The physiological and biochemical basis of potency thresholds modeled using human estrogen receptor alpha: implications for identifying endocrine disruptors.

The endocrine system functions by interactions between ligands and receptors. Ligands exhibit potency for binding to and interacting with receptors. Potency is the product of affinity and efficacy. Potency and physiological concentration determine the ability of a ligand to produce physiological effects. The kinetic behavior of ligand-receptor interactions conforms to the laws of mass action. The laws of mass action define the relationship between the affinity of a ligand and the fraction of cognate receptors that it occupies at any physiological concentration. We previously identified the minimum ligand potency required to produce clinically observable estrogenic agonist effects via the human estrogen receptor-alpha (ERα). By examining data on botanical estrogens and dietary supplements, we demonstrated that ERα ligands with potency lower than one one-thousandth that of the primary endogenous hormone 17ß-estradiol (E2) do not produce clinically observable estrogenic effects. This allowed us to propose a Human-Relevant Potency Threshold (HRPT) for ERα ligands of 1 × 10-4 relative to E2. Here, we test the hypothesis that the HRPT for ERα arises from the receptor occupancy by the normal metabolic milieu of endogenous ERα ligands. The metabolic milieu comprises precursors to hormones, metabolites of hormones, and other normal products of metabolism. We have calculated fractional receptor occupancies for ERα ligands with potencies below and above the previously established HRPT when normal circulating levels of some endogenous ERα ligands and E2 were also present. Fractional receptor occupancy calculations showed that individual ERα ligands with potencies more than tenfold higher than the HRPT can compete for occupancy at ERα against individual components of the endogenous metabolic milieu and against mixtures of those components at concentrations found naturally in human blood. Ligands with potencies less than tenfold higher than the HRPT were unable to compete successfully for ERα. These results show that the HRPT for ERα agonism (10-4 relative to E2) proposed previously is quite conservative and should be considered strong evidence against the potential for disruption of the estrogenic pathway. For chemicals with potency 10-3 of E2, the potential for estrogenic endocrine disruption must be considered equivocal and subject to the presence of corroborative evidence. Most importantly, this work demonstrates that the endogenous metabolic milieu is responsible for the observed ERα agonist HRPT, that this HRPT applies also to ERα antagonists, and it provides a compelling mechanistic explanation for the HRPT that is grounded in basic principles of molecular kinetics using well characterized properties and concentrations of endogenous components of normal metabolism.

Kinetically-derived maximal dose (KMD) indicates lack of human carcinogenicity of ethylbenzene.

The kinetically-derived maximal dose (KMD) is defined as the maximal external dose at which kinetics are unchanged relative to lower doses, e.g., doses at which kinetic processes are not saturated. Toxicity produced at doses above the KMD can be qualitatively different from toxicity produced at lower doses. Here, we test the hypothesis that neoplastic lesions reported in the National Toxicology Program's (NTP) rodent cancer bioassay with ethylbenzene are a high-dose phenomenon secondary to saturation of elimination kinetics. To test this, we applied Bayesian modeling on kinetic data for ethylbenzene from rats and humans to estimate the Vmax and Km for the Michaelis-Menten equation that governs the elimination kinetics. Analysis of the Michaelis-Menten elimination curve generated from those Vmax and Km values indicated KMD ranges for venous ethylbenzene of 8-17 mg/L in rats and 10-18 mg/L in humans. Those venous concentrations are produced by inhalation concentrations of around 200 ppm ethylbenzene, which is well above typical human exposures. These KMD estimates support the hypothesis that neoplastic lesions seen in the NTP rodent bioassay occur secondary to saturation of ethylbenzene elimination pathways and are not relevant for human risk assessment. Thus, ethylbenzene does not pose a credible cancer risk to humans under foreseeable exposure conditions. Cancer risk assessments focused on protecting human health should avoid endpoint data from rodents exposed to ethylbenzene above the KMD range and future toxicological testing should focus on doses below the KMD range.

The database makes the poison: How the selection of datasets in QSAR models impacts toxicant prediction of higher tier endpoints.

