Pubertal Girls With Overweight/Obesity Have Higher Androgen Levels-Can Metabolomics Tell us Why?

CONTEXT: Pubertal girls with higher total body fat (TBF) demonstrate higher androgen levels. The cause of this association is unknown but is hypothesized to relate to insulin resistance. OBJECTIVE: This work aimed to investigate the association between higher TBF and higher androgens in pubertal girls using untargeted metabolomics. METHODS: Serum androgens were determined using a quantitative mass spectrometry (MS)-based assay. Metabolomic samples were analyzed using liquid chromatography high-resolution MS. Associations between TBF or body mass index (BMI) z score (exposure) and metabolomic features (outcome) and between metabolomic features (exposure) and serum hormones (outcome) were examined using gaussian generalized estimating equation models with the outcome lagged by one study visit. Benjamini-Hochberg false discovery rate (FDR) adjusted P values were calculated to account for multiple testing. RaMP-DB (relational database of metabolomic pathways) was used to conduct enriched pathway analyses among features nominally associated with body composition or hormones. RESULTS: Sixty-six pubertal, premenarchal girls (aged 10.9 ± 1.39 SD years; 60% White, 24% Black, 16% other; 63% normal weight, 37% overweight/obese) contributed an average of 2.29 blood samples. BMI and TBF were negatively associated with most features including raffinose (a plant trisaccharide) and several bile acids. For BMI, RaMP-DB identified many enriched pathways related to bile acids. Androstenedione also showed strong negative associations with raffinose and bile acids. CONCLUSION: Metabolomic analyses of samples from pubertal girls did not identify an insulin resistance signature to explain the association between higher TBF and androgens. Instead, we identified potential novel signaling pathways that may involve raffinose or bile acid action at the adrenal gland.

Profiling whole-tissue metabolic reprogramming during cutaneous poxvirus infection and clearance.

IMPORTANCE: Human poxvirus infections have caused significant public health burdens both historically and recently during the unprecedented global Mpox virus outbreak. Although vaccinia virus (VACV) infection of mice is a commonly used model to explore the anti-poxvirus immune response, little is known about the metabolic changes that occur in vivo during infection. We hypothesized that the metabolome of VACV-infected skin would reflect the increased energetic requirements of both virus-infected cells and immune cells recruited to sites of infection. Therefore, we profiled whole VACV-infected skin using untargeted mass spectrometry to define the metabolome during infection, complementing these experiments with flow cytometry and transcriptomics. We identified specific metabolites, including nucleotides, itaconic acid, and glutamine, that were differentially expressed during VACV infection. Together, this study offers insight into both virus-specific and immune-mediated metabolic pathways that could contribute to the clearance of cutaneous poxvirus infection.

Investigating sensitization activity of azobenzene disperse dyes via the Direct Peptide Reactivity Assay (DPRA).

Azobenzene disperse dyes are the fastest-growing category of commercial dyestuffs and have been found in indoor house dust and in children's polyester apparel. Azobenzene disperse dyes are implicated as potentially allergenic; however, little experimental data is available on allergenicity of these dyes. Here, we examine the binding of azobenzene disperse dyes to nucleophilic peptide residues as a proxy for their potential reactivity as electrophilic allergenic sensitizers. The Direct Peptide Reactivity Assay (DPRA) was utilized via both a spectrophotometric method and a high-performance liquid chromatography (HPLC) method. We tested dyes purified from commercial dyestuffs as well as several known transformation products. All dyes were found to react with nucleophilic peptides in a dose-dependent manner with pseudo-first order kinetics (rate constants as high as 0.04 h-1). Rates of binding reactivity were also found to correlate to electrophilic properties of dyes as measured by Hammett constants and electrophilicity indices. Reactivities of polyester shirt extracts were also tested for DPRA activity and the shirt extracts with high measured abundances of azobenzene disperse dyes were observed to induce greater peptide reactivity. Results suggest that azobenzene disperse dyes may function as immune sensitizers, and that clothing containing these dyes may pose risks for skin sensitization.

Characterizing azobenzene disperse dyes and related compounds in house dust and their correlations with other organic contaminant classes.

