Identification of Xenobiotic Biotransformation Products Using Mass Spectrometry-Based Metabolomics Integrated with a Structural Elucidation Strategy by Assembling Fragment Signatures.

The identification of xenobiotic biotransformation products is crucial for delineating toxicity and carcinogenicity that might be caused by xenobiotic exposures and for establishing monitoring systems for public health. However, the lack of available reference standards and spectral data leads to the generation of multiple candidate structures during identification and reduces the confidence in identification. Here, a UHPLC-HRMS-based metabolomics strategy integrated with a metabolite structure elucidation approach, namely, FragAssembler, was proposed to reduce the number of false-positive structure candidates. biotransformation product candidates were filtered by mass defect filtering (MDF) and multiple-group comparison. FragAssembler assembled fragment signatures from the MS/MS spectra and generated the modified moieties corresponding to the identified biotransformation products. The feasibility of this approach was demonstrated by the three biotransformation products of di(2-ethylhexyl)phthalate (DEHP). Comprehensive identification was carried out, and 24 and 13 biotransformation products of two xenobiotics, DEHP and 4'-Methoxy-α-pyrrolidinopentiophenone (4-MeO-α-PVP), were annotated, respectively. The number of 4-MeO-α-PVP biotransformation product candidates in the FragAssembler calculation results was approximately 2.1 times lower than that generated by BioTransformer 3.0. Our study indicates that the proposed approach has great potential for efficiently and reliably identifying xenobiotic biotransformation products, which is attributed to the fact that FragAssembler eliminates false-positive reactions and chemical structures and distinguishes modified moieties on isomeric biotransformation products. The FragAssembler software and associated tutorial are freely available at https://cosbi.ee.ncku.edu.tw/FragAssembler/ and the source code can be found at https://github.com/YuanChihChen/FragAssembler.

Discovering Hair Biomarkers of Alzheimer's Disease Using High Resolution Mass Spectrometry-Based Untargeted Metabolomics.

Hair may be a potential biospecimen to discover biomarkers for Alzheimer's disease (AD) since it reflects the integral metabolic profiles of body burden over several months. Here, we described the AD biomarker discovery in the hair using a high-resolution mass spectrometry (HRMS)-based untargeted metabolomics approach. A total of 24 patients with AD and 24 age- and sex-matched cognitively healthy controls were recruited. The hair samples were collected 0.1-cm away from the scalp and further cut into 3-cm segments. Hair metabolites were extracted by ultrasonication with methanol/phosphate-buffered saline 50/50 (v/v) for 4 h. A total of 25 discriminatory chemicals in hair between the patients with AD and controls were discovered and identified. The AUC value achieved 0.85 (95% CI: 0.72~0.97) in patients with very mild AD compared to healthy controls using a composite panel of the 9 biomarker candidates, indicating high potential for the initiation or promotion phase of AD dementia in the early stage. A metabolic panel combined with the nine metabolites may be used as biomarkers for the early detection of AD. The hair metabolome can be used to reveal metabolic perturbations for biomarker discovery. Investigating perturbations of the metabolites will offer insight into the pathogenesis of AD.

Investigation of New Psychoactive Substances (NPS), Other Illicit Drugs, and Drug-Related Compounds in a Taiwanese Wastewater Sample Using High-Resolution Mass-Spectrometry-Based Targeted and Suspect Screening.

The proliferation of new psychoactive substances (NPSs) in recent years has posed a significant challenge to public health. Traditional monitoring methods have proven insufficient in tracking these constantly evolving substances, leading to the development of alternative approaches such as wastewater-based epidemiology (WBE). The present study aims to utilize high-resolution mass spectrometry (HRMS)-based targeted and suspect screening to profile NPS, other illicit drugs, and drug-related compounds in a Taiwanese wastewater sample. For the targeted analysis, 8 out 18 standards of illicit drugs have been identified. The suspect screening approach based on approximately 3600 substances in the SWGDRUG library can further identify 92 compounds, including opiate analgesics, synthetic cathinones, phenylalkylamines derivatives, phenethylamine derivatives, tryptamine derivatives, steroids, and ephedrine-related compounds. Additionally, the presence of 5-methoxy-2-aminoindane (MEAI) in the wastewater indicates that drug dealers have recently sold this potential NPS to evade drug regulations. This study firstly reports the HRMS-based comprehensive profile of NPS, other illicit drugs, and drug-related compounds in Taiwan, which could be applied as biomarkers for estimating the consumption of drugs.

