Expanding Coverage of the Chemical Exposome Using Novel Data Acquisition and Computational Tools

The chemical exposome encompasses the sum of all exposures during an individual’s lifetime. This group of compounds, when combined with genetic traits, determine chronic disease, thus, to fully understand human health issues, it is imperative to improve exposome measurements to match genomic technologies. My dissertation focuses on expanding the coverage of the chemical exposome and developing tools and techniques that will increase the reliability of untargeted mass spectrometry based studies. In Chapter 1, I address the many challenges faced when trying to measure all compounds contained within the chemical exposome. Exposome compounds cover a large range of biological concentration, many different structural classes, and are extensively modified in the body via detoxification pathways. I explore the potential strategies researchers can employ to address these difficulties, from instrumental acquisition and sample preparation, to compound identification and other data processing tools. In Chapter 2, I discuss the utilization of hydrogen-deuterium exchange (HDX) for identifying unknown compounds in metabolomics and exposomics studies. HDX is a method by which all of the acidic protons in a compound are exchanged with deuterium prior to mass spectrometric analysis with the goal of using the resulting mass shift to illuminate potential substructures. In this work, we compared the efficacy of different deuterium incorporation methods, explored the filtering potential of this method on 253 test compounds and large chemical databases, and identified 101 compounds in mouse mammary tumors. The results of this study show that HDX can alleviate researchers’ reliance on mass spectral databases for identifying compounds in biological samples. Chapter 3 focuses on an untargeted exposomics method that was developed to measure chemical exposures that influence female reproductive health. In the human body, xenobiotic compounds are transformed through metabolism to increase polarity and allow for excretion in the urine. These transformations commonly consist of phase I hydroxylation reactions mediated by CYP450 enzymes and phase II conjugations mediated by transferases. In the urine, the most common conjugate forms are glucuronides and sulfates. Our urinary exposomics method takes advantage of this knowledge. By using B-glucuronidase/aryl sulfatase, we can cleave the phase II conjugates into their phase I forms, and easily extract these less polar analytes from the polar urinary matrix. This method was used to measure exposure compounds in 50 women from Orange County, CA at three different time points. The compound measurements were then used to build linear mixed effects models to predict the chemical variables that influence hormonally derived endocrine endpoints. Models were built for menstrual cycle length, cycle peak luteinizing hormone, follicular estrone-1,3-glucuronide, and estrone-1,3-glucuronide slope. Chemical variables that had not previously been associated with reproductive function were identified for each of these endocrine endpoints. These models illuminate novel chemical exposures for future causative studies on reproductive health

Identifying Toxicologically Significant Compounds in Urban Wildfire Ash Using In Vitro Bioassays and High-Resolution Mass Spectrometry.

Urban wildfires may generate numerous unidentified chemicals of toxicity concern. Ash samples were collected from burned residences and from an undeveloped upwind reference site, following the Tubbs fire in Sonoma County, California. The solvent extracts of ash samples were analyzed using GC- and LC-high-resolution mass spectrometry (HRMS) and using a suite of in vitro bioassays for their bioactivity toward nuclear receptors [aryl hydrocarbon receptor (AhR), estrogen receptor (ER), and androgen receptor (AR)], their influence on the expression of genetic markers of stress and inflammation [interleukin-8 (IL-8) and cyclooxygenase-2 (COX-2)], and xenobiotic metabolism [cytochrome P4501A1 (CYP1A1)]. Genetic markers (CYP1A1, IL-8, and COX-2) and AhR activity were significantly higher with wildfire samples than in solvent controls, whereas AR and ER activities generally were unaffected or reduced. The bioassay responses of samples from residential areas were not significantly different from the samples from the reference site despite differing chemical compositions. Suspect and nontarget screening was conducted to identify the chemicals responsible for elevated bioactivity using the multiple streams of HRMS data and open-source data analysis workflows. For the bioassay endpoint with the largest available database of pure compound results (AhR), nontarget features statistically related to whole sample bioassay response using Spearman's rank-order correlation coefficients or elastic net regression were significantly more likely (by 10 and 15 times, respectively) to be known AhR agonists than the overall population of compounds tentatively identified by nontarget analysis. The findings suggest that a combination of nontarget analysis, in vitro bioassays, and statistical analysis can identify bioactive compounds in complex mixtures

A Comprehensive Plasma Metabolomics Dataset for a Cohort of Mouse Knockouts within the International Mouse Phenotyping Consortium

Mouse knockouts facilitate the study ofgene functions. Often, multiple abnormal phenotypes are induced when a gene is inactivated. The International Mouse Phenotyping Consortium (IMPC) has generated thousands of mouse knockouts and catalogued their phenotype data. We have acquired metabolomics data from 220 plasma samples from 30 unique mouse gene knockouts and corresponding wildtype mice from the IMPC. To acquire comprehensive metabolomics data, we have used liquid chromatography (LC) combined with mass spectrometry (MS) for detecting polar and lipophilic compounds in an untargeted approach. We have also used targeted methods to measure bile acids, steroids and oxylipins. In addition, we have used gas chromatography GC-TOFMS for measuring primary metabolites. The metabolomics dataset reports 832 unique structurally identified metabolites from 124 chemical classes as determined by ChemRICH software. The GCMS and LCMS raw data files, intermediate and finalized data matrices, R-Scripts, annotation databases, and extracted ion chromatograms are provided in this data descriptor. The dataset can be used for subsequent studies to link genetic variants with molecular mechanisms and phenotypes