As the United States and the European Union continue their steady march towards the acceptance of new approach methodologies (NAMs), we need to ensure that the available tools are fit for purpose. Critics will be well-positioned to caution against NAMs acceptance and adoption if the tools turn out to be inadequate. In this paper, we focus on Quantitative Structure Activity-Relationship (QSAR) models and highlight how the training database affects quality and performance of these models. Our analysis goes to the point of asking, "are the endpoints extracted from the experimental studies in the database trustworthy, or are they false negatives/positives themselves?" We also discuss the impacts of chemistry on QSAR models, including issues with 2-D structure analyses when dealing with isomers, metabolism, and toxicokinetics. We close our analysis with a discussion of challenges associated with translational toxicology, specifically the lack of adverse outcome pathways/adverse outcome pathway networks (AOPs/AOPNs) for many higher tier endpoints. We recognize that it takes a collaborate effort to build better and higher quality QSAR models especially for higher tier toxicological endpoints. Hence, it is critical to bring toxicologists, statisticians, and machine learning specialists together to discuss and solve these challenges to get relevant predictions.

Understanding and overcoming the technical challenges in using in silico predictions in regulatory decisions of complex toxicological endpoints - A pesticide perspective for regulatory toxicologists with a focus on machine learning models.

There are many challenges that must be overcome before in silico toxicity predictions are ripe for regulatory decision-making. Today, mandates in the United States of America and the European Union to avoid animal usage in toxicity testing is driving the need to consider alternative technologies, including Quantitative Structure Activity Relationship (QSAR) models, and read across approaches. However, when adopting new methods, it is critical that both new approach developers as well as regulatory users understand the strengths and challenges with these new approaches. In this paper, we identify potential sources of bias in machine learning methods specific to toxicity predictions, that may impact the overall performance of in silico models. We also discuss ways to mitigate these biases. Based on our experiences, the most prevalent sources of bias include class imbalance (differing numbers of "toxic" vs "nontoxic" compounds), limited numbers of chemicals within a particular chemistry, and biases within the studies that make up the database used for model building, as well as model evaluation biases. While this is already complex for repeated dose toxicity, in reproduction and developmental toxicity a further level of complexity is introduced by the need to evaluate effects on individual animal and litter basis (e.g., a hierarchal structure). We also discuss key considerations developers and regulators need to make when they use machine learning models to predict chemical safety. Our objective is for our paper to serve as a desk reference for model developers and regulators as they evaluate machine learning models and as they make decisions using these models.

Range of the perfluorooctanoate (PFOA) safe dose for human health: An international collaboration.

Many government agencies and expert groups have estimated a dose-rate of perfluorooctanoate (PFOA) that would protect human health. Most of these evaluations are based on the same studies (whether of humans, laboratory animals, or both), and all note various uncertainties in our existing knowledge. Nonetheless, the values of these various, estimated, safe-doses vary widely, with some being more than 100,000 fold different. This sort of discrepancy invites scrutiny and explanation. Otherwise what is the lay public to make of this disparity? The Steering Committee of the Alliance for Risk Assessment (2022) called for scientists interested in attempting to understand and narrow these disparities. An advisory committee of nine scientists from four countries was selected from nominations received, and a subsequent invitation to scientists internationally led to the formation of three technical teams (for a total of 24 scientists from 8 countries). The teams reviewed relevant information and independently developed ranges for estimated PFOA safe doses. All three teams determined that the available epidemiologic information could not form a reliable basis for a PFOA safe dose-assessment in the absence of mechanistic data that are relevant for humans at serum concentrations seen in the general population. Based instead on dose-response data from five studies of PFOA-exposed laboratory animals, we estimated that PFOA dose-rates 10-70 ng/kg-day are protective of human health.

A novel approach to calculating the kinetically derived maximum dose.

