Parkinson’s disease (PD) is a neurodegenerative condition marked by loss of motor coordination and cognitive impairment. According to global estimates, the worldwide prevalence of PD will likely exceed 12 million cases by 2040. PD is primarily associated with genetic factors, while clinically, cases are attributed to idiopathic factors such as environmental or occupational exposure. The heavy metals linked to PD and other neurodegenerative disorders include copper, manganese, and zinc. Chronic exposure to metals induces elevated oxidative stress and disrupts homeostasis, resulting in neuronal death. These metals are suggested to induce idiopathic PD in the literature. This study measures the effects of lethal concentration at 10% cell death (LC₁₀) and lethal concentration at 50% cell death (LC₅₀) concentrations of copper, manganese, and zinc chlorides on SH-SY5Y cells via markers for dopamine, reactive oxygen species (ROS) generation, DNA damage, and mitochondrial dysfunction after a 24 h exposure. These measurements were compared to a known neurotoxin to induce PD, 100 µM 6-hydroxydopamine (6-ODHA). Between the three metal chlorides, zinc was statistically different in all parameters from all other treatments and induced significant dopaminergic loss, DNA damage, and mitochondrial dysfunction. The LC₅₀ of manganese and copper had the most similar response to 6-ODHA in all parameters, while LC₁₀ of manganese and copper responded most like untreated cells. This study suggests that these metal chlorides respond differently from 6-ODHA and each other, suggesting that idiopathic PD utilizes a different mechanism from the classic PD model.

Inhaled chemicals can cause dysfunction in the lung surfactant, a protein-lipid complex with critical biophysical and biochemical functions. This inhibition has many structure-related and dose-dependent mechanisms, making hazard identification challenging. We developed quantitative structure-activity relationships for predicting lung surfactant inhibition using machine learning. Logistic regression, support vector machines, random forest, gradient-boosted trees, prior-data-fitted networks, and multilayer perceptron were evaluated as methods. Multilayer perceptron had the strongest performance with 96% accuracy and an F1 score of 0.97. Support vector machines and logistic regression also performed well with lower computation costs. This serves as a proof-of-concept for efficient hazard screening in the emerging area of lung surfactant inhibition.

Lung surfactant lowers surface tension in the alveoli, playing a critical role in lung function and immune defense. Inhaled aerosol particles may induce adverse health effects via the interruption of lung surfactant surface tension. Previous generations of surfactometers have been developed and used to characterize surfactants and their response to various exposures. However, these devices do not allow for simultaneous exposure with multiple aerosols, the incorporation of multiple aerosolization methods, or the co-exposure scenario involving both lung surfactant fluid and pulmonary cells/tissues. Herein, we introduce a new configuration, termed the aerosol exposure surfactometer (AESM), that addresses all of these limitations. The results indicate correlation of surface tension changes in concordance with aerosol properties while preventing unwanted cell death. Further investigation using this method may elucidate mechanisms of pathogenesis related to surfactant dysfunction and provide the foundation for predictive models.

Environmental contaminants are ubiquitous in the air we breathe and can potentially cause adverse immunological outcomes such as respiratory sensitization, a type of immune-driven allergic response in the lungs. Wood dust, latex, pet dander, oils, fragrances, paints, and glues have all been implicated as possible respiratory sensitizers. With the increased incidence of exposure to chemical mixtures and the rapid production of novel materials, it is paramount that testing regimes accounting for sensitization are incorporated into development cycles. However, no validated assay exists that is universally accepted to measure a substance’s respiratory sensitizing potential. The lungs comprise various cell types and regions where sensitization can occur, with the gas-exchange interface being especially important due to implications for overall lung function. As such, an assay that can mimic the alveolar compartment and assess sensitization would be an important advance for inhalation toxicology. Some such models are under development, but in-depth transcriptomic analyses have yet to be reported. Understanding the transcriptome after sensitizer exposure would greatly advance hazard assessment and sustainability. We tested two known sensitizers (i.e., isophorone diisocyanate and ethylenediamine) and two known non-sensitizers (i.e., chlorobenzene and dimethylformamide). RNA sequencing was performed in our in vitro alveolar model, consisting of a 3D co-culture of epithelial, macrophage, and dendritic cells. Sensitizers were readily distinguishable from non-sensitizers by principal component analysis. However, few differentially regulated genes were common across all pair-wise comparisons (i.e., upregulation of genes SOX9, UACA, CCDC88A, FOSL1, KIF20B). While the model utilized in this study can differentiate the sensitizers from the non-sensitizers tested, further studies will be required to robustly identify critical pathways inducing respiratory sensitization. Graphical Abstract Graphical headlines/headlights Pollutants may trigger lung allergies, but no universal method measures respiratory sensitization potential. In vitro systems can detect respiratory sensitizers, aiding in anticipating and reducing the risks of new materials. Sensitizers and non-sensitizers can be distinguished through transcriptome investigation. The sensitizers tested induced cell differentiation and proliferation pathways while inhibiting immune defense and functionality.

Humans are exposed to various environmental chemicals, particles, and pathogens that can cause adverse health outcomes. These exposures are rarely homogenous but rather complex mixtures in which the components may interact, such as through synergism or antagonism. Toxicologists have conducted preliminary investigations into binary mixtures of two components, but little work has been done to understand mixtures of three or more components. We investigated mixtures of divalent metal ions, quantifying the toxic interactions in a human lung model. Eight metals were chosen: heavy metals cadmium, copper, lead, and tin, as well as transition metals iron, manganese, nickel, and zinc. Human alveolar epithelial cells (A549) were exposed to individual metals and sixteen binary and six ternary combinations. The dose-response was modeled using logistic regression in R to extract LC₅₀ values. Among the individual metals, the highest and lowest toxicity were observed with copper at an LC₅₀ of 102 μM and lead at an LC₅₀ of 5639 μM, respectively. First and second-order interaction coefficients were obtained using machine learning-based linear regression in Python. The resulting second-degree polynomial model formed either a hyperbolic or elliptical conic section, and the positive quadrant was used to produce isobolograms and contour plots. The strongest synergism and antagonism were observed in cadmium-copper and iron-zinc, respectively. A three-way interaction term was added to produce full ternary isobologram surfaces, which, to our knowledge, are a significant first in the toxicology literature.

Silicon dioxide (SiO₂), commonly known as silica, is a naturally occurring mineral extracted from the Earth’s crust. It is widely used in commercial products such as food, medicine, and dental ceramics. There are few studies on the health effects of pyrogenic and colloidal silica after ingestion. No research has compared the impact of microscale morphologies on mitochondrial activity in colon cells after acute exposure. The results show that crystalline and amorphous silica had a concentration-independent effect on cells, with an initial increase in mitochondrial activity followed by a decrease. Vitreous silica did not affect cells. Diatomaceous earth and pyrogenic silica had a concentration-dependent response, causing a reduction in mitochondrial activity as concentration increased. Diatomaceous earth triggered the highest cellular response, with mitochondrial activity ranging from 78.84% ± 12.34 at the highest concentration (1000 ppm) to 62.54% ± 17.43 at the lowest concentration (0.01 ppm) and an average H₂O₂ concentration of 1.48 ± 0.15 RLUs. This research advances our understanding of silica’s impact on human gastrointestinal cells, highlighting the need for ongoing exploration. These findings can improve risk mitigation strategies in silica-exposed environments.