A Fast FPGA Hardware Accelerator for Remote Heart Rate Detection Based on RGB Vision.

A fast hardware accelerator is created by this work via field programmable gate array (FPGA) to estimate heart rate (HR) through the video recorded by a RGB camera based on the technology of remote photoplethysmography (rPPG). The method of rPPG acquires physiological signals of a human body by analyzing the subtle color changes on the surface of the human skin. The hardware implementation of rPPG to estimate HR is proposed herein to aim for a much faster calculation speed than software for a number of applications, like heart failure pre-warning of an in-action athlete and drowsiness detection of a driver. In this accelerator, ICA (Independent Component Analysis) is used to recover the blood volume pulse from the raw signals of remote PPG, and then obtain the heart rate value. The architecture of the hardware circuit is described in Verilog HDL and verified by Quartus II, and also implemented in an Altera DE10-Standard FPGA board, which consists of image capture, heart rate algorithm and image display. A TRDB-D5M camera is utilized for image capture. Two experiments were conducted with image collecting duration of 16 seconds and 8 seconds respectively, and the commercial device Omron HEM-6111 was used as the golden value. The proposed system achieves an accuracy in (ME ± 1.96SD) of -0.76 ± 5.09 and -0.70 ± 8.71 bpm in the short periods of 16-second and 8-second versions, respectively, which outperforms all the reported prior works in combined computation time and accuracy.

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.

Development of a lateral flow immunoassays-based method for the screening of ractopamine in foods and evaluation of the optimal strategy in combination of screening and confirmatory tests.

Ractopamine has been authorized as a feed additive and permitted in animal husbandry. With the establishment of the regulation to limit the concentration of ractopamine, a rapid screening method for ractopamine is urgently needed. Additionally, how to combine the screening and confirmatory tests of ractopamine is also critical to maximizing the efficiency of testing. Here, we developed a lateral flow immunoassays-based method for the screening of ractopamine in foods and proposed a cost-benefit analysis approach to optimize cost allocation between screening and confirmatory tests. After verifying the analytical and clinical performances of the screening method, a mathematical model was established to calculate the screening and confirmatory test results with various parameter settings, such as cost allocation, false-negative tolerance, and total budget size. The developed immunoassay-based screening test could successfully distinguish gravy samples with ractopamine levels over and lower than maximum residue limits (MRL). The area under curve (AUC) value of receiver operating characteristic (ROC) curve is 0.99. For the cost-benefit analysis, mathematical simulation indicated that when the samples are allocated to screening and confirmatory tests at the optimized cost allocation, the number of confirmed positive samples can increase by 26 times compared to the scenarios entirely relying on confirmatory testing. While conventional wisdom considers that screening should be carried out at low false-negative rates, such as 0.1%, our results indicated that the cutoff value of a screening test with a 20% false-negative rate at MRL could capture the maximum number of confirmed positive samples at a limited budget. Our work indicated that the participation of the screening method in ractopamine analysis and optimized cost allocation between screening and confirmatory tests could enhance the efficiency in detecting the positive samples, which provides a rational basis for decision-making in food safety enforcement for public health.

Identifying Hair Biomarker Candidates for Alzheimer's Disease Using Three High Resolution Mass Spectrometry-Based Untargeted Metabolomics Strategies.

High-resolution mass spectrometry (HRMS)-based untargeted metabolomics strategies have emerged as an effective tool for discovering biomarkers of Alzheimer's disease (AD). There are various HRMS-based untargeted metabolomics strategies for biomarker discovery, including the data-dependent acquisition (DDA) method, the combination of full scan and target MS/MS, and the all ion fragmentation (AIF) method. Hair has emerged as a potential biospecimen for biomarker discovery in clinical research since it might reflect the circulating metabolic profiles over several months, while the analytical performances of the different data acquisition methods for hair biomarker discovery have been rarely investigated. Here, the analytical performances of three data acquisition methods in HRMS-based untargeted metabolomics for hair biomarker discovery were evaluated. The human hair samples from AD patients (N = 23) and cognitively normal individuals (N = 23) were used as an example. The most significant number of discriminatory features was acquired using the full scan (407), which is approximately 10-fold higher than that using the DDA strategy (41) and 11% higher than that using the AIF strategy (366). Only 66% of discriminatory chemicals discovered in the DDA strategy were discriminatory features in the full scan dataset. Moreover, compared to the deconvoluted MS/MS spectra with coeluted and background ions from the AIF method, the MS/MS spectrum obtained from the targeted MS/MS approach is cleaner and purer. Therefore, an untargeted metabolomics strategy combining the full scan with the targeted MS/MS method could obtain most discriminatory features along with a high quality MS/MS spectrum for discovering the AD biomarkers.