The kinetically derived maximal dose (KMD) provides a toxicologically relevant upper range for the determination of chemical safety. Here, we describe a new way of calculating the KMD that is based on sound Bayesian, theoretical, biochemical, and toxicokinetic principles, that avoids the problems of relying upon the area under the curve (AUC) approach that has often been used. Our new, mathematically rigorous approach is based on converting toxicokinetic data to the overall, or system-wide, Michaelis-Menten curve (which is the slope function for the toxicokinetic data) using Bayesian methods and using the "kneedle" algorithm to find the "knee" or "elbow"-the point at which there is diminishing returns in the velocity of the Michaelis-Menten curve (or acceleration of the toxicokinetic curve). Our work fundamentally reshapes the KMD methodology, placing it within the well-established Michaelis-Menten theoretical framework by defining the KMD as the point where the kinetic rate approximates the Michaelis-Menten asymptote at higher concentrations. By putting the KMD within the Michaelis-Menten framework, we leverage existing biochemical and pharmacological concepts such as "saturation" to establish the region where the KMD is likely to exist. The advantage of defining KMD as a region, rather than as an inflection point along the curve, is that a region reflects uncertainty and clarifies that there is no single point where the curve is expected to "break;" rather, there is a region where the curve begins to taper off as it approaches the asymptote (Vmax in the Michaelis-Menten equation).

Predicting the Probability that a Chemical Causes Steatosis Using Adverse Outcome Pathway Bayesian Networks (AOPBNs).

Adverse outcome pathway Bayesian networks (AOPBNs) are a promising avenue for developing predictive toxicology and risk assessment tools based on adverse outcome pathways (AOPs). Here, we describe a process for developing AOPBNs. AOPBNs use causal networks and Bayesian statistics to integrate evidence across key events. In this article, we use our AOPBN to predict the occurrence of steatosis under different chemical exposures. Since it is an expert-driven model, we use external data (i.e., data not used for modeling) from the literature to validate predictions of the AOPBN model. The AOPBN accurately predicts steatosis for the chemicals from our external data. In addition, we demonstrate how end users can utilize the model to simulate the confidence (based on posterior probability) associated with predicting steatosis. We demonstrate how the network topology impacts predictions across the AOPBN, and how the AOPBN helps us identify the most informative key events that should be monitored for predicting steatosis. We close with a discussion of how the model can be used to predict potential effects of mixtures and how to model susceptible populations (e.g., where a mutation or stressor may change the conditional probability tables in the AOPBN). Using this approach for developing expert AOPBNs will facilitate the prediction of chemical toxicity, facilitate the identification of assay batteries, and greatly improve chemical hazard screening strategies.

Different as night and day: Behavioural and life history responses to varied photoperiods in Daphnia magna.

Nearly all animal species have utilized photoperiod to cue seasonal behaviours and life history traits. We investigated photoperiod responses in keystone species, Daphnia magna, to identify molecular processes underlying ecologically important behaviours and traits using functional transcriptomic analyses. Daphnia magna were photoperiod-entrained immediately posthatch to a standard control photoperiod of 16 light/ 8 dark hours (16L:8D) relative to shorter (4L:20D, 8L:16D, 12L:12L) and longer (20L:4D) day length photoperiods. Short-day photoperiods induced significantly increased light-avoidance behaviours relative to controls. Correspondingly, significant differential transcript expression for genes involved in glutamate signalling was observed, a critical signalling pathway in arthropod light-avoidance behaviour. Additionally, period circadian protein and proteins coding F-box/LRR-repeat domains were differentially expressed which are recognized to establish circadian rhythms in arthropods. Indicators of metabolic rate increased in short-day photoperiods which corresponded with broadscale changes in transcriptional expression across system-level energy metabolism pathways. The most striking observations included significantly decreased neonate production at the shortest day length photoperiod (4L:20D) and significantly increased male production across short-day and equinox photoperiods (4L:20D, 8L:16D and 12L:12D). Transcriptional expression consistent with putative mechanisms of male production was observed including photoperiod-dependent expression of transformer-2 sex-determining protein and small nuclear ribonucleoprotein particles (snRNPs) which control splice variant expression for genes like transformer. Finally, increased transcriptional expression of glutamate has also been shown to induce male production in Daphnia pulex via photoperiod-sensitive mechanisms. Overall, photoperiod entrainment affected molecular pathways that underpin critical behavioural and life history traits in D. magna providing fundamental insights into biological responses to this primary environmental cue.

Using In Vitro High-Throughput Screening Data for Predicting Benzo[k]Fluoranthene Human Health Hazards.

