Simulating Collision-Induced Dissociation Tandem Mass Spectrometry (CID-MS/MS) for the Blood Exposome Database Using Quantum Chemistry Methods - A Pilot Study.

A significant number of compounds in exposome databases and chemical inventories lack mass spectral data due to the nonavailability of reference standards. To address this limitation, computational chemistry methods can be utilized to extend mass spectral libraries for a set of chemicals. In this pilot study, we employed quantum-chemistry-based software QCxMS to generate collision-induced dissociation mass spectra for 121 compounds from the Blood Exposome Database.

Exposure to per- and poly-fluoroalkyl substances in association to later occurrence of type 2 diabetes and metabolic pathway dysregulation in a multiethnic US population.

Background: Growing evidence suggests that exposure to per- and polyfluoroalkyl substances (PFAS) are linked to an increased risk of type 2 diabetes (T2D); however, the effect of PFAS mixtures and underlying mechanisms are not well understood. We examined the associations between exposure to PFAS mixture with later T2D diagnosis and underlying metabolic dysregulations.

Methods: We conducted a nested case-control study within BioMe, an electronic health record-linked biobank of >65,000 patients seeking primary care at Mount Sinai Hospital, New York, since 2007.

Circulating per- and polyfluoroalkyl substances and liver cancer risk: a nested case-control analysis of individual participant data from 12 prospective cohorts.

Background: Per- and polyfluoroalkyl substances (PFAS) have been associated with numerous deleterious health outcomes including liver damage. However, whether exposure to PFAS is associated with liver cancer risk remains unclear.

Methods: We conducted a matched nested case-control study among 12 prospective cohort studies located in the United States.

Pregnancy as a Susceptible Period to Ambient Air Pollution Exposure on the Maternal Postpartum Metabolome.

Pregnancy is a potential critical window to air pollution exposure for long-term maternal metabolic effects. However, little is known about potential early metabolic mechanisms linking air pollution to maternal metabolic health. We included 544 pregnant Mexican women with both ambient PM levels during pregnancy and untargeted serum metabolomics to examine associations between pregnancy PM exposure (overall and monthly) and postpartum metabolites, implementing FDR-adjusted robust linear regression controlling for covariates.

IARC Workshop on the Key Characteristics of Carcinogens: Assessment of End Points for Evaluating Mechanistic Evidence of Carcinogenic Hazards.

Background: The 10 key characteristics (KCs) of carcinogens form the basis of a framework to identify, organize, and evaluate mechanistic evidence relevant to carcinogenic hazard identification. The 10 KCs are related to mechanisms by which carcinogens cause cancer. The () programme has successfully applied the KCs framework for the mechanistic evaluation of different types of exposures, including chemicals, metals, and complex exposures, such as environmental, occupational, or dietary exposures.

The need for a cancer exposome atlas: a scoping review.

Background: Despite advances in understanding genetic susceptibility to cancer, much of cancer heritability remains unidentified. At the same time, the makeup of industrial chemicals in our environment only grows more complex. This gap in knowledge on cancer risk has prompted calls to expand cancer research to the comprehensive, discovery-based study of nongenetic environmental influences, conceptualized as the "exposome.

High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage.

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling.

IDSL_MINT: a deep learning framework to predict molecular fingerprints from mass spectra.

The majority of tandem mass spectrometry (MS/MS) spectra in untargeted metabolomics and exposomics studies lack any annotation. Our deep learning framework, Integrated Data Science Laboratory for Metabolomics and Exposomics-Mass INTerpreter (IDSL_MINT) can translate MS/MS spectra into molecular fingerprint descriptors. IDSL_MINT allows users to leverage the power of the transformer model for mass spectrometry data, similar to the large language models.

IDSL.GOA: gene ontology analysis for interpreting metabolomic datasets.

Biological interpretation of metabolomic datasets often ends at a pathway analysis step to find the over-represented metabolic pathways in the list of statistically significant metabolites. However, definitions of biochemical pathways and metabolite coverage vary among different curated databases, leading to missed interpretations. For the lists of genes, transcripts and proteins, Gene Ontology (GO) terms over-presentation analysis has become a standardized approach for biological interpretation.

Sexual Dimorphism of the Mouse Plasma Metabolome Is Associated with Phenotypes of 30 Gene Knockout Lines.

Although metabolic alterations are observed in many monogenic and complex genetic disorders, the impact of most mammalian genes on cellular metabolism remains unknown. Understanding the effect of mouse gene dysfunction on metabolism can inform the functions of their human orthologues. We investigated the effect of loss-of-function mutations in 30 unique gene knockout (KO) lines on plasma metabolites, including genes coding for structural proteins (11 of 30), metabolic pathway enzymes (12 of 30) and protein kinases (7 of 30).

IDSL.CSA: Composite Spectra Analysis for Chemical Annotation of Untargeted Metabolomics Datasets.

Poor chemical annotation of high-resolution mass spectrometry data limits applications of untargeted metabolomics datasets. Our new software, the Integrated Data Science Laboratory for Metabolomics and Exposomics─Composite Spectra Analysis (IDSL.CSA) R package, generates composite mass spectra libraries from MS1-only data, enabling the chemical annotation of high-resolution mass spectrometry coupled with liquid chromatography peaks regardless of the availability of MS2 fragmentation spectra.