Azobenzene disperse dyes are the fastest-growing category of commercial dyestuffs and are implicated in the literature as potentially allergenic. In the indoor environment, these dyes may be shed from various textiles, including clothing and upholstery and accumulate in dust particles potentially leading to exposure in young children who have higher exposure to chemicals associated with dust due to their crawling and mouthing behaviors. Children may be more vulnerable to dye exposure due to their developing immune systems, and therefore, it is critical to characterize azobenzene disperse dyes in children's home environments. Here, we investigate azobenzene disperse dyes and related compounds in house dust samples (n = 124) that were previously analyzed for flame retardants, phthalates, pesticides and per- and polyfluoroalkyl substances (PFAS). High-resolution mass spectrometry was used to support both targeted and suspect screening of dyes in dust. Statistical analyses were conducted to determine if dye concentrations were related to demographic information. Detection frequencies for 12 target dyes ranged from 11% to 89%; of the dyes that were detected in at least 50% of the samples, geometric mean levels ranged from 32.4 to 360 ng/g. Suspect screening analysis identified eight additional high-abundance azobenzene compounds in dust. Some dyes were correlated to numerous flame retardants and several antimicrobials, and statistically higher levels of some dyes were observed in homes of non-Hispanic Black mothers than in homes of non-Hispanic white mothers. To our knowledge, this is the most comprehensive study of azobenzene disperse dyes in house dust to date. Future studies are needed to quantify additional dyes in dust and to examine exposure pathways of dyes in indoor environments where children are concerned.

Loss of CD4+ T cell-intrinsic arginase 1 accelerates Th1 response kinetics and reduces lung pathology during influenza infection.

Arginase 1 (Arg1), the enzyme catalyzing the conversion of arginine to ornithine, is a hallmark of IL-10-producing immunoregulatory M2 macrophages. However, its expression in T cells is disputed. Here, we demonstrate that induction of Arg1 expression is a key feature of lung CD4+ T cells during mouse in vivo influenza infection. Conditional ablation of Arg1 in CD4+ T cells accelerated both virus-specific T helper 1 (Th1) effector responses and its resolution, resulting in efficient viral clearance and reduced lung pathology. Using unbiased transcriptomics and metabolomics, we found that Arg1-deficiency was distinct from Arg2-deficiency and caused altered glutamine metabolism. Rebalancing this perturbed glutamine flux normalized the cellular Th1 response. CD4+ T cells from rare ARG1-deficient patients or CRISPR-Cas9-mediated ARG1-deletion in healthy donor cells phenocopied the murine cellular phenotype. Collectively, CD4+ T cell-intrinsic Arg1 functions as an unexpected rheostat regulating the kinetics of the mammalian Th1 lifecycle with implications for Th1-associated tissue pathologies.

Signal Response Evaluation Applied to Untargeted Mass Spectrometry Data to Improve Data Interpretability.

Feature finding is a common way to process untargeted mass spectrometry (MS) data to obtain a list of chemicals present in a sample. Most feature finding algorithms naïvely search for patterns of unique descriptors (e.g., m/z, retention time, and mobility) and provide a list of unannotated features. There is a need for solutions in processing untargeted MS data, independent of chemical or origin, to assess features based on measurement quality with the aim of improving interpretation. Here, we report the signal response evaluation as a method by which to assess the individual features observed in untargeted MS data. The basis of this method is the ubiquitous relationship between the amount and response in all MS measurements. Three different metrics with user-defined parameters can be used to assess the monotonic or linear relationship of each feature in a dilution series or multiple injection volumes. We demonstrate this approach in metabolomics data obtained from a uniform biological matrix (NIST SRM 1950) and a variable biological matrix (murine kidney tissue). The code is provided to facilitate implementation of this data processing method.

STopTox: An in Silico Alternative to Animal Testing for Acute Systemic and Topical Toxicity.