Today there are more than 80,000 chemicals in commerce and the environment. The potential human health risks are unknown for the vast majority of these chemicals as they lack human health risk assessments, toxicity reference values, and risk screening values. We aim to use computational toxicology and quantitative high-throughput screening (qHTS) technologies to fill these data gaps, and begin to prioritize these chemicals for additional assessment. In this pilot, we demonstrate how we were able to identify that benzo[k]fluoranthene may induce DNA damage and steatosis using qHTS data and two separate adverse outcome pathways (AOPs). We also demonstrate how bootstrap natural spline-based meta-regression can be used to integrate data across multiple assay replicates to generate a concentration-response curve. We used this analysis to calculate an in vitro point of departure of 0.751 µM and risk-specific in vitro concentrations of 0.29 µM and 0.28 µM for 1:1,000 and 1:10,000 risk, respectively, for DNA damage. Based on the available evidence, and considering that only a single HSD17B4 assay is available, we have low overall confidence in the steatosis hazard identification. This case study suggests that coupling qHTS assays with AOPs and ontologies will facilitate hazard identification. Combining this with quantitative evidence integration methods, such as bootstrap meta-regression, may allow risk assessors to identify points of departure and risk-specific internal/in vitro concentrations. These results are sufficient to prioritize the chemicals; however, in the longer term we will need to estimate external doses for risk screening purposes, such as through margin of exposure methods.

Overview of Chronic Oral Toxicity Values for Chemicals Present in Hydraulic Fracturing Fluids, Flowback, and Produced Waters.

Concerns have been raised about potential public health effects that may arise if hydraulic fracturing-related chemicals were to impact drinking water resources. This study presents an overview of the chronic oral toxicity values-specifically, chronic oral reference values (RfVs) for noncancer effects, and oral slope factors (OSFs) for cancer-that are available for a list of 1173 chemicals that the United States (U.S.) Environmental Protection Agency (EPA) identified as being associated with hydraulic fracturing, including 1076 chemicals used in hydraulic fracturing fluids and 134 chemicals detected in flowback or produced waters from hydraulically fractured wells. The EPA compiled RfVs and OSFs using six governmental and intergovernmental data sources. Ninety (8%) of the 1076 chemicals reported in hydraulic fracturing fluids and 83 (62%) of the 134 chemicals reported in flowback/produced water had a chronic oral RfV or OSF available from one or more of the six sources. Furthermore, of the 36 chemicals reported in hydraulic fracturing fluids in at least 10% of wells nationwide (identified from EPA's analysis of the FracFocus Chemical Disclosure Registry 1.0), 8 chemicals (22%) had an available chronic oral RfV. The lack of chronic oral RfVs and OSFs for the majority of these chemicals highlights the significant knowledge gap that exists to assess the potential human health hazards associated with hydraulic fracturing.

Estimating the Potential Toxicity of Chemicals Associated with Hydraulic Fracturing Operations Using Quantitative Structure-Activity Relationship Modeling.

The United States Environmental Protection Agency (EPA) identified 1173 chemicals associated with hydraulic fracturing fluids, flowback, or produced water, of which 1026 (87%) lack chronic oral toxicity values for human health assessments. To facilitate the ranking and prioritization of chemicals that lack toxicity values, it may be useful to employ toxicity estimates from quantitative structure-activity relationship (QSAR) models. Here we describe an approach for applying the results of a QSAR model from the TOPKAT program suite, which provides estimates of the rat chronic oral lowest-observed-adverse-effect level (LOAEL). Of the 1173 chemicals, TOPKAT was able to generate LOAEL estimates for 515 (44%). To address the uncertainty associated with these estimates, we assigned qualitative confidence scores (high, medium, or low) to each TOPKAT LOAEL estimate, and found 481 to be high-confidence. For 48 chemicals that had both a high-confidence TOPKAT LOAEL estimate and a chronic oral reference dose from EPA's Integrated Risk Information System (IRIS) database, Spearman rank correlation identified 68% agreement between the two values (permutation p-value =1 × 10(-11)). These results provide support for the use of TOPKAT LOAEL estimates in identifying and prioritizing potentially hazardous chemicals. High-confidence TOPKAT LOAEL estimates were available for 389 of 1026 hydraulic fracturing-related chemicals that lack chronic oral RfVs and OSFs from EPA-identified sources, including a subset of chemicals that are frequently used in hydraulic fracturing fluids.

A Market-Basket Approach to Predict the Acute Aquatic Toxicity of Munitions and Energetic Materials.

