Observing the human exposome as reflected in breath biomarkers: heat map data interpretation for environmental and intelligence research

Moderate body lipid accumulation in mice attenuated benzene-induced hematotoxicity via acceleration of benzene metabolism and clearance

Recent population and animal studies have revealed a correlation between fat content and the severity of benzene-induced hematologic toxicity. However, the precise impact of lipid deposition on benzene-induced hematotoxicity and the underlying mechanisms remain unclear. In this study, we established a mouse model with moderate lipid accumulation by subjecting the mice to an 8-week high-fat diet (45% kcal from fat, HFD), followed by 28-day inhalation of benzene at doses of 0, 1, 10, and 100 ppm. The results showed that benzene exposure caused a dose-dependent reduction of peripheral white blood cell (WBC) counts in both diet groups. Notably, this reduction was less pronounced in the HFD-fed mice, suggesting that moderate lipid accumulation mitigates benzene-related hematotoxicity. To investigate the molecular basis for this effect, we performed bioinformatics analysis of high-throughput transcriptome sequencing data, which revealed that moderate lipid deposition alters mouse metabolism and stress tolerance towards xenobiotics. Consistently, the expression of key metabolic enzymes, such as Cyp2e1 and Gsta1, were upregulated in the HFD-fed mice upon benzene exposure. Furthermore, we utilized a real-time exhaled breath detection technique to monitor exhaled benzene metabolites, and the results indicated that moderate lipid deposition enhanced metabolic activation and increased the elimination of benzene metabolites. Collectively, these findings demonstrate that moderate lipid deposition confers reduced susceptibility to benzene-induced hematotoxicity in mice, at least in part, by accelerating benzene metabolism and clearance.

Hyphenated Mass Spectrometry versus Real-Time Mass Spectrometry Techniques for the Detection of Volatile Compounds from the Human Body

Mass spectrometry (MS) is an analytical technique that can be used for various applications in a number of scientific areas including environmental, security, forensic science, space exploration, agri-food, and numerous others. MS is also continuing to offer new insights into the proteomic and metabolomic fields. MS techniques are frequently used for the analysis of volatile compounds (VCs). The detection of VCs from human samples has the potential to aid in the diagnosis of diseases, in monitoring drug metabolites, and in providing insight into metabolic processes. The broad usage of MS has resulted in numerous variations of the technique being developed over the years, which can be divided into hyphenated and real-time MS techniques. Hyphenated chromatographic techniques coupled with MS offer unparalleled qualitative analysis and high accuracy and sensitivity, even when analysing complex matrices (breath, urine, stool, etc.). However, these benefits are traded for a significantly longer analysis time and a greater need for sample preparation and method development. On the other hand, real-time MS techniques offer highly sensitive quantitative data. Additionally, real-time techniques can provide results in a matter of minutes or even seconds, without altering the sample in any way. However, real-time MS can only offer tentative qualitative data and suffers from molecular weight overlap in complex matrices. This review compares hyphenated and real-time MS methods and provides examples of applications for each technique for the detection of VCs from humans.

A literature survey of all volatiles from healthy human breath and bodily fluids: the human volatilome

This paper comprises an updated version of the 2014 review which reported 1846 volatile organic compounds (VOCs) identified from healthy humans. In total over 900 additional VOCs have been reported since the 2014 review and the VOCs from semen have been added. The numbers of VOCs found in breath and the other bodily fluids are: blood 379, breath 1488, faeces 443, milk 290, saliva 549, semen 196, skin 623 and urine 444. Compounds were assigned CAS registry numbers and named according to a common convention where possible. The compounds have been included in a single table with the source reference(s) for each VOC, an update on our 2014 paper. VOCs have also been grouped into tables according to their chemical class or functionality to permit easy comparison. Careful use of the database is needed, as a number of the identified VOCs only have level 2-putative assignment, and only a small fraction of the reported VOCs have been validated by standards. Some clear differences are observed, for instance, a lack of esters in urine with a high number in faeces and breath. However, the lack of compounds from matrices such a semen and milk compared to breath for example could be due to the techniques used or reflect the intensity of effort e.g. there are few publications on VOCs from milk and semen compared to a large number for breath. The large number of volatiles reported from skin is partly due to the methodologies used, e.g. by collecting skin sebum (with dissolved VOCs and semi VOCs) onto glass beads or cotton pads and then heating to a high temperature to desorb VOCs. All compounds have been included as reported (unless there was a clear discrepancy between name and chemical structure), but there may be some mistaken assignations arising from the original publications, particularly for isomers. It is the authors' intention that this work will not only be a useful database of VOCs listed in the literature but will stimulate further study of VOCs from healthy individuals; for example more work is required to confirm the identification of these VOCs adhering to the principles outlined in the metabolomics standards initiative. Establishing a list of volatiles emanating from healthy individuals and increased understanding of VOC metabolic pathways is an important step for differentiating between diseases using VOCs.

