Large scale enzyme based xenobiotic identification for exposomics

Simultaneous profiling of mercapturic acids, glucuronic acids, and sulfates in human urine

Humans are constantly exposed to both naturally-occurring and anthropogenic chemicals. Targeted mass spectrometry approaches are frequently used to measure a small panel of chemicals and their metabolites in environmental or biological matrices, but methods for comprehensive individual-level exposure assessment are limited. In this study, we applied an integrated library-guided analysis (ILGA) with ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) to profile phase II metabolites, specifically mercapturic acids (MAs), glucuronic acids (GAs), and sulfates (SAs) in human urine samples (n = 844). We annotated 424 metabolites (146 MAs, 171 GAs, 107 SAs) by querying chromatographic features with in-house structural libraries, filtering against fragmentation patterns (common neutral loss and ion fragment), and comparing mass spectra with in-silico fragmentations and external spectral libraries. These metabolites were derived from over 200 putative parent compounds of exogenous and endogenous sources, such as dietary compounds, benzene/monocyclic substituted aromatics, pharmaceuticals, polycyclic aromatic hydrocarbons, bile acids/bile salts, and 4-hydroxyalkenals associated with lipid peroxidation process. Further, we performed statistical analyses on 214 metabolites found in more than 75% of samples to examine the association between metabolites and demographic characteristics using integrated network analysis, principal component analysis (PCA), and multivariable linear regression models. The network analysis revealed four distinct communities of 37 positively correlated metabolites, and the PCA (using the 37 metabolites) presented 4 principal components that meaningfully explained at least 80% of the variance in the data. The multivariable linear regression models showed some positive and negative associations between metabolite profiles and certain demographic variables (e.g., age, sex, race, education, income, and tobacco use).

Liquid and gas-chromatography-mass spectrometry methods for exposome analysis

Mass spectrometry-based methods have become fundamental to exposome research, providing the capability to explore a broad spectrum of chemical exposures. Liquid and gas chromatography coupled with low/high-resolution mass spectrometry (MS) are among the most frequently employed platforms due to their sensitivity and accuracy. However, these approaches present challenges, such as the inherent complexity of MS data and the expertise of biologists, chemists, clinicians, and data analysts to integrate and interpret MS data with other datasets effectively. The "omics" era advances rapidly, driven by developments of AI-based algorithms and an increase in accessible data; nevertheless, further efforts are necessary to ensure that exposomics outputs are comparable and reproducible, thus enhancing research findings. This review outlines the principles of MS-based methods for the exposome analytical pipeline, from sample collection to data analysis. We summarize and review both standard and cutting-edge strategies in exposome research, covering sample preparation, focusing on MS-based platforms, data acquisition strategies, and data annotation. The ultimate goal of this review is to highlight applications that enable the simultaneous analysis of endogenous metabolites and xenobiotics, which can help enhance our understanding of the impact of human exposure on health and disease and support personalized healthcare.

Pharmacometabolomics in TB meningitis-Understanding the pharmacokinetic, metabolic, and immune factors associated with anti-TB drug concentrations in cerebrospinal fluid

Poor penetration of many anti-tuberculosis (TB) antibiotics into the central nervous system (CNS) is thought to be a major driver of morbidity and mortality in TB meningitis (TBM). While the amount of a particular drug that crosses into the cerebrospinal fluid (CSF) varies from person to person, little is known about the host factors associated with interindividual differences in CSF concentrations of anti-TB drugs. In patients diagnosed with TBM from the country of Georgia (n = 17), we investigate the association between CSF concentrations of anti-TB antibiotics and multiple host factors including serum drug concentrations and CSF concentrations of metabolites and cytokines. We found > 2-fold differences in CSF concentrations of anti-TB antibiotics from person to person for all drugs tested including cycloserine, ethambutol, imipenem, isoniazid, levofloxacin, linezolid, moxifloxacin, pyrazinamide, and rifampin. While serum drug concentrations explained over 30% of the variation in CSF drug concentrations for cycloserine, isoniazid, linezolid, and pyrazinamide (adjusted R2 ≥ 0.3, p < 0.001 for all), there was no significant association between serum concentrations of imipenem and ethambutol and their respective CSF concentrations. CSF concentrations of carnitines were significantly associated with concentrations of ethambutol and imipenem (q < 0.05), and imipenem was the only antibiotic significantly associated with CSF cytokine concentrations. These results indicate that there is high interindividual variability in CSF drug concentrations in patients treated for TBM, which is only partially explained by differences in serum drug concentrations. With the exception of imipenem, there was no association between CSF drug concentrations and concentrations of cytokines and chemokines.