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

Hair has recently emerged as a biospecimen for characterizing the long-term chemical exposome in biomonitoring investigations spanning several months, as chemical compounds circulating in the bloodstream accumulate in hair. Although there has been interest in using human hair as a biospecimen for exposome studies, it has yet to be widely adopted compared to blood and urine. Here, we applied a high-resolution mass spectrometry (HRMS)-based suspect screening strategy to characterize the long-term chemical exposome in human hair. Hair samples were collected from 70 subjects and cut into 3 cm segments, which were then mixed to prepare pooled samples. The pooled hair samples underwent a sample preparation procedure, and the hair extracts were further analyzed using an HRMS-based suspect screening approach. An in-house chemical suspect list containing 1227 chemical entries from National Report on Human Exposure to Environmental Chemicals (Report) published by the U.S. CDC and the Exposome-Explorer 3.0 database developed by the WHO was subsequently used to screen and filter the suspect features against the HRMS dataset. Overall, we matched 587 suspect features in the HRMS dataset to 246 unique chemical formulas in the suspect list, and the structures of 167 chemicals were further identified through a fragmentation analysis. Among these, chemicals such as mono-2-ethylhexyl phthalate, methyl paraben, and 1-naphthol, which have been detected in the urine or blood for exposure assessment, were also identified in human hair. This suggests that hair reflects the accumulation of environmental compounds to which an individual is exposed. Exposure to exogenous chemicals may exert adverse effects on cognitive function, and we discovered 15 chemicals in human hair that may contribute to the pathogenesis of Alzheimer's disease. This finding suggests that human hair may be a promising biospecimen for monitoring long-term exposure to multiple environmental chemicals and perturbations in endogenous chemicals in biomonitoring investigations.

Exposure marker discovery of di-2(propylheptyl) phthalate using ultra-performance liquid chromatography-mass spectrometry and a rat model.

Di-(2-propylheptyl) phthalate (DPHP) is a plasticizer and has been suggested to be a subchronic toxicant in rats. DPHP has been approved to be used in food containers and handling by the U.S. Food and Drug Administration. The use of DPHP is still increasing, and the risk of human exposure to DPHP via food may be high. Exposure markers measured in human samples are commonly used to monitor human exposure levels. Ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) and a rat model were used to discover tentative DPHP exposure markers. DPHP and mono-(2-propylheptyl) phthalate (MPHP) were used as the precursors for calculating metabolite candidates using biotransformation mass changes of known enzymatic reactions. A rat model was designed to validate these metabolite candidates as tentative exposure markers. A total of 28 signals show dose-response relationships and these signals contain a few isomers. The chemical structures of 15 tentative exposure marker signals were speculated based on the product ion mass spectra from MS/MS analysis. These 15 signals included 7 chemical structures and some of them may be isomers. The different arrangement of the atoms in space of these isomers should be validated by standard compounds in the future studies. Among the 7 speculated chemical structures, 2 structures were novel tentative DPHP metabolites, and 5 structures have been previously reported in the literature. The results indicate that using UPLC-MS and a rat model can be used to identify tentative toxicant exposure markers.

Profiling and comparison of toxicant metabolites in hair and urine using a mass spectrometry-based metabolomic data processing method.

Urine and hair are used for assessing human exposure to toxicants. Urine tests can show acute toxicant exposure. Hair analysis can be used to determine chronic toxicant exposure after months to years; however, compared to urine, hair analysis in exposure assessments is much less frequently investigated. Urine and hair are different matrices, and their mechanisms of toxicant metabolite incorporation are different. The toxicant metabolites present in urine and hair may also be different. To clarify this issue, a procedure was developed to identify toxicant metabolites in rat samples using a mass spectrometry-based metabolomic data processing method. Di-(2-propylheptyl) phthalate (DPHP), an industrial plasticizer, was used as the model toxicant. The developed procedure identified not only known DPHP metabolites (mono-(propyl-6-oxo-heptyl) phthalate, mono-(propyl-6-hydroxyheptyl) phthalate, and mono-(propyl-6-carboxyhexyl) phthalate) but also novel metabolites that were structurally related to DPHP in the rat samples, indicating that the developed procedure successfully identified toxicant metabolites in in vivo samples. Among the 62 tentative metabolites identified from the 7th-day urine and the 28th-day hair samples, 33 were detected in only the urine samples, 19 were detected in only the hair samples, and 10 were identified in both the urine and hair samples. A total of 15 out of the 62 metabolites were confirmed as DPHP structure-related metabolites based on MS/MS analysis. Among the 15 DPHP structure-related metabolites, only 2 metabolites were present in both the urine and hair samples. These results suggested that the metabolites identified in urine could not be applied to exposure assessments based on hair analysis.