Positive matrix factorization: A data preprocessing strategy for direct mass spectrometry-based breath analysis

Interest in exhaled breath has grown considerably in recent years, as breath biosampling has shown promise for non-invasive disease diagnosis, therapeutic drug monitoring, and environmental exposure. Real time breath analysis can be accomplished via direct online mass spectrometry (MS)-based methods, which can provide more accurate and detailed data and an enhanced understanding of the temporal evolution of exhaled VOCs in the breath; however, the complicated chemical composition and large raw datasets involved in breath analysis have hindered the discovery of sources contributing to the exhaled VOCs. The positive matrix factorization (PMF) receptor model has been widely used for source apportionment in atmospheric studies. Since the exhaled VOCs contain compounds from various sources, such as alveolar air, mouth air and respiratory dead-space air, PMF may be also helpful for source apportionment of exhaled VOCs in the breath. Thus, this study explores the application of PMF in the pretreatment of direct breath measurement data. The results indicate that (i) endogenous compounds and background contaminants sources can be readily distinguished by PMF in data obtained from replicate measurements of human exhaled breath at single time points (~30 s/measurement), which may benefit both exhalome investigations and the identification of exposure biomarkers; (ii) sources resolved from online measurement data collected over longer periods (1.5 h) can be used to isolate the evolution of exhaled VOCs and investigate processes such as the pharmacokinetics of ketamine and its major metabolites. Therefore, PMF has shown promise for both data processing and subsequent data mining for the ambient MS-based breath analysis.

Human biomarker interpretation: the importance of intra-class correlation coefficients (ICC) and their calculations based on mixed models, ANOVA, and variance estimates

Human biomonitoring is the foundation of environmental toxicology, community public health evaluation, preclinical health effects assessments, pharmacological drug development and testing, and medical diagnostics. Within this framework, the intra-class correlation coefficient (ICC) serves as an important tool for gaining insight into human variability and responses and for developing risk-based assessments in the face of sparse or highly complex measurement data. The analytical procedures that provide data for clinical and public health efforts are continually evolving to expand our knowledge base of the many thousands of environmental and biomarker chemicals that define human systems biology. These chemicals range from the smallest molecules from energy metabolism (i.e., the metabolome), through larger molecules including enzymes, proteins, RNA, DNA, and adducts. In additiona, the human body contains exogenous environmental chemicals and contributions from the microbiome from gastrointestinal, pulmonary, urogenital, naso-pharyngeal, and skin sources. This complex mixture of biomarker chemicals from environmental, human, and microbiotic sources comprise the human exposome and generally accessed through sampling of blood, breath, and urine. One of the most difficult problems in biomarker assessment is assigning probative value to any given set of measurements as there are generally insufficient data to distinguish among sources of chemicals such as environmental, microbiotic, or human metabolism and also deciding which measurements are remarkable from those that are within normal human variability. The implementation of longitudinal (repeat) measurement strategies has provided new statistical approaches for interpreting such complexities, and use of descriptive statistics based upon intra-class correlation coefficients (ICC) has become a powerful tool in these efforts. This review has two parts; the first focuses on the history of repeat measures of human biomarkers starting with occupational toxicology of the early 1950s through modern applications in interpretation of the human exposome and metabolic adverse outcome pathways (AOPs). The second part reviews different methods for calculating the ICC and explores the strategies and applications in light of different data structures.

Investigation on the influence of time-of-day on benzene metabolic pharmacokinetics by direct breath analysis in mice