BACKGROUND: Modern chemical toxicology is facing a growing need to Reduce, Refine, and Replace animal tests (Russell 1959) for hazard identification. The most common type of animal assays for acute toxicity assessment of chemicals used as pesticides, pharmaceuticals, or in cosmetic products is known as a "6-pack" battery of tests, including three topical (skin sensitization, skin irritation and corrosion, and eye irritation and corrosion) and three systemic (acute oral toxicity, acute inhalation toxicity, and acute dermal toxicity) end points. METHODS: We compiled, curated, and integrated, to the best of our knowledge, the largest publicly available data sets and developed an ensemble of quantitative structure-activity relationship (QSAR) models for all six end points. All models were validated according to the Organisation for Economic Co-operation and Development (OECD) QSAR principles, using data on compounds not included in the training sets. RESULTS: In addition to high internal accuracy assessed by cross-validation, all models demonstrated an external correct classification rate ranging from 70% to 77%. We established a publicly accessible Systemic and Topical chemical Toxicity (STopTox) web portal (https://stoptox.mml.unc.edu/) integrating all developed models for 6-pack assays. CONCLUSIONS: We developed STopTox, a comprehensive collection of computational models that can be used as an alternative to in vivo 6-pack tests for predicting the toxicity hazard of small organic molecules. Models were established following the best practices for the development and validation of QSAR models. Scientists and regulators can use the STopTox portal to identify putative toxicants or nontoxicants in chemical libraries of interest. https://doi.org/10.1289/EHP9341.

Characterizing azobenzene disperse dyes in commercial mixtures and children's polyester clothing.

Azobenzene disperse dyes are the fastest-growing class of dyestuffs, yet little is known about dye occurrences, sources, and transformations; azo dyes are also underrepresented in chemical standard catalogs, molecular databases, and mass spectral libraries. Many azo dyes are known to have sensitization, mutagenic, and carcinogenic properties. To fill these knowledge gaps, azo dyes were purified from dyestuffs by Soxhlet extraction and flash chromatography and characterized using ultra-high-performance liquid chromatography (UHPLC) coupled to a high resolution Orbitrap Fusion Lumos mass spectrometer operated in positive electrospray ionization mode, as well as by 1H and 13C NMR. Data were analyzed to identify likely chemical formulas and structures using a weight-of-evidence approach with multiple open-source, in silico computational mass spectrometry tools. Nineteen total azobenzene dyes were detected in dyestuffs via a non-targeted analysis approach; the azobenzene dyes Disperse Blue 79:1, Disperse Blue 183:1, Disperse Orange 44, Disperse Orange 73, Disperse Red 50, Disperse Red 73, and Disperse Red 354 were purified from raw dyestuffs. Samples of children's polyester clothing were then analyzed likewise. In clothing, 21 azobenzene disperse dyes were detected, 12 of which were confirmed and quantified via reference standards. Individual dyes in apparel were quantified at concentrations up to 9230 µg dye/g shirt, with geometric means ranging 7.91-300 µg dye/g shirt. Total dye load in apparel was quantified at up to 11,430 µg dye/g shirt. This research supported the development of reference standards and library mass spectra for azobenzene disperse dyes previously absent from standard and spectral libraries. By analyzing the scope and quantities of azo dyes in children's polyester apparel, this study will facilitate a more robust understanding of sources of these potentially allergenic and mutagenic compounds.

Assessment of emerging polar organic pollutants linked to contaminant pathways within an urban estuary using non-targeted analysis.

A comprehensive, non-targeted analysis of polar organic pollutants using high resolution/accurate mass (HR/AM) mass spectrometry approaches has been applied to water samples from San Francisco (SF) Bay, a major urban estuary on the western coast of the United States, to assess occurrence of emerging contaminants and inform future monitoring and management activities. Polar Organic Chemical Integrative Samplers (POCIS) were deployed selectively to evaluate the influence of three contaminant pathways: urban stormwater runoff (San Leandro Bay), wastewater effluent (Coyote Creek, Lower South Bay), and agricultural runoff (Napa River). Grab samples were collected before and after deployment of the passive samplers to provide a quantitative snapshot of contaminants for comparison. Composite samples of wastewater effluent (24 hours) were also collected from several wastewater dischargers. Samples were analyzed using liquid-chromatography coupled to high resolution mass spectrometry. Resulting data were analyzed using a customized workflow designed for high-fidelity detection, prioritization, identification, and semi-quantitation of detected molecular features. Approximately 6350 compounds were detected in the combined data set, with 424 of those compounds tentatively identified through high quality spectral library match scores. Compounds identified included ethoxylated surfactants, pesticide and pharmaceutical transformation products, polymer additives, and rubber vulcanization agents. Compounds identified in samples were reflective of the apparent sources and pathways of organic pollutant inputs, with stormwater-influenced samples dominated by additive chemicals likely derived from plastics and vehicle tires, as well as ethoxylated surfactants.