Exposure marker discovery of di(isononyl)cyclohexane-1,2-dicarboxylate using two mass spectrometry-based metabolite profiling data processing methods.

Di(isononyl)cyclohexane-1,2-dicarboxylate (DINCH) is a plasticizer used in polyvinyl chloride (PVC) products, such as toys and food packaging. Because the use of DINCH is on the rise, the risk of human exposure to this chemical may likewise increase. Discovering markers for assessing human chemical exposure is difficult because the metabolism of chemicals within humans is complex. In this study, two mass spectrometry (MS)-based metabolite profiling data processing methods, the mass defect filter (MDF) method and the signal mining algorithm with isotope tracing (SMAIT) method, were used for DINCH metabolite discovery, and 110 and 18 potential DINCH metabolite signal candidates were discovered, respectively, from in vitro DINCH incubation samples. Of these, the 21 signals were validated as tentative exposure marker signals in a rat model. Interestingly, the two methods generated rather different sets of DINCH exposure markers. Five of the 21 tentative exposure marker signals were verified as the probable DINCH structure-related metabolite signals based on their MS/MS product ion profiles. These five signals were detected in at least one human urine sample. Of the five probable DINCH structure-related metabolite signals, two novel signals might be suitable exposure markers that should be further investigated for their application in human DINCH exposure assessments. These observations indicate that the MDF and SMAIT methods may be used to discover a relatively different set of potential DINCH exposure markers.

Identification of urinary biomarkers of exposure to di-(2-propylheptyl) phthalate using high-resolution mass spectrometry and two data-screening approaches.

Di-(2-propylheptyl) phthalate (DPHP) is a plasticizer used in polyvinyl chloride and vinyl chloride copolymer that has been suggested to be a toxicant in rats and may affect human health. Because the use of DPHP is increasing, the general German population is being exposed to DPHP. Toxicant metabolism is important for human toxicant exposure assessments. To date, the knowledge regarding DPHP metabolism has been limited, and only four metabolites have been identified in human urine. Ultra-performance liquid chromatography was coupled with Orbitrap high-resolution mass spectrometry (MS) and two data-screening approaches-the signal mining algorithm with isotope tracing (SMAIT) and the mass defect filter (MDF)-for DPHP metabolite candidate discovery. In total, 13 and 104 metabolite candidates were identified by the two approaches, respectively, in in vitro DPHP incubation samples. Of these candidates, 17 were validated as tentative exposure biomarkers using a rat model, 13 of which have not been reported in the literature. The two approaches generated rather different tentative DPHP exposure biomarkers, indicating that these approaches are complementary for discovering exposure biomarkers. Compared with the four previously reported DPHP metabolites, the three tentative novel biomarkers had higher peak intensity ratios, and two were confirmed as DPHP hydroxyl metabolites based on their MS/MS product ion profiles. These three tentative novel biomarkers should be further investigated for potential application in human exposure assessment.

Exposure Marker Discovery of Phthalates Using Mass Spectrometry.

Phthalates are chemicals widely used in industry and the consequences on human health caused by exposure to these agents are of significant interest currently. The urinary metabolites of phthalates can be measured and used as exposure markers for the assessment of the actual internal contamination of phthalates coming from different sources and absorbed by various ways. The purpose of this paper is to review the markers for exposure and risk assessment of phthalates such as di-methyl phthalate (DMP), di-ethyl phthalate (DEP), di-butyl phthalate (DBP), benzylbutyl phthalate (BBP), di-(2-ethylhexyl)phthalate (DEHP), di-(2-propylheptyl)phthalate (DPHP), di-iso-nonyl phthalate (DINP), di-n-octyl phthalate (DnOP) and di-iso-decyl phthalate (DIDP), and introduction of the analytical approach of three metabolomics data processing approaches that can be used for chemical exposure marker discovery in urine with high-resolution mass spectrometry (HRMS) data.