Benzene, well known as a ubiquitous environmental pollutant, can lead to increasing risk of cancer, bone marrow failure as well as other serious diseases. Benzene has been classified as carcinogenic to humans with no recommended safe level of exposure. In this study, the influence of time-of-day on benzene metabolism has been tentatively explored in a mouse model based on direct real-time breath analysis by using membrane inlet single photon ionization time of flight mass spectrometry (MI-SPI-TOFMS). The exhaled breath of eight mice was monitored at a time resolution of less than 20 s after intraperitoneal (I.P.) injection of benzene in the morning, afternoon and evening on different days, and two rounds of experiments were carried out in total. The pharmacokinetic parameters such as total exposure AUC_0-∞ (h ng/mL), peak level C_max (ng/mL), time of peak level t_max (h), and terminal half-life t_1/2z (h) were calculated and discussed. The values of individual parameter varied greatly among the eight mice, e.g., AUC_0-∞ in the morning of the first round of experiment ranged from 10.66 to 162.17 h ng/mL and the mean ± SD was 103.72 ± 99.72 h ng/mL (n = 8). Significant difference has also been observed between two rounds of experiments, implying the damage in the liver caused by the benzene exposure. However, there is no significant difference among the results from the morning, afternoon and evening for each round of the experiment. In our follow-up study, the influence of time-of-day will be further investigated, in which the metabolites of benzene as well as endogenous metabolites will be considered.

Direct human breath analysis by secondary nano-electrospray ionization ultrahigh-resolution mass spectrometry: Importance of high mass resolution and mass accuracy

RATIONALE

Direct mass spectrometry (MS)-based methods make it possible to monitor the molecular compositions of hundreds of volatile organic compounds (VOCs) in exhaled human breath in real time. Mass resolution and mass accuracy play important roles for direct MS analysis, especially for the low-concentration isobaric compounds in non-target research.

METHODS

Direct detection of VOCs in exhaled breath of four healthy subjects (3 males and 1 female aged between 25 to 35 years old) has been performed by using secondary nano-electrospray ionization mass spectrometry (Sec-nanoESI-UHRMS) at resolutions (R) of 15,000, 30,000, 60,000 and 120,000.

RESULTS

For some low-intensity isobaric ions, they could be distinguished only when R ≥ 60,000, e.g., signals at m/z 96.9591 (sulfate/sulfuric acid), m/z 96.9687 (phosphate/phosphoric acid) and m/z 96.9756 ([C₄ H₂ O₇ S]^- ), m/z 234.1161 ([C₁₀ H₂₀ O₃ NS]⁺ ) and m/z 234.1338 ([C₁₀ H₂₀ O₅ N]⁺ ), m/z 119.0686 (isotope of indole) and m/z 119.0705 (an interfering signal), respectively. At R 120,000, the mass errors were obtained from a set of reference ions, and the values were ≤0.6 mDa for ions detected in positive detection mode and in the range of -1.0-1.1 mDa for the negative mode. These mass errors were used to exclusively identify unknown compounds detected in the breath samples. By utilizing the present setup, besides the normal VOCs reported previously, we detected non-volatile species (sulfate/sulfuric acid, silicate/silicic acid, phosphate/phosphoric acid and nitrate/nitric acid), dichlorobenzene and an ammonium adduct ([(C₂ H₆ SiO)₆  + NH₄ ]⁺ ), which were ascribed to exhaled particles, indoor air pollution and an endogenous source, respectively.

CONCLUSIONS

For direct breath analysis, high mass resolution of ≥60,000 and mass errors of 1.0 mDa (absolute value) covering the mass range of interests (e.g., m/z 50-500) are necessary for the exploration and accurate identification of low-intensity unknown isobaric compounds in non-target research. Copyright © 2017 John Wiley & Sons, Ltd.

Estimating central tendency from a single spot measure: A closed-form solution for lognormally distributed biomarker data for risk assessment at the individual level

Exposure-based risk assessment employs large cross-sectional data sets of environmental and biomarker measurements to predict population statistics for adverse health outcomes. The underlying assumption is that long-term (many years) latency health problems including cancer, autoimmune and cardiovascular disease, diabetes, and asthma are triggered by lifetime exposures to environmental stressors that interact with the genome. The aim of this study was to develop a specific predictive method that provides the statistical parameters for chronic exposure at the individual level based upon a single spot measurement and knowledge of global summary statistics as derived from large data sets. This is a profound shift in exposure and health statistics in that it begins to answer the question "How large is my personal risk?" rather than just providing an overall population-based estimate. This approach also holds value for interpreting exposure-based risks for small groups of individuals within a community in comparison to random individuals from the general population.