Redox Metabolomics of Menthol in Children's Plasma with Second-Hand Cigarette and Electronic Cigarette Exposures

Background

Cigarettes and electronic cigarettes generate many redox-active materials which could impact children's health through second-hand exposures. High-resolution metabolomics methods enable use of non-targeted mass spectrometry of plasma to test for redox consequences of second-hand exposures.

Objectives

Our objectives were to test for oxidative stress metabolites and altered metabolic pathways associated with second-hand exposure to redox-active flavorants and flavorant metabolites in plasma of infants and children.

Methods

Untargeted plasma metabolomics data for infants and children in a population known to include individuals with second-hand exposures to cigarettes and electronic cigarettes were analyzed for cotinine and metabolites of flavorants. A metabolome-wide association study (MWAS) was performed separately for cotinine and menthol glucuronide, derived from the redox-active flavorant, menthol. Pathway enrichment analysis was used to identify metabolic pathways, and xMWAS was used to detect metabolic communities associated with flavorant metabolites.

Results

Menthol glucuronide was one of several flavorant metabolites positively correlated with cotinine. MWAS and pathway enrichment analysis revealed that some pathways associated with both menthol glucuronide and cotinine, while others only associated with menthol glucuronide, including sphingolipid, glycerophospholipid, antioxidant, N-glycan and mitochondrial energy metabolism. 4-hydroxynonenal and other oxidized lipids positively correlated with menthol glucuronide.

Discussion

The results show that flavorants from second-hand electronic cigarette and cigarette exposures in infants and children are associated with changes in redox metabolism which are known to associate with human lung diseases.

An Enhanced Protocol to Expand Human Exposome and Machine Learning-Based Prediction for Methodology Application

The human exposome remains limited due to the challenging analytical strategies used to reveal low-level endocrine-disrupting chemicals (EDCs) and their metabolites in serum and urine. This limits the integrity of the EDC exposure assessment and hinders understanding of their cumulative health effects. In this study, we propose an enhanced protocol based on multi-solid-phase extraction (multi-SPE) to expand human exposome with polar EDCs and metabolites and train a machine learning (ML) model for methodology prediction based on molecular descriptors. The protocol enhanced the measurement of 70 (25%) and 34 (12%) out of 295 well-acknowledged EDCs in serum and urine compared to the hydrophilic-lipophilic balance sorbent alone. In a nontarget analysis of serum and urine from 20 women of childbearing age in a cohort of 498, controlling occupational factors and daily behaviors for high chemical exposure potential, the multi-SPE protocol increased the measurement of 10 (40%) and 16 (53%) target EDCs and identification of 17 (77%) and 70 (36%) nontarget chemicals (confidence ≥ level 3) in serum and urine, respectively. Interestingly, the ML model predicted that the multi-SPE protocol could identify an additional 38% of the most bioactive chemicals. In conclusion, the multi-SPE protocol advances human exposome by expanding the measurement and identification of exposure profiles.

Introducing "Identification Probability" for Automated and Transferable Assessment of Metabolite Identification Confidence in Metabolomics and Related Studies

Methods for assessing compound identification confidence in metabolomics and related studies have been debated and actively researched for the past two decades. The earliest effort in 2007 focused primarily on mass spectrometry and nuclear magnetic resonance spectroscopy and resulted in four recommended levels of metabolite identification confidence─the Metabolite Standards Initiative (MSI) Levels. In 2014, the original MSI Levels were expanded to five levels (including two sublevels) to facilitate communication of compound identification confidence in high resolution mass spectrometry studies. Further refinement in identification levels have occurred, for example to accommodate use of ion mobility spectrometry in metabolomics workflows, and alternate approaches to communicate compound identification confidence also have been developed based on identification points schema. However, neither qualitative levels of identification confidence nor quantitative scoring systems address the degree of ambiguity in compound identifications in the context of the chemical space being considered. Neither are they easily automated nor transferable between analytical platforms. In this perspective, we propose that the metabolomics and related communities consider identification probability as an approach for automated and transferable assessment of compound identification and ambiguity in metabolomics and related studies. Identification probability is defined simply as 1/N, where N is the number of compounds in a database that matches an experimentally measured molecule within user-defined measurement precision(s), for example mass measurement or retention time accuracy, etc. We demonstrate the utility of identification probability in an in silico analysis of multiproperty reference libraries constructed from a subset of the Human Metabolome Database and computational property predictions, provide guidance to the community in transparent implementation of the concept, and invite the community to further evaluate this concept in parallel with their current preferred methods for assessing metabolite identification confidence.