Urinary exposure marker discovery for toxicants using ultra-high pressure liquid chromatography coupled with Orbitrap high resolution mass spectrometry and three untargeted metabolomics approaches.

Human biomonitoring is the assessment of actual internal contamination of chemicals by measuring exposure markers, chemicals or their metabolites, in human urine, blood, serum, and other body fluids. However, the metabolism of chemicals within an organism is extremely complex. Therefore, the identification of metabolites is often difficult and laborious. Several untargeted metabolomics methods have been developed to perform objective searching/filtering of accurate-mass-based LC-MS data to facilitate metabolite identification. In this study, three metabolomics data processing approaches were used for chemical exposure marker discovery in urine with an LTQ-Orbitrap high-resolution mass spectrometry (HRMS) dataset; di-isononyl phthalate (DINP) was used as an example. The data processing techniques included the SMAIT, mass defect filtering (MDF), and XCMS Online. Sixteen, 83, and 139 probable DINP metabolite signals were obtained using the SMAIT, MDF, and XCMS procedures, respectively. Fourteen probable metabolite signals mined simultaneously by the three metabolomics approaches were confirmed as DINP metabolites by structural information provided by LC-MS/MS. Among them, 13 probable metabolite signals were validated as exposure-related markers in a rat model. Six (m/z 319.155, 361.127, 373.126, 389.157, 437.112 and 443.130) of the 13 exposure-related DINP metabolite signals have not previously been reported in the literature. Our data indicate that SMAIT provided an efficient method to discover effectively and systematically urinary exposure markers of toxicant. The DINP metabolism information can provide valuable information for further investigations of DINP toxicity, toxicokinetics, exposure assessment, and human health effects.

The potential use of diisononyl phthalate metabolites hair as biomarkers to assess long-term exposure demonstrated by a rat model.

Diisononyl phthalate (DINP) is a widely used industrial plasticizer. People come into contact with this chemical by using plastic products made with it. Human health can be adversely affected by long-term DINP exposure. However, because the body rapidly excretes DINP metabolites, the use of single-point urine analysis to assess long-term exposure may produce inconsistent results in epidemiologic studies. Hair analysis has a useful place in biomonitoring, particularly in estimating long-term or historical exposure for some chemicals. Several studies have reported using hair analysis to assess the concentrations of heavy metals, drugs and organic pollutants in humans. As a biomarker, DINP metabolites were measured in rat hair in animal experiments to evaluated long-term exposure to DINP. In addition, we evaluated the correlation between the levels of DINP metabolites in hair and in urine. The levels of DINP metabolites in rat hair were significantly higher in the exposure group, relative to the control group (p<0.05). DINP metabolites had a positive correlation with increasing administered dose. Significant positive correlations for MINP, MOINP and MHINP were found between hair and urine (r=0.86, r=0.79 and r=0.74, respectively, p<0.05). Several metabolites in urine showed earlier saturation than in hair. In this report, we detected eight metabolites in hair and demonstrate that hair analysis has potential applications in the assessment of long-term exposure to DINP.

Background levels of persistent organic pollutants in humans from Taiwan: perfluorooctane sulfonate and perfluorooctanoic acid.

Perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA) have recently received attention due to their widespread contamination of the environment. PFOS and PFOA are stable in the environment and resistant to metabolism, hydrolysis, photolysis and biodegradation. PFOS and PFOA have been found in human blood and tissue samples from both occupationally exposed workers and the general worldwide population. This study aimed to determine the background levels of PFOS and PFOA in the Taiwanese population, investigate related factors, and compare exposure in Taiwan to that in other countries. The concentration of PFOS in the 59 serum samples collected from the general population in Taiwan ranged from 3.45 to 25.65ngmL(-1) (median: 8.52), and the concentration of PFOA ranged from 1.55 to 7.69ngmL(-1) (median: 3.22). There was a significant positive correlation (r=0.51; p<0.0001) between PFOS and PFOA concentrations. Males had higher concentrations of PFOA and PFOS than females. PFOS levels in serum increased with age. This study is the first investigation to reveal the PFOS and PFOA levels of serum samples in the general population of Taiwan. The levels of PFOS and PFOA in Taiwanese serum samples were comparable with those from other countries (PFOS: 5.0-35ngmL(-1), PFOA: 1.5-10ngmL(-1)).