Systemic exposure to PAHs and benzene in firefighters suppressing controlled structure fires

Turnout gear provides protection against dermal exposure to contaminants during firefighting; however, the level of protection is unknown. We explored the dermal contribution to the systemic dose of polycyclic aromatic hydrocarbons (PAHs) and other aromatic hydrocarbons in firefighters during suppression and overhaul of controlled structure burns. The study was organized into two rounds, three controlled burns per round, and five firefighters per burn. The firefighters wore new or laundered turnout gear tested before each burn to ensure lack of PAH contamination. To ensure that any increase in systemic PAH levels after the burn was the result of dermal rather than inhalation exposure, the firefighters did not remove their self-contained breathing apparatus until overhaul was completed and they were >30 m upwind from the burn structure. Specimens were collected before and at intervals after the burn for biomarker analysis. Urine was analyzed for phenanthrene equivalents using enzyme-linked immunosorbent assay and a benzene metabolite (s-phenylmercapturic acid) using liquid chromatography/tandem mass spectrometry; both were adjusted by creatinine. Exhaled breath collected on thermal desorption tubes was analyzed for PAHs and other aromatic hydrocarbons using gas chromatography/mass spectrometry. We collected personal air samples during the burn and skin wipe samples (corn oil medium) on several body sites before and after the burn. The air and wipe samples were analyzed for PAHs using a liquid chromatography with photodiode array detection. We explored possible changes in external exposures or biomarkers over time and the relationships between these variables using non-parametric sign tests and Spearman tests, respectively. We found significantly elevated (P < 0.05) post-exposure breath concentrations of benzene compared with pre-exposure concentrations for both rounds. We also found significantly elevated post-exposure levels of PAHs on the neck compared with pre-exposure levels for round 1. We found statistically significant positive correlations between external exposures (i.e. personal air concentrations of PAHs) and biomarkers (i.e. change in urinary PAH metabolite levels in round 1 and change in breath concentrations of benzene in round 2). The results suggest that firefighters wearing full protective ensembles absorbed combustion products into their bodies. The PAHs most likely entered firefighters' bodies through their skin, with the neck being the primary site of exposure and absorption due to the lower level of dermal protection afforded by hoods. Aromatic hydrocarbons could have been absorbed dermally during firefighting or inhaled during the doffing of gear that was off-gassing contaminants.

Tracing environmental markers of autoimmunity: introducing the infectome

We recently introduced the concept of the infectome as a means of studying all infectious factors which contribute to the development of autoimmune disease. It forms the infectious part of the exposome, which collates all environmental factors contributing to the development of disease and studies the sum total of burden which leads to the loss of adaptive mechanisms in the body. These studies complement genome-wide association studies, which establish the genetic predisposition to disease. The infectome is a component which spans the whole life and may begin at the earliest stages right up to the time when the first symptoms manifest, and may thus contribute to the understanding of the pathogenesis of autoimmunity at the prodromal/asymptomatic stages. We provide practical examples and research tools as to how we can investigate disease-specific infectomes, using laboratory approaches employed from projects studying the “immunome” and “microbiome”. It is envisioned that an understanding of the infectome and the environmental factors that affect it will allow for earlier patient-specific intervention by clinicians, through the possible treatment of infectious agents as well as other compounding factors, and hence slowing or preventing disease development.

Estimating common parameters of lognormally distributed environmental and biomonitoring data: harmonizing disparate statistics from publications

The progression of science is driven by the accumulation of knowledge and builds upon published work of others. Another important feature is to place current results into the context of previous observations. The published literature, however, often does not provide sufficient direct information for the reader to interpret the results beyond the scope of that particular article. Authors tend to provide only summary statistics in various forms, such as means and standard deviations, median and range, quartiles, 95% confidence intervals, and so on, rather than providing measurement data. Second, essentially all environmental and biomonitoring measurements have an underlying lognormal distribution, so certain published statistical characterizations may be inappropriate for comparisons. The aim of this study was to review and develop direct conversions of different descriptions of data into a standard format comprised of the geometric mean (GM) and the geometric standard deviation (GSD) and then demonstrate how, under the assumption of lognormal distribution, these parameters are used to answer questions of confidence intervals, exceedance levels, and statistical differences among distributions. A wide variety of real-world measurement data sets was reviewed, and it was demonstrated that these data sets are indeed of lognormal character, thus making them amenable to these methods. Potential errors incurred from making retrospective estimates from disparate summary statistics are described. In addition to providing tools to interpret "other people's data," this review should also be seen as a cautionary tale for publishing one's own data to make it as useful as possible for other researchers.