Mammalian hydroxylation of microbiome-derived obesogen, delta-valerobetaine, to homocarnitine, a 5-carbon carnitine analog

The recently discovered microbiome-generated obesogen, δ-valerobetaine (5-(trimethylammonio)pentanoate), is a 5-carbon structural analog of the carnitine precursor, γ-butyrobetaine. Here, we report that δ-valerobetaine is enzymatically hydroxylated by mammalian γ-butyrobetaine dioxygenase (BBOX) to form 3-hydroxy-5-(trimethylammonio)pentanoate, a 5-carbon analog of carnitine, which we term homocarnitine. Homocarnitine production by human liver extracts depends upon the required BBOX cofactors, 2-oxoglutarate, Fe2+, and ascorbate. Molecular dynamics simulations show successful docking of δ-valerobetaine and homocarnitine to BBOX, pharmacological inhibition of BBOX prevents homocarnitine production, and transfection of a liver cell line with BBOX substantially increases production. Furthermore, an in vivo isotope tracer study shows the conversion of 13C3-trimethyllysine to 13C3-δ-valerobetaine then 13C3-homocarnitine in mice, confirming the in vivo production of homocarnitine. Functional assays show that carnitine palmitoyltransferase acylates homocarnitine to acyl-homocarnitine, analogous to the reactions for the carnitine shuttle. Studies of mouse tissues and human plasma show widespread distribution of homocarnitine and fatty acyl-homocarnitines. The respective structural similarities of homocarnitine and acyl-homocarnitines to carnitine and acyl-carnitines indicate that homocarnitine could impact multiple sites of carnitine distribution and activity, potentially mediating microbiome-associated obesity and metabolic disorders.

Introducing 'identification probability' for automated and transferable assessment of metabolite identification confidence in metabolomics and related studies

Methods for assessing compound identification confidence in metabolomics and related studies have been debated and actively researched for the past two decades. The earliest effort in 2007 focused primarily on mass spectrometry and nuclear magnetic resonance spectroscopy and resulted in four recommended levels of metabolite identification confidence - the Metabolite Standards Initiative (MSI) Levels. In 2014, the original MSI Levels were expanded to five levels (including two sublevels) to facilitate communication of compound identification confidence in high resolution mass spectrometry studies. Further refinement in identification levels have occurred, for example to accommodate use of ion mobility spectrometry in metabolomics workflows, and alternate approaches to communicate compound identification confidence also have been developed based on identification points schema. However, neither qualitative levels of identification confidence nor quantitative scoring systems address the degree of ambiguity in compound identifications in context of the chemical space being considered, are easily automated, or are transferable between analytical platforms. In this perspective, we propose that the metabolomics and related communities consider identification probability as an approach for automated and transferable assessment of compound identification and ambiguity in metabolomics and related studies. Identification probability is defined simply as 1/N, where N is the number of compounds in a reference library or chemical space that match to an experimentally measured molecule within user-defined measurement precision(s), for example mass measurement or retention time accuracy, etc. We demonstrate the utility of identification probability in an in silico analysis of multi-property reference libraries constructed from the Human Metabolome Database and computational property predictions, provide guidance to the community in transparent implementation of the concept, and invite the community to further evaluate this concept in parallel with their current preferred methods for assessing metabolite identification confidence.

The associations between pre-conception urinary phthalate concentrations, the serum metabolome, and live birth among women undergoing assisted reproduction

BACKGROUND

Phthalates are ubiquitous endocrine disruptors. Past studies have shown an association between higher preconception urinary concentrations of phthalate metabolites and lower fertility in women; however, the biological mechanisms remain unclear. Our exploratory study aimed to understand the metabolites and pathways associated with maternal preconception phthalate exposure and examine if any may underline the association between phthalate exposure and live birth using untargeted metabolomics.

