Sequencing the exposome: A call to action

Abstract

The exposome is a complement to the genome that includes non-genetic causes of disease. Multiple definitions are available, with salient points being global inclusion of exposures and behaviors, and cumulative integration of associated biologic responses. As such, the concept is both refreshingly simple and dauntingly complex. This article reviews high-resolution metabolomics (HRM) as an affordable approach to routinely analyze samples for a broad spectrum of environmental chemicals and biologic responses. HRM has been successfully used in multiple exposome research paradigms and is suitable to implement in a prototype universal exposure surveillance system. Development of such a structure for systematic monitoring of environmental exposures is an important step toward sequencing the exposome because it builds upon successes of exposure science, naturally connects external exposure to body burden and partitions the exposome into workable components. Practical results would be repositories of quantitative data on chemicals according to geography and biology. This would support new opportunities for environmental health analysis and predictive modeling. Complementary approaches to hasten development of exposome theory and associated biologic response networks could include experimental studies with model systems, analysis of archival samples from longitudinal studies with outcome data and study of relatively short-lived animals, such as household pets (dogs and cats) and non-human primates (common marmoset). International investment and cooperation to sequence the human exposome will advance scientific knowledge and also provide an important foundation to control adverse environmental exposures to sustain healthy living spaces and improve prediction and management of disease.

Keywords: Mass spectrometry; analytical chemistry; biomonitoring; environmental surveillance; metabolomics.

Fig. 1

Mass spectrometry (MS) for metabolomics. A. Mass is a fundamental characteristic of a chemical, and mass spectrometers measure mass by converting chemicals to ions in the gas phase and measuring the movement of the ions in an electromagnetic field. Mass spectrometers have an ion source to generate ions, a mass analyzer that separates ions according to mass to charge ratio (m/z) and a detector to quantify the intensity of respective ions with corresponding m/z. For metabolomics, samples are usually fractionated prior to delivery to the ion source by gas chromatography (GC) or liquid chromatography (LC). B. Many mass spectrometers measure m/z with ±0.1 atomic mass unit (AMU), which is not sufficient to distinguish chemicals with very similar mass. Consequently, these instruments require separation by GC, LC or other methods prior to mass spectral analysis. Such configurations are designated by hybrid terms: GC–MS or LC–MS. C. Tandem mass spectrometry (e.g., triple quadrupole and Q-TOF instruments) involves use of combinations of mass spectrometry components to obtain m/z measurements on an ion and then subsequent measurement of m/z for product ions generated following ion dissociation. In some instruments, this process can be repeated multiple times (MSⁿ) to gain additional structural information; for quantification, the first ion dissociation (MS/MS or MS²) is often used for targeted chemical analysis because it allows quantification of specific chemicals based upon product ions even when the precursor ion is not separated from chemicals with very similar mass. D. Ultra-high resolution accurate mass (UHRAM) mass spectrometers resolve ions and measure m/z much more precisely than other mass spectrometers. This mass resolution and mass accuracy simplifies separation requirements and provides improved capability to measure low abundance chemicals in complex matrices such as human plasma. Panels B–D were modified from with permission.

Fig. 2

High-resolution metabolomics (HRM) for advanced chemical profiling. A. The human metabolome is complex and likely to include >400,000 environmental chemicals. B. The cost for measurement of large numbers of environmental chemicals is impractical with targeted analytical methods but can be affordable if large numbers of chemicals are measured in a single analysis. C. High-resolution metabolomics uses high-resolution mass spectrometry with liquid chromatography to measure >20,000 chemicals based upon high mass resolution and high mass accuracy. D. An important advantage of high-resolution mass spectrometry is that data in profile mode contains more information than commonly used centroid mode. The centroid mode decreases the m/z window to zero, thereby being much more efficient for data storage. As this simulation shows, ions resolved by a high-resolution (High Res) mass spectrometer are not resolved by a low-resolution (low Res) instrument. When the latter is expressed in centroid mode, only 3 of 9 ions are detectable.

Fig. 3

Recent improvements in high-resolution metabolomics. A. Improved coverage of chemicals in human plasma have occurred over the past several years due to improved data extraction algorithms, improved instrumentation and improved standard operating procedures (graphic based upon figure prepared by DI Walker). B. Multiple steps have been introduced to improve consistency and data quality. C. Cross laboratory comparisons provide an effective approach to verify correct identification and evaluate quantification of metabolites. Phenylalanine (Phe) and threonine (Thr) comparisons were between HRM and amino acid measurements by the Emory Human Genetics Laboratory. Creatine and cortisol comparisons were between HRM and measurements by Metabolon. D. Quantification of environmental chemicals in human plasma by HRM. Chemical identities in human plasma were confirmed by co-elution with authentic standards and matched product ions in ion dissociation spectra. Quantification was performed in plasma from 163 healthy individuals by reference standardization with method of additions. Figures from were used with permission conveyed through Copyright Clearance Center.

Fig. 4

Data characteristics for high-resolution metabolomics. A. xMSanalyzer provides a summary of data characteristics including histograms showing the distribution of the log of ion intensity, the distribution of the median coefficient of variation for metabolites and the distribution of the percentage of missing values for the metabolites. B. A type 1 Manhattan plot is the negative log p as a function of m/z for a statistical analysis of each metabolite. This illustration is from a study to evaluate metabolites correlated with an amino acid. In the plot, ions with non-significant raw p are green, those positively associated are in red and those negatively associated are in blue. The broken lines are corresponding cutoffs for false discovery rates of 0.05 (top line) and 0.2 (bottom line). C. A type 2 Manhattan plot is the same data as in B plotted as a function of retention time. This plot is useful for separations obtained with C18 chromatography because more hydrophobic lipid-like chemicals elute at greater retention times. Thus, the retention time provides useful information concerning the properties of the chemical.

Fig. 5

High-resolution metabolomics workflow. A. An MWAS was performed for pulmonary tuberculosis (TB) patients compared to uninfected household controls. Respective broken lines, from bottom, raw p = 0.05, FDR = 0.2, FDR = 0.05. B. Selected metabolites that differ in A are plotted using box and whiskers plots. C. Two-way hierarchical cluster analysis of metabolites that differ in A shows that the metabolites separate the individuals and that the metabolites are associated into clusters. D. Use of Kyoto Encyclopedia of Genes and Genomes (KEGG) Brite Classification of metabolomics database matches give a depiction of the types of chemicals that differ between patients and controls. Data are from .

Fig. 6

High-resolution metabolomics provides an approach to detect environmental chemicals associated with disease and to discover co-exposures of chemicals. A. MWAS of age-related macular degeneration (AMD) patients and controls revealed associations of chemicals matching pentachlorochemicals with AMD . B. PLS-DA of smokers and non-smokers, classified by plasma cotinine concentrations, showed complete separation. C. MWAS of the data in B showed that 7 metabolites were correlated with cotinine, including two cotinine metabolites and a cigarette flavoring agent, methoxypyrazine. D. Network analyses using MetabNet showed that cotinine was associated with disruption of methionine metabolism, specifically associated with decreased S-methylmethionine. B–D are unpublished data from mass spectral analyses performed by DI Walker and bioinformatics analyses performed by Karan Uppal.

Fig. 7

Integration of high-resolution metabolomics with gene expression and redox proteomics analyses. A. A heatmap of associations of top correlations between transcriptome and metabolome analyses reveals strong associations with exposure to the fungicide, maneb, and the herbicide, paraquat. B. Network associations showed four strongly associated hubs of transcripts and metabolites. C. Metabolic changes occurred in pathways in which mitochondrial proteins were oxidized in response to cadmium. Data for A and B are from , and data for C are from .