Analysis of exhaled breath for disease detection

Breath analysis is a young field of research with great clinical potential. As a result of this interest, researchers have developed new analytical techniques that permit real-time analysis of exhaled breath with breath-to-breath resolution in addition to the conventional central laboratory methods using gas chromatography-mass spectrometry. Breath tests are based on endogenously produced volatiles, metabolites of ingested precursors, metabolites produced by bacteria in the gut or the airways, or volatiles appearing after environmental exposure. The composition of exhaled breath may contain valuable information for patients presenting with asthma, renal and liver diseases, lung cancer, chronic obstructive pulmonary disease, inflammatory lung disease, or metabolic disorders. In addition, oxidative stress status may be monitored via volatile products of lipid peroxidation. Measurement of enzyme activity provides phenotypic information important in personalized medicine, whereas breath measurements provide insight into perturbations of the human exposome and can be interpreted as preclinical signals of adverse outcome pathways.

Employing heat maps to mine associations in structured routine care data

OBJECTIVE

Mining the electronic medical record (EMR) has the potential to deliver new medical knowledge about causal effects, which are hidden in statistical associations between different patient attributes. It is our goal to detect such causal mechanisms within current research projects which include e.g. the detection of determinants of imminent ICU readmission. An iterative statistical approach to examine each set of considered attribute pairs delivers potential answers but is difficult to interpret. Therefore, we aimed to improve the interpretation of the resulting matrices by the use of heat maps. We propose strategies to adapt heat maps for the search for associations and causal effects within routine EMR data.

METHODS

Heat maps visualize tabulated metric datasets as grid-like choropleth maps, and thus present measures of association between numerous attribute pairs clearly arranged. Basic assumptions about plausible exposures and outcomes are used to allocate distinct attribute sets to both matrix dimensions. The image then avoids certain redundant graphical elements and provides a clearer picture of the supposed associations. Specific color schemes have been chosen to incorporate preexisting information about similarities between attributes. The use of measures of association as a clustering input has been taken as a trigger to apply transformations which ensure that distance metrics always assume finite values and treat positive and negative associations in the same way. To evaluate the general capability of the approach, we conducted analyses of simulated datasets and assessed diagnostic and procedural codes in a large routine care dataset.

RESULTS

Simulation results demonstrate that the proposed clustering procedure rearranges attributes similar to simulated statistical associations. Thus, heat maps are an excellent tool to indicate whether associations concern the same attributes or different ones, and whether affected attribute sets conform to any preexisting relationship between attributes. The dendrograms help in deciding if contiguous sequences of attributes effectively correspond to homogeneous attribute associations. The exemplary analysis of a routine care dataset revealed patterns of associations that follow plausible medical constellations for several diseases and the associated medical procedures and activities. Cases with breast cancer (ICD C50), for example, appeared to be associated with radiation therapy (8-52). In cross check, approximately 60 percent of the attribute pairs in this dataset showed a strong negative association, which can be explained by diseases treated in a medical specialty which routinely does not perform the respective procedures in these cases. The corresponding diagram clearly reflects these relationships in the shape of coherent subareas.

CONCLUSION

We could demonstrate that heat maps of measures of association are effective for the visualization of patterns in routine care EMRs. The adjustable method for the assignment of attributes to image dimensions permits a balance between the display of ample information and a favorable level of graphical complexity. The scope of the search can be adapted by the use of pre-existing assumptions about plausible effects to select exposure and outcome attributes. Thus, the proposed method promises to simplify the detection of undiscovered causal effects within routine EMR data.

Infectome: a platform to trace infectious triggers of autoimmunity

The "exposome" is a term recently used to describe all environmental factors, both exogenous and endogenous, which we are exposed to in a lifetime. It represents an important tool in the study of autoimmunity, complementing classical immunological research tools and cutting-edge genome wide association studies (GWAS). Recently, environmental wide association studies (EWAS) investigated the effect of environment in the development of diseases. Environmental triggers are largely subdivided into infectious and non-infectious agents. In this review, we introduce the concept of the "infectome", which is the part of the exposome referring to the collection of an individual's exposures to infectious agents. The infectome directly relates to geoepidemiological, serological and molecular evidence of the co-occurrence of several infectious agents associated with autoimmune diseases that may provide hints for the triggering factors responsible for the pathogenesis of autoimmunity. We discuss the implications that the investigation of the infectome may have for the understanding of microbial/host interactions in autoimmune diseases with long, pre-clinical phases. It may also contribute to the concept of the human body as a superorganism where the microbiome is part of the whole organism, as can be seen with mitochondria which existed as microbes prior to becoming organelles in eukaryotic cells of multicellular organisms over time. A similar argument can now be made in regard to normal intestinal flora, living in symbiosis within the host. We also provide practical examples as to how we can characterise and measure the totality of a disease-specific infectome, based on the experimental approaches employed from the "immunome" and "microbiome" projects.