Monitoring long-term chemical exposome by characterizing the hair metabolome using a high-resolution mass spectrometry-based suspect screening approach

Xenometabolomics in Ecotoxicology: Concepts and Applications

Nontargeted high-resolution mass spectrometry (HRMS) allows for the characterization of a large fraction of the exposome, i.e., the entirety of chemicals an organism is exposed to, and helps detect important exogenous chemical compounds that could be key drivers of toxicological impact. Along with these chemical compounds occur endogenous metabolites that are essential for the health of the host organism. Chemical compounds derived from the biotransformation of xenobiotics present in the exposome are referred to as the xenometabolome, while endogenous metabolites derived from the host organism are referred to as the endometabolome. Recent advancements in HRMS technology allow for the detection of chemical features of biological and ecological importance in the context of chemical safety assessments with unprecedented sensitivity and resolution. In this perspective, we highlight the application of HRMS-based metabolomics of organisms in the context of ecotoxicology, the complexity of comprehensively characterizing the endometabolome, and distinguishing chemical compounds of the xenometabolome.

Constructing HairDB to facilitate exposome research using human hair

This study introduces HairDB, an online database serving as a comprehensive repository of hair-related chemicals for exposome research. HairDB was created via an integrative approach. It first extracted 4,184 unique hair-related chemicals through text mining of over 34 million PubMed abstracts and 5.2 million PubMed Central articles, followed by manual data checking. HairDB also applied an artificial intelligence-enabled search to discover organic aerosol biomarkers in literature. A set of 768 chemicals used in hair-related products was then curated through a combination of manual searches and data extraction from the Cosmetic Ingredient Database (CosIng) of the European Union. From manually reading review papers, 29 organic aerosol biomarkers were extracted. Furthermore, 3,679 known exposure chemicals extracted from the Toxin and Toxin Target Database (T3DB) were incorporated in HairDB to represent the possible environmental exposures detected on hair surfaces. The comprehensive set of chemicals captured in HairDB represents the current knowledge of what can be found in and on hair. HairDB was constructed as a user-friendly web interface, allowing easy exploration of hair-related chemicals and tailored for annotating mass spectrometry-based hair exposomics data. The development of HairDB marks an important step forward in using hair as a biological matrix for chemical exposure measurement, facilitating the adoption of hair for exposome research. HairDB is publicly available at https://www.hairdb.ca/.

Optimized analytical strategy based on high-resolution mass spectrometry for unveiling associations between long-term chemical exposome in hair and Alzheimer's disease

Exposure to environmental pollutants or contaminants is correlated with detrimental effects on human health, such as neurodegenerative diseases. Adopting hair as a biological matrix for biomonitoring is a significant innovation, since it can reflect the long-term chemical exposome, spanning months to years. However, only a limited number of studies have developed analytical strategies for profiling the chemical exposome in this heterogeneous biological matrix. In this study, a systematic investigation of the chemical extraction procedure from human hair was conducted, using a design of experiments and a high-resolution mass spectrometry (HRMS)-based suspect screening approach. The PlackettBurman (PB) design was applied to identify the significant variables influencing the number of detected features. Then, a central composite design was implemented to optimize the levels of each identified significant variable. Under the optimal conditions-15-minute pulverization, 25 mg of hair weight, 40 min of sonication, and a sonication temperature of 35 °C-approximately 32,000 and 15,000 aligned features were detected in positive and negative ion modes, respectively. This optimized analytical procedure was applied to hair samples from patients with Alzheimer's disease (AD) and individuals with normal cognitive function. Overall, 307 chemicals were identified using the suspect screening approach, with 37 chemicals differentiating patients with AD from controls. This study not only optimized an analytical procedure for characterizing the long-term chemical exposome in human hair but also explored the associations between AD and environmental factors.

Assessing the contribution of the chemical exposome to neurodegenerative disease

Over the past few decades, numerous environmental chemicals from solvents to pesticides have been suggested to be involved in the development and progression of neurodegenerative diseases. Most of the evidence has accumulated from occupational or cohort studies in humans or laboratory research in animal models, with a range of chemicals being implicated. What has been missing is a systematic approach analogous to genome-wide association studies, which have identified dozens of genes involved in Alzheimer's disease, Parkinson's disease and other neurodegenerative diseases. Fortunately, it is now possible to study hundreds to thousands of chemical features under the exposome framework. This Perspective explores how advances in mass spectrometry make it possible to generate exposomic data to complement genomic data and thereby better understand neurodegenerative diseases.

Characterization of Hair Metabolome in 5xFAD Mice and Patients with Alzheimer's Disease Using Mass Spectrometry-Based Metabolomics

Hair emerged as a biospecimen for long-term investigation of endogenous metabolic perturbations, reflecting the chemical composition circulating in the blood over the past months. Despite its potential, the use of human hair for metabolomics in Alzheimer's disease (AD) research remains limited. Here, we performed both untargeted and targeted metabolomic approaches to profile the key metabolic pathways in the hair of 5xFAD mice, a widely used AD mouse model. Furthermore, we applied the discovered metabolites to human subjects. Hair samples were collected from 6-month-old 5xFAD mice, a stage marked by widespread accumulation of amyloid plaques in the brain, followed by sample preparation and high-resolution mass spectrometry analysis. Forty-five discriminatory metabolites were discovered in the hair of 6-month-old 5xFAD mice compared to wild-type control mice. Enrichment analysis revealed three key metabolic pathways: arachidonic acid metabolism, sphingolipid metabolism, and alanine, aspartate, and glutamate metabolism. Among these pathways, six metabolites demonstrated significant differences in the hair of 2-month-old 5xFAD mice, a stage prior to the onset of amyloid plaque deposition. These findings suggest their potential involvement in the early stages of AD pathogenesis. When evaluating 45 discriminatory metabolites for distinguishing patients with AD from nondemented controls, a combination of l-valine and arachidonic acid significantly differentiated these two groups, achieving a 0.88 area under the curve. Taken together, these findings highlight the potential of hair metabolomics in identifying disease-specific metabolic alterations and developing biomarkers for improving disease detection and monitoring.