The shape of low-concentration dose-response functions for benzene: implications for human health risk assessment

UBE2L3 promotes benzene-induced hematotoxicity via autophagy-dependent ferroptosis

Benzene is a common environmental pollutant and significant health hazard. Low-dose benzene exposure is common in most industrial settings, and some workers exhibit hematotoxicity characterized by impaired hematopoietic function. Consequently, understanding the early hematopoietic damage and biomarkers associated with low-dose benzene exposure is of critical importance for health risk assessment. Using data from a 5-year prospective cohort study on benzene exposure and the National Center for Biotechnology Information's Gene Expression Omnibus database, we detected significant downregulation of the ubiquitin-conjugating enzyme UBE2L3 (E2) in benzene-exposed subjects compared to control subjects. Liquid chromatography tandem mass spectrometry and co-immunoprecipitation experiments illustrated the binding interaction between UBE2L3 and the ubiquitin-protein ligase ZNF598 (E3). We applied deep learning algorithms to predict candidate interacting proteins and then conducted validation via co-immunoprecipitation experiments, which showed that ZNF598 engages in binding with the autophagy protein LAMP-2. Subsequent overexpression and knockdown of UBE2L3 coupled with immunofluorescence experiments and transmission electron microscopy revealed that UBE2L3 disrupts the ubiquitination-degradation of LAMP-2 by ZNF598, reduces GPX4 expression levels, and activates an autophagy-dependent ferroptosis pathway. It also leads to increased lipid peroxidation, thereby promoting ferroptosis and contributing to the hematotoxicity induced by benzene. In summary, our results suggest that UBE2L3 may be involved in early hematopoietic damage by modulating the autophagy-dependent ferroptosis signaling pathway in benzene-induced hematotoxicity.

Interventional probability of causation (IPoC) with epidemiological and partial mechanistic evidence: benzene vs. formaldehyde and acute myeloid leukemia (AML)

INTRODUCTION

Causal epidemiology for regulatory risk analysis seeks to evaluate how removing or reducing exposures would change disease occurrence rates. We define interventional probability of causation (IPoC) as the change in probability of a disease (or other harm) occurring over a lifetime or other specified time interval that would be caused by a specified change in exposure, as predicted by a fully specified causal model. We define the closely related concept of causal assigned share (CAS) as the predicted fraction of disease risk that would be removed or prevented by a specified reduction in exposure, holding other variables fixed. Traditional approaches used to evaluate the preventable risk implications of epidemiological associations, including population attributable fraction (PAF) and the Bradford Hill considerations, cannot reveal whether removing a risk factor would reduce disease incidence. We argue that modern formal causal models coupled with causal artificial intelligence (CAI) and realistically partial and imperfect knowledge of underlying disease mechanisms, show great promise for determining and quantifying IPoC and CAS for exposures and diseases of practical interest.

METHODS

We briefly review key CAI concepts and terms and then apply them to define IPoC and CAS. We present steps to quantify IPoC using a fully specified causal Bayesian network (BN) model. Useful bounds for quantitative IPoC and CAS calculations are derived for a two-stage clonal expansion (TSCE) model for carcinogenesis and illustrated by applying them to benzene and formaldehyde based on available epidemiological and partial mechanistic evidence.

RESULTS

Causal BN models for benzene and risk of acute myeloid leukemia (AML) incorporating mechanistic, toxicological and epidemiological findings show that prolonged high-intensity exposure to benzene can increase risk of AML (IPoC of up to 7e-5, CAS of up to 54%). By contrast, no causal pathway leading from formaldehyde exposure to increased risk of AML was identified, consistent with much previous mechanistic, toxicological and epidemiological evidence; therefore, the IPoC and CAS for formaldehyde-induced AML are likely to be zero.

CONCLUSION

We conclude that the IPoC approach can differentiate between likely and unlikely causal factors and can provide useful upper bounds for IPoC and CAS for some exposures and diseases of practical importance. For causal factors, IPoC can help to estimate the quantitative impacts on health risks of reducing exposures, even in situations where mechanistic evidence is realistically incomplete and individual-level exposure-response parameters are uncertain. This illustrates the strength that can be gained for causal inference by using causal models to generate testable hypotheses and then obtaining toxicological data to test the hypotheses implied by the models-and, where necessary, refine the models. This virtuous cycle provides additional insight into causal determinations that may not be available from weight-of-evidence considerations alone.

Review on novel toxicological effects and personalized health hazard in workers exposed to low doses of benzene

Several recent reports indicate health hazards for workers with below occupational limit exposure to benzene (BZ). Our updated review indicates that such low exposures induced traditional as well as novel toxicity/genotoxicity, e.g., increased mitochondria copy numbers, prolongation of telomeres, impairment of DNA damage repair response (DDRR), perturbations of expression in non-coding RNAs, and epigenetic changes. These abnormalities were associated with alterations of gene expression and cellular signaling pathways which affected hematopoietic cell development, expression of apoptosis, autophagy, etc. The overarching mechanisms for induction of health risk are impaired DDRR, inhibition of tumor suppressor genes, and changes of MDM2-p53 axis activities that contribute to perturbed control for cancer pathways. Evaluation of the unusual dose-responses to BZ exposure indicates cellular over-compensation and reprogramming to overcome toxicity and to promote survival. However, these abnormal mechanisms also promote the induction of leukemia. Further investigations indicate that the current exposure limits for workers to BZ are unacceptable. Based on these studies, the new exposure limits should be less than 0.07 ppm rather than the current 1 ppm. This review also emphasizes the need to conduct appropriate bioassays, and to provide more reliable decisions on health hazards as well as on exposure limits for workers. In addition, it is important to use scientific data to provide significantly improved risk assessment, i.e., shifting from a population- to an individual-based risk assessment.