METHODS

Participants (n = 183) were part of the Environment and Reproductive Health (EARTH) study, a prospective cohort that followed women undergoing in vitro fertilization (IVF) at the Massachusetts General Hospital Fertility Center (2005-2016). On the same day, women provided a serum sample during controlled ovarian stimulation, which was analyzed for metabolomics using liquid chromatography coupled with high-resolution mass spectrometry and two chromatography columns, and a urine sample, which was analyzed for 11 phthalate metabolites using targeted approaches. We used multivariable generalized linear models to identified metabolic features associated with urinary phthalate metabolite concentrations and live birth, followed by enriched pathway analysis. We then used a meet-in-the-middle approach to identify overlapping pathways and features.

RESULTS

Metabolic pathway enrichment analysis revealed 43 pathways in the C18 negative and 32 pathways in the HILIC positive columns that were significantly associated (p < 0.05) with at least one of the 11 urinary phthalate metabolites or molar sum of di-2-ethylhexyl phthalate metabolites. Lipid, amino acid, and carbohydrate metabolism were the most common pathways associated with phthalate exposure. Five pathways, tryptophan metabolism, tyrosine metabolism, biopterin metabolism, carnitine shuttle, and vitamin B6 metabolism, were also identified as being associated with at least one phthalate metabolite and live birth following IVF.

CONCLUSION

Our study provides further insight into the metabolites and metabolomics pathways, including amino acid, lipid, and vitamin metabolism that may underlie the observed associations between phthalate exposures and lower fertility in women.

Association of Coffee Consumption and Prediagnostic Caffeine Metabolites With Incident Parkinson Disease in a Population-Based Cohort

BACKGROUND AND OBJECTIVES

Inverse associations between caffeine intake and Parkinson disease (PD) have been frequently implicated in human studies. However, no studies have quantified biomarkers of caffeine intake years before PD onset and investigated whether and which caffeine metabolites are related to PD.

METHODS

Associations between self-reported total coffee consumption and future PD risk were examined in the EPIC4PD study, a prospective population-based cohort including 6 European countries. Cases with PD were identified through medical records and reviewed by expert neurologists. Hazard ratios (HRs) and 95% CIs for coffee consumption and PD incidence were estimated using Cox proportional hazards models. A case-control study nested within the EPIC4PD was conducted, recruiting cases with incident PD and matching each case with a control by age, sex, study center, and fasting status at blood collection. Caffeine metabolites were quantified by high-resolution mass spectrometry in baseline collected plasma samples. Using conditional logistic regression models, odds ratios (ORs) and 95% CIs were estimated for caffeine metabolites and PD risk.

RESULTS

In the EPIC4PD cohort (comprising 184,024 individuals), the multivariable-adjusted HR comparing the highest coffee intake with nonconsumers was 0.63 (95% CI 0.46-0.88, p = 0.006). In the nested case-control study, which included 351 cases with incident PD and 351 matched controls, prediagnostic caffeine and its primary metabolites, paraxanthine and theophylline, were inversely associated with PD risk. The ORs were 0.80 (95% CI 0.67-0.95, p = 0.009), 0.82 (95% CI 0.69-0.96, p = 0.015), and 0.78 (95% CI 0.65-0.93, p = 0.005), respectively. Adjusting for smoking and alcohol consumption did not substantially change these results.

DISCUSSION

This study demonstrates that the neuroprotection of coffee on PD is attributed to caffeine and its metabolites by detailed quantification of plasma caffeine and its metabolites years before diagnosis.