Serum unsaturated phosphatidylcholines predict longitudinal basal forebrain degeneration in Alzheimer's disease.

Basal forebrain cholinergic neurons are among the first cell types affected by Alzheimer's disease pathology, but the cause of their early vulnerability is unknown. The lipid phosphatidylcholine is an essential component of the cell membrane, and phosphatidylcholine levels have been shown to be abnormal in the blood and brain of Alzheimer's disease patients. We hypothesized that disease-related changes in phosphatidylcholine metabolism may disproportionately affect basal forebrain cholinergic neurons due to their extremely large size, plasticity in adulthood and unique reliance on phosphatidylcholine for acetylcholine synthesis.

IDSL.GOA: Gene Ontology Analysis for Interpreting Metabolomic datasets.

Biological interpretation of metabolomic datasets often ends at a pathway analysis step to find the over-represented metabolic pathways in the list of statistically significant metabolites. However, definitions of biochemical pathways and metabolite coverage vary among different curated databases, leading to missed interpretations. For the lists of genes, transcripts and proteins, Gene Ontology (GO) terms over-presentation analysis has become a standardized approach for biological interpretation.

IDSL.CSA: Composite Spectra Analysis for Chemical Annotation of Untargeted Metabolomics Datasets.

Poor chemical annotation of high-resolution mass spectrometry data limit applications of untargeted metabolomics datasets. Our new software, the Integrated Data Science Laboratory for Metabolomics and Exposomics - Composite Spectra Analysis (IDSL.CSA) R package, generates composite mass spectra libraries from MS1-only data, enabling the chemical annotation of LC/HRMS peaks regardless of the availability of MS2 fragmentation spectra.

In utero metabolomic signatures of refined grain intake and risk of gestational diabetes: A metabolome-wide association study.

Background: Epidemiologic evidence has linked refined grain intake to a higher risk of gestational diabetes (GDM), but the biological underpinnings remain unclear.

Objectives: We aimed to identify and validate refined grain-related metabolomic biomarkers for GDM risk.

Methods: In a metabolome-wide association study of 91 cases with GDM and 180 matched controls without GDM (discovery set) nested in the prospective Pregnancy Environment and Lifestyle Study (PETALS), refined grain intake during preconception and early pregnancy and serum untargeted metabolomics were assessed at gestational weeks 10-13.

An actionable annotation scoring framework for gas chromatography-high-resolution mass spectrometry.

Omics-based technologies have enabled comprehensive characterization of our exposure to environmental chemicals (chemical exposome) as well as assessment of the corresponding biological responses at the molecular level (eg, metabolome, lipidome, proteome, and genome). By systematically measuring personal exposures and linking these stimuli to biological perturbations, researchers can determine specific chemical exposures of concern, identify mechanisms and biomarkers of toxicity, and design interventions to reduce exposures. However, further advancement of metabolomics and exposomics approaches is limited by a lack of standardization and approaches for assigning confidence to chemical annotations.

IDSL.UFA Assigns High-Confidence Molecular Formula Annotations for Untargeted LC/HRMS Data Sets in Metabolomics and Exposomics.

Untargeted liquid chromatography/high-resolution mass spectrometry (LC/HRMS) assays in metabolomics and exposomics aim to characterize the small molecule chemical space in a biospecimen. To gain maximum biological insights from these data sets, LC/HRMS peaks should be annotated with chemical and functional information including molecular formula, structure, chemical class, and metabolic pathways. Among these, molecular formulas may be assigned to LC/HRMS peaks through matching theoretical and observed isotopic profiles (MS1) of the underlying ionized compound.

Metabolomic Evidence for Peroxisomal Dysfunction in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic and debilitating disease characterized by unexplained physical fatigue, cognitive and sensory dysfunction, sleeping disturbances, orthostatic intolerance, and gastrointestinal problems. People with ME/CFS often report a prodrome consistent with infections. Using regression, Bayesian and enrichment analyses, we conducted targeted and untargeted metabolomic analysis of plasma from 106 ME/CFS cases and 91 frequency-matched healthy controls.

IDSL.IPA Characterizes the Organic Chemical Space in Untargeted LC/HRMS Data Sets.

Generating comprehensive and high-fidelity metabolomics data matrices from LC/HRMS data remains to be extremely challenging for population-scale large studies ( > 200). Here, we present a new data processing pipeline, the Intrinsic Peak Analysis (IDSL.IPA) R package (https://ipa.

Predictive Metabolomic Markers in Early to Mid-pregnancy for Gestational Diabetes Mellitus: A Prospective Test and Validation Study.

Gestational diabetes mellitus (GDM) predisposes pregnant individuals to perinatal complications and long-term diabetes and cardiovascular diseases. We developed and validated metabolomic markers for GDM in a prospective test-validation study. In a case-control sample within the PETALS cohort (GDM n = 91 and non-GDM n = 180; discovery set), a random PETALS subsample (GDM n = 42 and non-GDM n = 372; validation set 1), and a case-control sample within the GLOW trial (GDM n = 35 and non-GDM n = 70; validation set 2), fasting serum untargeted metabolomics were measured by gas chromatography/time-of-flight mass spectrometry.