Neurotoxicity of fine and ultrafine particulate matter: A comprehensive review using a toxicity pathway-oriented adverse outcome pathway framework

Reversing Epigenetic Dysregulation in Neurodegenerative Diseases: Mechanistic and Therapeutic Considerations

Epigenetic dysregulation has emerged as an important player in the pathobiology of neurodegenerative diseases (NDDs), such as Alzheimer's, Parkinson's, and Huntington's diseases. Aberrant DNA methylation, histone modifications, and dysregulated non-coding RNAs have been shown to contribute to neuronal dysfunction and degeneration. These alterations are often exacerbated by environmental toxins, which induce oxidative stress, inflammation, and genomic instability. Reversing epigenetic aberrations may offer an avenue for restoring brain mechanisms and mitigating neurodegeneration. Herein, we revisit the evidence suggesting the ameliorative effects of epigenetic modulators in toxin-induced models of NDDs. The restoration of normal gene expressions, the improvement of neuronal function, and the reduction in pathological markers by histone deacetylase (HDAC) and DNA methyltransferase (DNMT) inhibitors have been demonstrated in preclinical models of NDDs. Encouragingly, in clinical trials of Alzheimer's disease (AD), HDAC inhibitors have caused improvements in cognition and memory. Combining these beneficial effects of epigenetic modulators with neuroprotective agents and the clearance of misfolded amyloid proteins may offer synergistic benefits. Reinforced by the emerging methods for more effective and brain-specific delivery, reversibility, and safety considerations, epigenetic modulators are anticipated to minimize systemic toxicity and yield more favorable outcomes in NDDs. In summary, although still in their infancy, epigenetic modulators offer an integrated strategy to address the multifactorial nature of NDDs, altering their therapeutic landscape.

Novel perspective on particulate matter and Alzheimer's disease: Insights from adverse outcome pathway framework

Alzheimer's disease (AD) is a neurodegenerative disease, that accounts for 50-75% of all dementia cases. Evidence demonstrates the link between particulate matter (PM) exposure and AD. However, there are still considerable research gaps. This review aims to clarify the mechanism between PM and AD from different levels (subcellular/cellular/system/population) by using an adverse outcome pathway (AOP) framework. We applied a chemical-phenotype interaction network-based workflow to integrate diverse genes and phenotypes. The interactions among PM, genes, phenotypes, and AD were retrieved from the Comparative Toxicogenomics Database (CTD), DisGeNET, MalaCards, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG), which are publicly available databases. The filtered genes and phenotypes were assembled as molecular initiating events (MIEs) and key events (KEs) according to the upstream and downstream relationships, generating a predictive PM-Gene-Phenotype-AD AOP network. According to the Organization for Economic Co-operation and Development handbook (OECD), a verified AOP network was assessed and applied to determine the effects of PM on AD. PM could increase APP and GSK3B, increase apoptosis, impair cognition and memory, and ultimately lead to AD. Overall, chemical-phenotype interactions are expressed in a formal structured notation using controlled terms for chemicals, phenotypes, taxons, and anatomical descriptors. To our knowledge, this is the first AOP framework focusing on the underlying mechanism of exposure to PM on AD. Our network-based approach not only fills mechanism gaps in PM and AD but sheds light on constructing AOP frameworks for new chemicals.

Comparative Toxicogenomics Database's 20th anniversary: update 2025

For 20 years, the Comparative Toxicogenomics Database (CTD; https://ctdbase.org) has provided high-quality, literature-based curated content describing how environmental chemicals affect human health. Today, CTD includes over 94 million toxicogenomic connections relating chemicals, genes/proteins, phenotypes, anatomical terms, diseases, comparative species, pathways and exposures. In this 20th year anniversary update, we reflect on CTD's remarkable growth and provide an overview of the increased data content and new features, including enhancements to the curation workflow (e.g. new exposure curation tool and expanded use of natural language processing), added functionality (e.g. improvements to CTD Tetramers and Pathway View tools) and significant upgrades to software and infrastructure. Linking lab-based core curation with real-world human exposure curation via the use of controlled vocabularies facilitates analysis of content across the entire environmental health continuum, from molecular toxicological mechanisms to the population level, and vice versa. The 'prototype database' originally described in 2004 has evolved into a premier, sophisticated, highly cited and well-engineered knowledgebase and discoverybase that is utilized by scientists worldwide to design testable hypotheses about environmental health.