Pharmacometabolomics in TB Meningitis - understanding the pharmacokinetic, metabolic, and immune factors associated with anti-TB drug concentrations in cerebrospinal fluid

Poor penetration of many anti-tuberculosis (TB) antibiotics into the central nervous system (CNS) is thought to be a major driver of morbidity and mortality in TB meningitis (TBM). While the amount of a particular drug that crosses into the cerebrospinal fluid (CSF) varies from person to person, little is known about the host factors associated with interindividual differences in CSF concentrations of anti-TB drugs. In patients diagnosed with TBM from the country of Georgia (n=17), we investigate the association between CSF concentrations of anti-TB antibiotics and multiple host factors including serum drug concentrations and CSF concentrations of metabolites and cytokines. We found >2-fold differences in CSF concentrations of anti-TB antibiotics from person to person for all drugs tested including cycloserine, ethambutol, imipenem, isoniazid, levofloxacin, linezolid, moxifloxacin pyrazinamide, and rifampin. While serum drug concentrations explained over 40% of the variation in CSF drug concentrations for cycloserine, isoniazid, linezolid, and pyrazinamide (adjusted R 2 >0.4, p<0.001 for all), there was no evidence of an association between serum concentrations of imipenem and ethambutol and their respective CSF concentrations. CSF concentrations of carnitines were significantly associated with concentrations of ethambutol and imipenem (q<0.05), and imipenem was the only antibiotic significantly associated with CSF cytokine concentrations. These results indicate that there is high interindividual variability in CSF drug concentrations in patients treated for TBM, which is only partially explained by differences in serum drug concentrations and not associated with concentrations of cytokines and chemokines in the CSF.

Metabolite Stability in Archived Neonatal Dried Blood Spots Used for Epidemiologic Research

Epidemiologic studies of low-frequency exposures or outcomes using metabolomics analyses of neonatal dried blood spots (DBS) often require assembly of samples with substantial differences in duration of storage. Independent assessment of stability of metabolites in archived DBS will enable improved design and interpretation of epidemiologic research utilizing DBS. Neonatal DBS routinely collected and stored as part of the California Genetic Disease Screening Program between 1983 and 2011 were used. The study population included 899 children without cancer before age 6 years, born in California. High-resolution metabolomics with liquid-chromatography mass spectrometry was performed, and the relative ion intensities of common metabolites and selected xenobiotic metabolites of nicotine (cotinine and hydroxycotinine) were evaluated. In total, we detected 26,235 mass spectral features across 2 separate chromatography methods (C18 hydrophobic reversed-phase chromatography and hydrophilic-interaction liquid chromatography). For most of the 39 metabolites related to nutrition and health status, we found no statistically significant annual trends across the years of storage. Nicotine metabolites were captured in the DBS with relatively stable intensities. This study supports the usefulness of DBS stored long-term for epidemiologic studies of the metabolome. -Omics-based information gained from DBS may also provide a valuable tool for assessing prenatal environmental exposures in child health research.

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.

Mussel mass mortality in the Clinch River, USA: metabolomics detects affected pathways and biomarkers of stress

Biologists monitoring freshwater mussel (order Unionida) populations rely on behavioral, often subjective, signs to identify moribund ("sick") or stressed mussels, such as gaping valves and slow response to probing, and they lack clinical indicators to support a diagnosis. As part of a multi-year study to investigate causes of reoccurring mortality of pheasantshell (Ortmanniana pectorosa; synonym Actinonaias pectorosa) in the Clinch River, Virginia and Tennessee, USA, we analyzed the hemolymph metabolome of a subset of mussels from the 2018 sampling period. Mussels at the mortality sites were diagnosed in the field as affected (case) or unaffected (control) based on behavioral and physical signs. Hemolymph was collected in the field by non-lethal methods from the anterior adductor muscle for analysis. We used ultra-high-performance liquid chromatography with quadrupole time-of-flight mass spectroscopy to detect targeted and untargeted metabolites in hemolymph and compared metabolomic profiles by field assessment of clinical status. Targeted biomarker analysis found 13 metabolites associated with field assessments of clinical status. Of these, increased gamma-linolenic acid and N-methyl-l-alanine were most indicative of case mussels, while adenine and inosine were the best indicators of control mussels. Five pathways in the targeted analysis differed by clinical status; two of these, purine metabolism and glycerophospholipid metabolism, were also indicated in the untargeted analysis. In the untargeted nalysis, 22 metabolic pathways were associated with clinical status. Many of the impacted pathways in the case group were catabolic processes, such as degradation of amino acids and fatty acids. Hierarchical clustering analysis matched clinical status in 72% (18 of 25) of mussels, with control mussels more frequently (5 of 16) not matching clinical status. Our study demonstrated that metabolomic analysis of hemolymph is suitable for assessing mussel condition and complements field-based indicators of health.

