of DNA damage response in either the crypts or the villi is consistent with published in vivo genotoxicity data. These results are also discussed in the context of modes of action that have been proposed for Cr(VI)-induced small intestine tumors in mice.

An adverse outcome pathway for small intestinal tumors in mice involving chronic cytotoxicity and regenerative hyperplasia: a case study with hexavalent chromium, captan, and folpet

Small intestinal (SI) tumors are relatively uncommon outcomes in rodent cancer bioassays, and limited information regarding chemical-induced SI tumorigenesis has been reported in the published literature. Herein, we propose a cytotoxicity-mediated adverse outcome pathway (AOP) for SI tumors by leveraging extensive target species- and site-specific molecular, cellular, and histological mode of action (MOA) research for three reference chemicals, the fungicides captan and folpet and the transition metal hexavalent chromium (Cr(VI)). The gut barrier functions through highly efficient homeostatic regulation of SI epithelial cell sloughing, regenerative proliferation, and repair, which involves the replacement of up to 10¹¹ cells per day. This dynamic turnover in the SI provides a unique local environment for a cytotoxicity mediated AOP/MOA. Upon entering the duodenum, cytotoxicity to the villous epithelium is the molecular initiating event, as indicated by crypt elongation, villous atrophy/blunting, and other morphologic changes. Over time, the regenerative capacity of the gut epithelium to compensate declines as epithelial loss accelerates, especially at higher exposures. The first key event (KE), sustained regenerative crypt proliferation/hyperplasia, requires sufficient durations, likely exceeding 6 or 12 months, due to extensive repair capacity, to create more opportunities for the second KE, spontaneous mutation/transformation, ultimately leading to proximal SI tumors. Per OECD guidance, biological plausibility, essentiality, and empirical support were assessed using modified Bradford Hill considerations. The weight-of-evidence also included a lack of induced mutations in the duodenum after up to 90 days of Cr(VI) or captan exposure. The extensive evidence for this AOP, along with the knowledge that human exposures are orders of magnitude below those associated with KEs in this AOP, supports its use for regulatory applications, including hazard identification and risk assessment.

Comparison of Normalization Methods for Analysis of TempO-Seq Targeted RNA Sequencing Data

Analysis of bulk RNA sequencing (RNA-Seq) data is a valuable tool to understand transcription at the genome scale. Targeted sequencing of RNA has emerged as a practical means of assessing the majority of the transcriptomic space with less reliance on large resources for consumables and bioinformatics. TempO-Seq is a templated, multiplexed RNA-Seq platform that interrogates a panel of sentinel genes representative of genome-wide transcription. Nuances of the technology require proper preprocessing of the data. Various methods have been proposed and compared for normalizing bulk RNA-Seq data, but there has been little to no investigation of how the methods perform on TempO-Seq data. We simulated count data into two groups (treated vs. untreated) at seven-fold change (FC) levels (including no change) using control samples from human HepaRG cells run on TempO-Seq and normalized the data using seven normalization methods. Upper Quartile (UQ) performed the best with regard to maintaining FC levels as detected by a limma contrast between treated vs. untreated groups. For all FC levels, specificity of the UQ normalization was greater than 0.84 and sensitivity greater than 0.90 except for the no change and +1.5 levels. Furthermore, K-means clustering of the simulated genes normalized by UQ agreed the most with the FC assignments [adjusted Rand index (ARI) = 0.67]. Despite having an assumption of the majority of genes being unchanged, the DESeq2 scaling factors normalization method performed reasonably well as did simple normalization procedures counts per million (CPM) and total counts (TCs). These results suggest that for two class comparisons of TempO-Seq data, UQ, CPM, TC, or DESeq2 normalization should provide reasonably reliable results at absolute FC levels ≥2.0. These findings will help guide researchers to normalize TempO-Seq gene expression data for more reliable results.