An ongoing challenge in chemical production, including the production of insensitive munitions and energetics, is the ability to make predictions about potential environmental hazards early in the process. To address this challenge, a quantitative structure activity relationship model was developed to predict acute fathead minnow toxicity of insensitive munitions and energetic materials. Computational predictive toxicology models like this one may be used to identify and prioritize environmentally safer materials early in their development. The developed model is based on the Apriori market-basket/frequent itemset mining approach to identify probabilistic prediction rules using chemical atom-pairs and the lethality data for 57 compounds from a fathead minnow acute toxicity assay. Lethality data were discretized into four categories based on the Globally Harmonized System of Classification and Labelling of Chemicals. Apriori identified toxicophores for categories two and three. The model classified 32 of the 57 compounds correctly, with a fivefold cross-validation classification rate of 74 %. A structure-based surrogate approach classified the remaining 25 chemicals correctly at 48 %. This result is unsurprising as these 25 chemicals were fairly unique within the larger set.

Plastids are major regulators of light signaling in Arabidopsis.

We previously provided evidence that plastid signaling regulates the downstream components of a light signaling network and that this signal integration coordinates chloroplast biogenesis with both the light environment and development by regulating gene expression. We tested these ideas by analyzing light- and plastid-regulated transcriptomes in Arabidopsis (Arabidopsis thaliana). We found that the enrichment of Gene Ontology terms in these transcriptomes is consistent with the integration of light and plastid signaling (1) down-regulating photosynthesis and inducing both repair and stress tolerance in dysfunctional chloroplasts and (2) helping coordinate processes such as growth, the circadian rhythm, and stress responses with the degree of chloroplast function. We then tested whether factors that contribute to this signal integration are also regulated by light and plastid signals by characterizing T-DNA insertion alleles of genes that are regulated by light and plastid signaling and that encode proteins that are annotated as contributing to signaling, transcription, or no known function. We found that a high proportion of these mutant alleles induce chloroplast biogenesis during deetiolation. We quantified the expression of four photosynthesis-related genes in seven of these enhanced deetiolation (end) mutants and found that photosynthesis-related gene expression is attenuated. This attenuation is particularly striking for Photosystem II subunit S expression. We conclude that the integration of light and plastid signaling regulates a number of END genes that help optimize chloroplast function and that at least some END genes affect photosynthesis-related gene expression.

MIPHENO: data normalization for high throughput metabolite analysis.

BACKGROUND: High throughput methodologies such as microarrays, mass spectrometry and plate-based small molecule screens are increasingly used to facilitate discoveries from gene function to drug candidate identification. These large-scale experiments are typically carried out over the course of months and years, often without the controls needed to compare directly across the dataset. Few methods are available to facilitate comparisons of high throughput metabolic data generated in batches where explicit in-group controls for normalization are lacking. RESULTS: Here we describe MIPHENO (Mutant Identification by Probabilistic High throughput-Enabled Normalization), an approach for post-hoc normalization of quantitative first-pass screening data in the absence of explicit in-group controls. This approach includes a quality control step and facilitates cross-experiment comparisons that decrease the false non-discovery rates, while maintaining the high accuracy needed to limit false positives in first-pass screening. Results from simulation show an improvement in both accuracy and false non-discovery rate over a range of population parameters (p < 2.2 × 10(-16)) and a modest but significant (p < 2.2 × 10(-16)) improvement in area under the receiver operator characteristic curve of 0.955 for MIPHENO vs 0.923 for a group-based statistic (z-score). Analysis of the high throughput phenotypic data from the Arabidopsis Chloroplast 2010 Project (http://www.plastid.msu.edu/) showed ~ 4-fold increase in the ability to detect previously described or expected phenotypes over the group based statistic. CONCLUSIONS: Results demonstrate MIPHENO offers substantial benefit in improving the ability to detect putative mutant phenotypes from post-hoc analysis of large data sets. Additionally, it facilitates data interpretation and permits cross-dataset comparison where group-based controls are missing. MIPHENO is applicable to a wide range of high throughput screenings and the code is freely available as Additional file 1 as well as through an R package in CRAN.

A graphical systems model and tissue-specific functional gene sets to aid transcriptomic analysis of chemical impacts on the female teleost reproductive axis.