Exploring Single-Cell Exposomics by Mass Spectrometry

Single-cell exposomics, a revolutionary approach that investigates cell-environment interactions at cellular and subcellular levels, stands distinct from conventional bulk exposomics. Leveraging advancements in mass spectrometry, it provides a detailed perspective on cellular dynamics, interactions, and responses to environmental stimuli and their impacts on human health. This work delves into this innovative realm, highlighting the nuanced interplay between environmental stressors and biological responses at cellular and subcellular levels. The application of spatial mass spectrometry in single-cell exposomics is discussed, revealing the intricate spatial organization and molecular composition within individual cells. Cell-type-specific exposomics, shedding light on distinct susceptibilities and adaptive strategies of various cell types to environmental exposures, is also examined. The Perspective further emphasizes the integration with molecular and cellular biology approaches to validate hypotheses derived from single-cell exposomics in a comprehensive biological context. Looking toward the future, we anticipate continued technological advancements and convergence with other -omics approaches and discuss implications for environmental health research, disease progression studies, and precision medicine. The final emphasis is on the need for robust computational tools and interdisciplinary collaboration to fully leverage the potential of single-cell exposomics, acknowledging the complexities inherent to this paradigm.

Advances in Ultra-High-Resolution Mass Spectrometry for Pharmaceutical Analysis

Pharmaceutical analysis refers to an area of analytical chemistry that deals with active compounds either by themselves (drug substance) or when formulated with excipients (drug product). In a less simplistic way, it can be defined as a complex science involving various disciplines, e.g., drug development, pharmacokinetics, drug metabolism, tissue distribution studies, and environmental contamination analyses. As such, the pharmaceutical analysis covers drug development to its impact on health and the environment. Moreover, due to the need for safe and effective medications, the pharmaceutical industry is one of the most heavily regulated sectors of the global economy. For this reason, powerful analytical instrumentation and efficient methods are required. In the last decades, mass spectrometry has been increasingly used in pharmaceutical analysis both for research aims and routine quality controls. Among different instrumental setups, ultra-high-resolution mass spectrometry with Fourier transform instruments, i.e., Fourier transform ion cyclotron resonance (FTICR) and Orbitrap, gives access to valuable molecular information for pharmaceutical analysis. In fact, thanks to their high resolving power, mass accuracy, and dynamic range, reliable molecular formula assignments or trace analysis in complex mixtures can be obtained. This review summarizes the principles of the two main types of Fourier transform mass spectrometers, and it highlights applications, developments, and future perspectives in pharmaceutical analysis.

PISTACHIo (PreemptIon of diSrupTive behAvior in CHIldren): real-time monitoring of sleep and behavior of children 3-7 years old receiving parent-child interaction therapy augment with artificial intelligence - the study protocol, pilot study

BACKGROUND

Emotional behavior problems (EBP) are the most common and persistent mental health issues in early childhood. Early intervention programs are crucial in helping children with EBP. Parent-child interaction therapy (PCIT) is an evidence-based therapy designed to address personal difficulties of parent-child dyads as well as reduce externalizing behaviors. In clinical practice, parents consistently struggle to provide accurate characterizations of EBP symptoms (number, timing of tantrums, precipitating events) even from the week before in their young children. The main aim of the study is to evaluate feasibility of the use of smartwatches in children aged 3-7 years with EBP.

METHODS

This randomized double-blind controlled study aims to recruit a total of 100 participants, consisting of 50 children aged 3-7 years with an EBP measure rated above the clinically significant range (T-score ≥ 60) (Eyberg Child Behavior Inventory-ECBI; Eyberg & Pincus, 1999) and their parents who are at least 18 years old. Participants are randomly assigned to the artificial intelligence-PCIT group (AI-PCIT) or the PCIT-sham biometric group. Outcome parameters include weekly ECBI and Pediatric Sleep Questionnaire (PSQ) as well as Child Behavior Checklist (CBCL) obtained weeks 1, 6, and 12 of the study. Two smartphone applications (Garmin connect and mEMA) and a wearable Garmin smartwatch are used collect the data to monitor step count, sleep, heart rate, and activity intensity. In the AI-PCIT group, the mEMA application will allow for the ecological momentary assessment (EMA) and will send behavioral alerts to the parent.

