In silico prediction of parkinsonian motor deficits-related neurotoxicants based on the adverse outcome pathway concept

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.

Analysis of implicit and explicit uncertainties in QSAR prediction of chemical toxicity: A case study of neurotoxicity

Although uncertainties expressed in texts within QSAR studies can guide quantitative uncertainty estimations, they are often overlooked during uncertainty analysis. Using neurotoxicity as an example, this study developed a method to support analysis of implicitly and explicitly expressed uncertainties in QSAR modeling studies. Text content analysis was employed to identify implicit and explicit uncertainty indicators, whereafter uncertainties within the indicator-containing sentences were identified and systematically categorized according to 20 uncertainty sources. Our results show that implicit uncertainty was more frequent within most uncertainty sources (13/20), while explicit uncertainty was more frequent in only three sources, indicating that uncertainty is predominantly expressed implicitly in the field. The most highly cited sources included Mechanistic plausibility, Model relevance and Model performance, suggesting they constitute sources of most concern. The fact that other sources like Data balance were not mentioned, although it is recognized in the broader QSAR literature as an area of concern, demonstrates that the output from the type of analysis conducted here must be interpreted in the context of the broader QSAR literature before conclusions are drawn. Overall, the method established here can be applied in other QSAR modeling contexts and ultimately guide efforts targeted towards addressing the identified uncertainty sources.

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

Fine particulate matter (PM2.5) can cause brain damage and diseases. Of note, ultrafine particles (UFPs) with an aerodynamic diameter less than or equal to 100 nm are a growing concern. Evidence has suggested toxic effects of PM2.5 and UFPs on the brain and links to neurological diseases. However, the underlying mechanism has not yet been fully illustrated due to the variety of the study models, different endpoints, etc. The adverse outcome pathway (AOP) framework is a pathway-based approach that could systematize mechanistic knowledge to assist health risk assessment of pollutants. Here, we constructed AOPs by collecting molecular mechanisms in PM-induced neurotoxicity assessments. We chose particulate matter (PM) as a stressor in the Comparative Toxicogenomics Database (CTD) and identified the critical toxicity pathways based on Ingenuity Pathway Analysis (IPA). We found 65 studies investigating the potential mechanisms linking PM2.5 and UFPs to neurotoxicity, which contained 2, 675 genes in all. IPA analysis showed that neuroinflammation signaling and glucocorticoid receptor signaling were the common toxicity pathways. The upstream regulator analysis (URA) of PM2.5 and UFPs demonstrated that the neuroinflammation signaling was the most initially triggered upstream event. Therefore, neuroinflammation was recognized as the MIE. Strikingly, there is a clear sequence of activation of downstream signaling pathways with UFPs, but not with PM2.5. Moreover, we found that inflammation response and homeostasis imbalance were key cellular events in PM2.5 and emphasized lipid metabolism and mitochondrial dysfunction, and blood-brain barrier (BBB) impairment in UFPs. Previous AOPs, which only focused on phenotypic changes in neurotoxicity upon PM exposure, we for the first time propose AOP framework in which PM2.5 and UFPs may activate pathway cascade reactions, resulting in adverse outcomes associated with neurotoxicity. Our toxicity pathway-based approach not only advances risk assessment for PM-induced neurotoxicity but shines a spotlight on constructing AOP frameworks for new chemicals.

Potential Applications of Mitochondrial Therapy with a Focus on Parkinson's Disease and Mitochondrial Transplantation

Purpose

Both aging and neurodegenerative illnesses are thought to be influenced by mitochondrial malfunction and free radical formation. Deformities of the energy metabolism, mitochondrial genome polymorphisms, nuclear DNA genetic abnormalities associated with mitochondria, modifications of mitochondrial fusion or fission, variations in shape and size, variations in transit, modified mobility of mitochondria, transcription defects, and the emergence of misfolded proteins associated with mitochondria are all linked to Parkinson's disease.

Methods

This review is a condensed compilation of data from research that has been published between the years of 2014 and 2022, using search engines like Google Scholar, PubMed, and Scopus.

Results

Mitochondrial transplantation is a one-of-a-kind treatment for mitochondrial diseases and deficits in mitochondrial biogenesis. The replacement of malfunctioning mitochondria with transplanted viable mitochondria using innovative methodologies has shown promising outcomes as a cure for Parkinson's, involving tissue sparing coupled with enhanced energy generation and lower oxidative damage. Numerous mitochondria-targeted therapies, including mitochondrial gene therapy, redox therapy, and others, have been investigated for their effectiveness and potency.

Conclusion

The development of innovative therapeutics for mitochondria-directed treatments in Parkinson's disease may be aided by optimizing mitochondrial dynamics. Many neurological diseases have been studied in animal and cellular models, and it has been found that mitochondrial maintenance can slow the death of neuronal cells. It has been hypothesized that drug therapies for neurodegenerative diseases that focus on mitochondrial dysfunction will help to delay the onset of neuronal dysfunction.