Construction of an adverse outcome pathway for the cardiac toxicity of bisphenol a by using bioinformatics analysis

Bisphenol A (BPA), a common endocrine disruptor, has shown cardiovascular toxicity in several epidemiological studies, as well as in vivo and in vitro experimental studies. However, the related adverse outcome pathway (AOP) of BPA toxicity remains unraveled. This study aimed to develop an AOP for the cardiac toxicity of BPA through bioinformatics analysis. The interactions among BPA, genes, phenotypes, and cardiac toxicity were retrieved from several databases, including the Comparative Toxicogenomics Database, Computational Toxicology, DisGeNet, and MalaCards. The target genes and part of target phenotypes were obtained by Venn analysis and literature screening. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed for target genes by using the DAVID online analysis tool to obtain other target phenotypes. AOP hypotheses from BPA exposure to heart disease were established and evaluated comprehensively by a quantitative weight of evidence (QWOE) method. The target genes included ESR2, MAPK1, TGFB1, and ESR1, and the target phenotypes included heart contraction, cardiac muscle contraction, cellular Ca2+ homeostasis, cellular metabolic process, heart development, etc. Overall, the AOP of BPA cardiac toxicity was deduced to be as follows. Initially, BPA bound with ERα/β and then activated the MAPK, AKT, and IL-17 signaling pathways, leading to Ca2+ homeostasis disorder and increased inflammatory response. Subsequently, cardiac function was impaired, causing coronary heart disease, arrhythmia, cardiac dysplasia, and other heart diseases. According to the Bradford-Hill causal considerations, the score of AOP by QWOE was 69, demonstrating a moderate confidence and providing clues on cardiotoxicity-assessment procedure and further studies on BPA.

Urban aerosol particulate matter promotes cellular senescence through mitochondrial ROS-mediated Akt/Nrf2 downregulation in human retinal pigment epithelial cells

Urban aerosol particulate matter (UPM) is widespread in the environment, and its concentration continues to increase. Several recent studies have reported that UPM results in premature cellular senescence, but few studies have investigated the molecular basis of UPM-induced senescence in retinal pigment epithelial (RPE) cells. In this study, we primarily evaluated UPM-induced premature senescence and the protective function of nuclear factor erythroid 2-related factor 2 (Nrf2) in human RPE ARPE-19 cells. The findings indicated that UPM exposure substantially induced premature cellular senescence in ARPE-19 cells, as observed by increased β-galactosidase activity, expression levels of senescence-associated marker proteins, and senescence-associated phenotypes. Such UPM-induced senescence is associated with mitochondrial oxidative stress-mediated phosphatidylinositol 3'-kinase/Akt/Nrf2 downregulation. Sulforaphane-mediated Nrf2 activation Sulforaphane-mediated upregulation of phosphorylated Nrf2 suppressed the decrease in its target antioxidant gene, NAD(P)H quinone oxidoreductase 1, under UPM, which notably prevented ARPE-19 cells from UPM-induced cellular senescence. By contrast, Nrf2 knockdown exacerbated cellular senescence and promoted oxidative stress. Collectively, our results demonstrate the regulatory role of Nrf2 in UPM-induced senescence of RPE cells and suggest that Nrf2 is a potential molecular target.

Prediction and validation of mild cognitive impairment in occupational dust exposure population based on machine learning

OBJECTIVE

Workers exposed to dust for extended periods may experience varying degrees of cognitive impairment. However, limited research exists on the associated risk factors. This study aims to identify key variables using machine learning algorithms (ML) and develop a model to predict the occurrence of mild cognitive impairment in miners.

METHODS

A total of 1938 miners were included in the study. Univariate analysis and multivariable logistic regression were employed to identify independent risk factors for cognitive impairment among miners. The dataset was randomly divided into a training set and a validation set in an 8:2 ratio of 1550 and 388 individuals, respectively. An additional group of 351 miners was collected as a test set for cognitive impairment assessment. Seven machine learning algorithms, including XGBoost, Logistic Regression, Random Forest, Complement Naive Bayes, Multi-layer Perceptron, Support Vector Machine, and K-Nearest Neighbors, were used to establish a predictive model for mild cognitive impairment in the dust-exposed population, based on baseline characteristics of the workers. The predictive performance of the models was evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC), and the XGBoost model was further explained using the Shapley Additive exPlanations (SHAP) package. Cognitive function assessments using rank sum tests were conducted to compare differences in cognitive domains between the mild cognitive impairment group and the normal group.

RESULTS

Univariate and multivariable logistic regression analyses revealed that education level, Age, Work years, SSRS (Self-Rating Scale for Stress), and HAMA (Hamilton Anxiety Rating Scale) were independent risk factors for cognitive impairment among dust-exposed workers. Comparative analysis of the performance of the seven machine learning algorithms demonstrated that XGBoost (training set: AUC=0.959, validation set: AUC=0.795) and Logistic Regression (training set: AUC=0.818, validation set: AUC=0.816) models exhibited superior predictive performance. Results from the test set showed that the AUC of the XGBoost model (AUC=0.913) outperformed the Logistic Regression model (AUC=0.778). Miners with mild cognitive impairment exhibited significant impairments (p<0.05) in visual-spatial abilities, attention, abstract thinking, and memory areas.

CONCLUSION

Machine learning algorithms can predict the risk of cognitive impairment in this population, with the XGBoost algorithm showing the best performance. The developed model can guide the implementation of appropriate preventive measures for dust-exposed workers.