DISCUSSION

Real-time predictive technologies to engage patients rely on daily commitment on behalf of the participant and recurrent frequent smartphone notifications. Ecological momentary assessment (EMA) provides a way to digitally phenotype in-the-moment behavior and functioning of the parent-child dyad. One of the study's goals is to determine if AI-PCIT outcomes are superior in comparison with standard PCIT. Overall, we believe that the PISTACHIo study will also be able to determine tolerability of smartwatches in children aged 3-7 with EBP and could participate in a fundamental shift from the traditional way of assessing and treating EBP to a more individualized treatment plan based on real-time information about the child's behavior.

TRIAL REGISTRATION

The ongoing clinical trial study protocol conforms to the international Consolidated Standards of Reporting Trials (CONSORT) guidelines and is registered in clinicaltrials.gov (ID: NCT05077722), an international clinical trial registry.

Using follicular fluid metabolomics to investigate the association between air pollution and oocyte quality

BACKGROUND AND AIM

Our objective was to use metabolomics in a toxicological-relevant target tissue to gain insight into the biological processes that may underlie the negative association between air pollution exposure and oocyte quality.

METHODS

Our study included 125 women undergoing in vitro fertilization at an academic fertility center in Massachusetts, US (2005-2015). A follicular fluid sample was collected during oocyte retrieval and untargeted metabolic profiling was conducted using liquid chromatography with ultra-high-resolution mass spectrometry and two chromatography columns (C18 and HILIC). Daily exposure to nitrogen dioxide (NO2), ozone, fine particulate matter, and black carbon was estimated at the women's residence using spatiotemporal models and averaged over the period of ovarian stimulation (2-weeks). Multivariable linear regression models were used to evaluate the associations between the air pollutants, number of mature oocytes, and metabolic feature intensities. A meet-in-the-middle approach was used to identify overlapping features and metabolic pathways.

RESULTS

Of the air pollutants, NO2 exposure had the largest number of overlapping metabolites (C18: 105; HILIC: 91) and biological pathways (C18: 3; HILIC: 6) with number of mature oocytes. Key pathways of overlap included vitamin D3 metabolism (both columns), bile acid biosynthesis (both columns), C21-steroid hormone metabolism (HILIC), androgen and estrogen metabolism (HILIC), vitamin A metabolism (HILIC), carnitine shuttle (HILIC), and prostaglandin formation (C18). Three overlapping metabolites were confirmed with level-1 or level-2 evidence. For example, hypoxanthine, a metabolite that protects against oxidant-induced cell injury, was positively associated with NO2 exposure and negatively associated with number of mature oocytes. Minimal overlap was observed between the other pollutants and the number of mature oocytes.

CONCLUSIONS

Higher exposure to NO2 during ovarian stimulation was associated with many metabolites and biologic pathways involved in endogenous vitamin metabolism, hormone synthesis, and oxidative stress that may mediate the observed associations with lower oocyte quality.

Untargeted metabolomics reveals that multiple reproductive toxicants are present at the endometrium

BACKGROUND

Recent epidemiologic research shows many environmental chemicals exhibit endocrine disrupting effects on the female reproductive system. Few studies have examined exposure at reproductive organs. Our aim was to perform a preliminary untargeted metabolomic characterization of menstrual blood, a novel biofluid, to identify environmental toxins present in the endometrium and evaluate the suitability of this sample type for exposome research.

METHODS

Whole blood menstrual samples were collected from four women using a menstrual cup. Samples were analyzed for small molecules that include both environmental chemicals and endogenous metabolites using untargeted liquid chromatography with high-resolution mass spectrometry (LC-HRMS). Principal component analysis (PCA) and ANOVA was used to identify differences within and between individuals' menstrual blood metabolomic profiles, and the influence of the sample processing method. To assess the presence of environmental exposures, LC-HRMS chemical profiles were matched to the ToxCast chemical database, which includes 4557 commonly used commercial chemicals. Select compounds were confirmed by comparison to reference standards.

RESULTS

PCA of metabolome profiles showed analysis of menstrual blood samples were highly reproducible, with high variability in detected metabolites between participants and low variability between analytical replicates of an individual's sample. Endogenous metabolites detected in menstrual blood samples achieved good coverage of the human blood metabolome. We found 1748 annotations for environmental chemicals, including suspected reproductive toxicants such as phenols, parabens, phthalates, and organochlorines. Storage temperature for the first 24 h did not significantly influence global metabolomic profiles.