CTD tetramers: a new online tool that computationally links curated chemicals, genes, phenotypes, and diseases to inform molecular mechanisms for environmental health

The molecular mechanisms connecting environmental exposures to adverse endpoints are often unknown, reflecting knowledge gaps. At the Comparative Toxicogenomics Database (CTD), we developed a bioinformatics approach that integrates manually curated, literature-based interactions from CTD to generate a "CGPD-tetramer": a 4-unit block of information organized as a step-wise molecular mechanism linking an initiating Chemical, an interacting Gene, a Phenotype, and a Disease outcome. Here, we describe a novel, user-friendly tool called CTD Tetramers that generates these evidence-based CGPD-tetramers for any curated chemical, gene, phenotype, or disease of interest. Tetramers offer potential solutions for the unknown underlying mechanisms and intermediary phenotypes connecting a chemical exposure to a disease. Additionally, multiple tetramers can be assembled to construct detailed modes-of-action for chemical-induced disease pathways. As well, tetramers can help inform environmental influences on adverse outcome pathways (AOPs). We demonstrate the tool's utility with relevant use cases for a variety of environmental chemicals (eg, perfluoroalkyl substances, bisphenol A), phenotypes (eg, apoptosis, spermatogenesis, inflammatory response), and diseases (eg, asthma, obesity, male infertility). Finally, we map AOP adverse outcome terms to corresponding CTD terms, allowing users to query for tetramers that can help augment AOP pathways with additional stressors, genes, and phenotypes, as well as formulate potential AOP disease networks (eg, liver cirrhosis and prostate cancer). This novel tool, as part of the complete suite of tools offered at CTD, provides users with computational datasets and their supporting evidence to potentially fill exposure knowledge gaps and develop testable hypotheses about environmental health.

An in silico insight on the mechanistic aspects of gelsenicine toxicity: A reverse screening study pointing to the possible involvement of acetylcholine binding receptor

Gelsedine-type alkaloids are highly toxic plant secondary metabolites produced by shrubs belonging to the Gelsemium genus. Gelsenicine is one of the most concerning gelsedine-type alkaloids with a lethal dose lower than 1 mg/Kg in mice. Several reported episodes of poisoning in livestock and fatality cases in humans due to the usage of Gelsemium plants extracts were reported. Also, gelsedine-type alkaloids were found in honey constituting a potential food safety issue. However, their toxicological understanding is scarce and the molecular mechanism underpinning their toxicity needs further investigations. In this context, an in silico approach based on reverse screening, docking and molecular dynamics successfully identified a possible gelsenicine biological target shedding light on its toxicodynamics. In line with the available crystallographic data, it emerged gelsenicine could target the acetylcholine binding protein possibly acting as a partial agonist against α7 nicotinic acetylcholine receptor (AChR). Overall, these results agreed with evidence previously reported and prioritized AChR for further dedicated analysis.

The essential role of GSTP1 I105V polymorphism in the prediction of CDNB metabolism and toxicity: In silico and in vitro insights

Humans are continuously exposed to toxic chemicals such as nitro-chlorobenzene (CDNB) through occupation, water, and even the air we breathe. Due to the severe toxicity caused by the high electrophilicity of CDNB, occupational and environmental exposure to CDNB can produce toxic effects that ultimately lead to cell damage. CDNB can be eliminated from organisms by binding to GSH, the catalytic product of glutathione S-transferase P1 (GSTP1). Therefore, GSTP1 plays an important role in the detoxification of CDNB. However, subtle variations in GSTP1 can result in single nucleotide polymorphisms (SNPs). Indeed, the correlation between the clinical outcome of the disease and certain genotypes of GSTP1 has been extensively studied, however, their impact on the metabolic detoxification of toxicants such as CDNB remains to be elucidated. Among the various SNPs of GSTP1, I105V has a significant effect on the catalytic activity of GSTP1. In this paper, a GSTP1 I105V polymorphism model was successfully established, and its effect on CDNB metabolism and toxicity was studied by computer analysis including molecular docking and molecular dynamics simulation. The result demonstrated that the binding capacity of CDNB decreases with the I105V mutation of GSTP1(p < 0.001), indicating the changes in its detoxification efficacy in CDNB-induced cell damage. Organisms expressing GSTP1 V105 are more susceptible to cell damage caused by CDNB than individuals expressing GSTP1 I105 (p < 0.001). In sum, the data in this study provide prospective insights into the mechanism and capacity of CDNB detoxification in the GSTP1 allele, extending the CDNB-mediated toxicological profile. In addition, the heterogeneity of the GSTP1 allele should be included in toxicological studies of individuals exposed to CDNB.