Oligonucleotide microarrays and other 'omics' approaches are powerful tools for unsupervised analysis of chemical impacts on biological systems. However, the lack of well annotated biological pathways for many aquatic organisms, including fish, and the limited power of microarray-based analyses to detect low level differential expression of individual genes can hinder the ability to infer and understand chemical effects based on transcriptomic data. Here we report on the supervised assembly of a series of tissue-specific functional gene sets intended to aid transcriptomic analysis of chemical impacts on the female teleost reproductive axis. Gene sets were defined based on an updated graphical systems model of the teleost brain-pituitary-gonadal-hepatic axis. Features depicted in the model were organized into gene sets and mapped to specific probes on three zebrafish (Danio rerio) and two fathead minnow (Pimephales promelas) microarray platforms. Coverage of target genes on the microarrays ranged from 48% for the fathead minnow arrays to 88% for the most current zebrafish platform. Additionally, extended fathead minnow gene sets, incorporating first degree neighbors identified from a Spearman correlation network derived from a large compendium of fathead minnow microarray data, were constructed. Overall, only 14% of the 78 genes queried were connected in the network. Among those, over half had less than five neighbors, while two genes, cyclin b1 and zona pellucida glycoprotein 3, had over 100 first degree neighbors, and were neighbors to one another. Gene set enrichment analyses were conducted using microarray data from a zebrafish hypoxia experiment and fathead minnow time-course experiments conducted with three different endocrine-active chemicals. Results of these analyses demonstrate the utility of the approach for supporting biological inference from ecotoxicogenomic data and comparisons across multiple toxicogenomic experiments. The graphical model, gene mapping, and gene sets described are now available to the scientific community as tools to support ecotoxicogenomic research.

Differences in TCDD-elicited gene expression profiles in human HepG2, mouse Hepa1c1c7 and rat H4IIE hepatoma cells.

BACKGROUND: 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) is an environmental contaminant that elicits a broad spectrum of toxic effects in a species-specific manner. Current risk assessment practices routinely extrapolate results from in vivo and in vitro rodent models to assess human risk. In order to further investigate the species-specific responses elicited by TCDD, temporal gene expression responses in human HepG2, mouse Hepa1c1c7 and rat H4IIE cells were compared. RESULTS: Microarray analysis identified a core set of conserved gene expression responses across species consistent with the role of AhR in mediating adaptive metabolic responses. However, significant species-specific as well as species-divergent responses were identified. Computational analysis of the regulatory regions of species-specific and -divergent responses suggests that dioxin response elements (DREs) are involved. These results are consistent with in vivo rat vs. mouse species-specific differential gene expression, and more comprehensive comparative DRE searches. CONCLUSIONS: Comparative analysis of human HepG2, mouse Hepa1c1c7 and rat H4IIE TCDD-elicited gene expression responses is consistent with in vivo rat-mouse comparative gene expression studies, and more comprehensive comparative DRE searches, suggesting that AhR-mediated gene expression is species-specific.

Non-additive hepatic gene expression elicited by 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and 2,2',4,4',5,5'-hexachlorobiphenyl (PCB153) co-treatment in C57BL/6 mice.

Interactions between environmental contaminants can lead to non-additive effects that may affect the toxicity and risk assessment of a mixture. Comprehensive time course and dose-response studies with 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), non-dioxin-like 2,2',4,4',5,5'-hexachlorobiphenyl (PCB153) and their mixture were performed in immature, ovariectomized C57BL/6 mice. Mice were gavaged once with 30 µg/kg TCDD, 300 mg/kg PCB153, a mixture of 30 µg/kg TCDD with 300 mg/kg PCB153 (MIX) or sesame oil vehicle for 4,12, 24,72 or 168 h. In the 24h dose-response study, animals were gavaged with TCDD (0.3,1, 3, 6, 10, 15, 30, 45 µg/kg), PCB153 (3,10, 30, 60, 100, 150, 300, 450 mg/kg), MIX (0.3+3, 1+10, 3+30, 6+60, 10+100, 15+150, 30+300, 45 µg/kg TCDD+450 mg/kg PCB153, respectively) or vehicle. All three treatments significantly increased relative liver weights (RLW), with MIX eliciting significantly greater increases compared to TCDD and PCB153 alone. Histologically, MIX induced hepatocellular hypertrophy, vacuolization, inflammation, hyperplasia and necrosis, a combination of TCDD and PCB153 responses. Complementary lipid analyses identified significant increases in hepatic triglycerides in MIX and TCDD samples, while PCB153 had no effect on lipids. Hepatic PCB153 levels were also significantly increased with TCDD co-treatment. Microarray analysis identified 167 TCDD, 185 PCB153 and 388 MIX unique differentially expressed genes. Statistical modeling of quantitative real-time PCR analysis of Pla2g12a, Serpinb6a, Nqo1, Srxn1, and Dysf verified non-additive expression following MIX treatment compared to TCDD and PCB153 alone. In summary, TCDD and PCB153 co-treatment elicited specific non-additive gene expression effects that are consistent with RLW increases, histopathology, and hepatic lipid accumulation.