CONCLUSION

Our results show chemical exposures linked to reproductive toxicity and endocrine disruption are present in menstrual blood, a sampling medium for the endometrium.

High-Throughput Production of Diverse Xenobiotic Metabolites with Cytochrome P450-Transduced Huh7 Hepatoma Cell Lines

Precision medicine and exposomics require methods to assess xenobiotic metabolism in human metabolomic analyses, including the identification of known and undocumented drug and chemical exposures as well as their metabolites. Recent work demonstrated the use of high-throughput generation of xenobiotic metabolites with human liver S-9 fractions for their detection in human plasma and urine. Here, we tested whether a panel of lentivirally transduced human hepatoma cell lines (Huh7) that express individual cytochrome P450 (P450) enzymes could be used to generate P450-specific metabolites in a high-throughput manner, while simultaneously identifying the enzymes responsible. Cell-line activities were verified using P450-specific probe substrates. To increase analytical throughput, we used a pooling strategy where 36 chemicals were grouped into 12 unique mixtures, each mixture containing 6 randomly selected compounds, and each compound being present in two separate mixtures. Each mixture was incubated with 8 different P450 cell lines for 0 and 2 hours and extracts were analyzed using liquid chromatography-high-resolution mass spectrometry. Cell lines selectively metabolized test substrates, e.g., pazopanib, bupropion, and β-naphthoflavone with expected substrate-enzyme specificities. Predicted metabolites from the remaining 33 compounds as well as many unidentified m/z features were detected. We also showed that a specific bupropion metabolite generated by CYP2B6 cells, but not detected in the S9 system, was identified in human samples. Our data show that the chemical mixtures approach accelerated characterization of xenobiotic chemical space, while simultaneously identifying enzyme sources that can be used for scalable generation of metabolites for their identification in human metabolomic analyses. SIGNIFICANCE STATEMENT: High-resolution mass spectrometry (HRMS) enables the detection of exposures to drugs and other xenobiotics in human samples, but chemical identification can be difficult for several reasons. This paper demonstrates the utility of a panel of engineered cytochrome P450-expressing hepatoma cells in a scalable workflow for production of xenobiotic metabolites, which will facilitate their use as surrogate standards to validate xenobiotic detection by HRMS in human metabolomic studies.

High-Resolution Exposomics and Metabolomics Reveals Specific Associations in Cholestatic Liver Diseases

Progress in development of prognostic and therapeutic options for the rare cholestatic liver diseases, primary sclerosing cholangitis (PSC) and primary biliary cholangitis (PBC), is hampered by limited knowledge of their pathogeneses. In particular, the potential role of hepatotoxic and/or metabolism-altering environmental chemicals in the pathogenesis of these diseases remains relatively unstudied. Moreover, the extent to which metabolic pathways are altered due to ongoing cholestasis and subsequent liver damage or possibly influenced by hepatotoxic chemicals is poorly understood. In this study, we applied a comprehensive exposomics-metabolomics approach to uncover potential pathogenic contributors to PSC and PBC. We used untargeted high-resolution mass spectrometry to characterize a wide range of exogenous chemicals and endogenous metabolites in plasma and tested them for association with disease. Exposome-wide association studies (EWAS) identified environmental chemicals, including pesticides, additives and persistent pollutants, that were associated with PSC and/or PBC, suggesting potential roles for these compounds in disease pathogenesis. Metabolome-wide association studies (MWAS) found disease-associated alterations to amino acid, eicosanoid, lipid, co-factor, nucleotide, mitochondrial and microbial metabolic pathways, many of which were shared between PSC and PBC. Notably, this analysis implicates a potential role of the 5-lipoxygenase pathway in the pathogenesis of these diseases. Finally, EWAS × MWAS network analysis uncovered linkages between environmental agents and disrupted metabolic pathways that provide insight into potential mechanisms for PSC and PBC. Conclusion: This study establishes combined exposomics-metabolomics as a generalizable approach to identify potentially pathogenic environmental agents and enumerate metabolic alterations that may impact PSC and PBC, providing a foundation for diagnostic and therapeutic strategies.