Prediction and assessment of xenoestrogens mixture effects using the in vitro ERα-CALUX assay

Introduction: Many natural or synthetic compounds used in foods, dietary supplements, and food contact materials (FCMs) are suspected endocrine disruptors (EDs). Currently, scientific evidence to predict the impacts on biological systems of ED mixtures is lacking. In this study, three classes of substances were considered: i) phytoestrogens, ii) plant protection products (PPP) and iii) substances related to FCMs. Fourteen compounds were selected based on their potential endocrine activity and their presence in food and FCMs. Methods: These compounds were evaluated using an in vitro gene expression assay, the ERα-CALUX, to characterize their responses on the estrogen receptor alpha. Cells were exposed to fixed ratio mixtures and non-equipotent mixtures of full and partial agonists. The concentration-response curves measured for the three classes of compounds were characterized by variable geometric parameters in terms of maximum response (efficacy), sensitivity (slope) and potency (median effective concentration EC50). To account for these variations, a generic response addition (GRA) model was derived from mass action kinetics. Results: Although GRA does not allow us to clearly separate the concentration addition (CA) and independent action (IA) models, it was possible to determine in a statistically robust way whether the combined action of the chemicals in the mixture acted by interaction (synergy and antagonism) or by additive behavior. This distinction is crucial for assessing the risks associated with exposure to xenoestrogens. A benchmark dose approach was used to compare the response of phytoestrogen blends in the presence and absence of the hormone estradiol (E2). At the same time, 12 mixtures of 2-5 constituents including phytoestrogens, phthalates and PPPs in proportions close to those found in food products were tested. In 95% of cases, the response pattern observed showed a joint and independent effect of the chemicals on ER. Discussion: Overall, these results validate a risk assessment approach based on an additive effects model modulated by intrinsic toxicity factors. Here, the CA and IA approaches cannot be distinguished solely based on the shape of the concentration response curves. However, the optimized GRA model is more robust than CA when the efficacy, potency, and sensitivity of individual chemical agonists show large variations.

Using Explainable Machine Learning to Interpret the Effects of Policies on Air Pollution: COVID-19 Lockdown in London

Activity changes during the COVID-19 lockdown present an opportunity to understand the effects that prospective emission control and air quality management policies might have on reducing air pollution. Using a regression discontinuity design for causal analysis, we show that the first UK national lockdown led to unprecedented decreases in road traffic, by up to 65%, yet incommensurate and heterogeneous responses in air pollution in London. At different locations, changes in air pollution attributable to the lockdown ranged from -50% to 0% for nitrogen dioxide (NO2), 0% to +4% for ozone (O3), and -5% to +0% for particulate matter with an aerodynamic diameter less than 10 μm (PM10), and there was no response for PM2.5. Using explainable machine learning to interpret the outputs of a predictive model, we show that the degree to which NO2 pollution was reduced in an area was correlated with spatial features (including road freight traffic and proximity to a major airport and the city center), and that existing inequalities in air pollution exposure were exacerbated: pollution reductions were greater in places with more affluent residents and better access to public transport services.

Accurate low-dose exposure assessment of benzene and monoaromatic compounds by diffusive sampling: sampling and analytical method validation according to ISO 23320 for radiello® samplers packed with activated charcoal

The research study aimed at providing an accurate low-dose benzene exposure assessment method, by validating diffusive monitoring techniques for benzene personal exposure measurements at workplaces where benzene concentrations are expected in the low ppb range, such as in the present-day chemical, petrochemical, foundry, and pharmaceutical industry. The project was aimed at addressing the need for a robust and fully validated method to perform personal exposure measurements considering that the occupational exposure limit value for benzene is going to be significantly lowered in the next few years. Diffusive sampling offers a reliable alternative to pumped sampling methods, intrinsic safety in potentially explosive atmospheres, lightness, and ease of use. In this study, the radiello® diffusive sampler, with the packed activated charcoal RAD130 adsorbing substrate [suitable for solvent desorption and analysis by high-resolution gas chromatography-flame ionization detection (HRGC-FID)], was used. The experiments have been conducted following the ISO 23320 standard in the range from 0.005 to 0.1 ppm (16 to 320 μg/m3), yielding a full validation of the sampling and analytical method. The sampler performances have fulfilled all requisites of the ISO 23320 standard, in particular: bias due to the selection of a non-ideal sorbent is lower than 10% (no significant back diffusion of benzene due to concentration change in the atmosphere); bias due to storage of samples for up to 2 months is lower than 10%; nominal uptake rate for benzene on RAD130 is 74.65 mL/min; and expanded uncertainty of the sampling and analytical method is 20.6%. The sampling and analytical method is therefore fit-for-purpose for the personal exposure measurements aimed at testing compliance with occupational exposure limit values for benzene. The method is also fit for short-duration exposure monitoring related to specific tasks, and other volatile organic compounds, usually found in the same workplaces, such as aliphatic and aromatic hydrocarbons and some oxygenated compounds, have also been studied. In particular, n-hexane and isopropyl benzene, whose classification is currently under revision, can be efficiently monitored by this technique.