Genome-wide computational analysis of dioxin response element location and distribution in the human, mouse, and rat genomes.

The aryl hydrocarbon receptor (AhR) mediates responses elicited by 2,3,7,8-tetrachlorodibenzo-p-dioxin by binding to dioxin response elements (DRE) containing the core consensus sequence 5'-GCGTG-3'. The human, mouse, and rat genomes were computationally searched for all DRE cores. Each core was then extended by 7 bp upstream and downstream, and matrix similarity (MS) scores for the resulting 19 bp DRE sequences were calculated using a revised position weight matrix constructed from bona fide functional DREs. In total, 72318 human, 70720 mouse, and 88651 rat high-scoring (MS ≥ 0.8437) putative DREs were identified. Gene-encoding intragenic DNA regions had ∼1.6 times more putative DREs than the noncoding intergenic DNA regions. Furthermore, the promoter region spanning ±1.5 kb of a TSS had the highest density of putative DREs within the genome. Chromosomal analysis found that the putative DRE densities of chromosomes X and Y were significantly lower than the mean chromosomal density. Interestingly, the 10 kb upstream promoter region on chromosome X of the genomes were significantly less dense than the chromosomal mean, while the same region in chromosome Y was the most dense. In addition to providing a detailed genomic map of all DRE cores in the human, mouse, and rat genomes, these data will further aid the elucidation of AhR-mediated signal transduction.

Hypoxia-inducible factor-1α regulates the expression of genes in hypoxic hepatic stellate cells important for collagen deposition and angiogenesis.

BACKGROUND/AIMS: Several studies have shown that regions of hypoxia develop in the liver during chronic injury. Furthermore, it has been demonstrated that hypoxia stimulates the release of mediators from hepatic stellate cells (HSCs) that may affect the progression of fibrosis. The mechanism by which hypoxia modulates gene expression in HSCs is not known. Recent studies demonstrated that the hypoxia-activated transcription factor, hypoxia-inducible factor (HIF)-1α, is critical for the development of fibrosis. Accordingly, the hypothesis was tested that HIF-1α is activated in HSCs and regulates the expression of genes important for HSC activation and liver fibrosis. METHODS: Hepatic stellate cells were isolated from mice and exposed to hypoxia. HIF-1α and HIF-2α activation were measured, and gene expression was analysed by gene array analysis. To identify the genes regulated by HIF-1α, HSCs were isolated from control and HIF-1α-deficient mice. RESULTS: Exposure of primary mouse HSCs to 0.5% oxygen activated HIF-1α and HIF-2α. mRNA levels of numerous genes were increased in HSCs exposed to 0.5% oxygen, many of which are important for HSC function, angiogenesis and collagen synthesis. Of the mRNAs increased, chemokine receptor (Ccr) 1, Ccr5, macrophage migration inhibitory factor, interleukin-13 receptor α1 and prolyl-4-hydroxylase α2 (P4h α2) were completely HIF-1α dependent. Upregulation of the vascular endothelial growth factor and the placental growth factor was partially HIF-1α dependent and upregulation of angiopoietin-like 4 and P4h α1 was HIF-1α independent. CONCLUSIONS: Results from these studies demonstrate that hypoxia, through activation of HIF-1α, regulates the expression of genes that may alter the sensitivity of HSCs to certain activators and chemotaxins, and regulates the expression of genes important for angiogenesis and collagen synthesis.