Jane E. Gallagher

Differential particulate air pollution induced oxidant stress in human granulocytes, monocytes and alveolar macrophages ☆

It has been proposed that oxidant stress of cells in the lung is one of the underlying mechanisms of particulate pollution-induced exacerbation of lung disease. Individuals who are considered most sensitive to particulate pollution are those with pre-existing airways inflammation, such as chronic obstructive pulmonary disease (COPD), lung infection or asthma. These diseases are characterized by a presence of inflammatory cells in the airways including neutrophils (PMN), eosinophils and monocytes (Mo), and increased numbers of alveolar macrophages (AM). These cells have a high capacity for production of oxygen radicals, as compared to other cell types of the lung. To assess the oxidative response of these various cell types to pollution particles of various sources, luminol-dependent chemiluminescence was employed. Particles including transition metal-rich residual oil fly ashes (ROFAs), coal fly ashes, diesel, SiO2, TiO2 and fugitive dusts were co-cultured with AM, Mo and PMN in a dose range of 10-100 microg/2 x 10(5) cells and chemiluminescence determined following a 20-min interaction. A strong oxidant response of AM was restricted to oil fly ashes, while the PMN were most reactive to the dusts containing aluminium silicate. In general, the Mo response was less vigorous, but overlapped both AM- and PMN-stimulating dusts. However, in response to SiO2 and volcanic ash the Mo chemiluminescence exceeded that of the other cell types. Oxygen radicals generated in response to ROFA by the AM were likely to be dependent on mitochondrial processes, while the response in PMN involved the membrane NADPH oxidase complex, as determined by targeting inhibitors. The response of AM to SiO2 of various sizes and TiO2 in the fine size range obtained from different commercial sources, was highly variable, implying that composition rather than size was responsible for the oxidant response. A strong chemiluminescence response was not consistently associated with cytotoxicity in the responsive cell. Taken together, these results suggest that oxidant activation by various sources of particulate matter is cell specific. Therefore, the inflamed lung is likely to be more susceptible to harm of ambient air particulates because of the oxidant stress posed by a broader range of particles.

Advancing Exposure Characterization for Chemical Evaluation and Risk Assessment

A new generation of scientific tools has emerged to rapidly measure signals from cells, tissues, and organisms following exposure to chemicals. High-visibility efforts to apply these tools for efficient toxicity testing raise important research questions in exposure science. As vast quantities of data from high-throughput screening (HTS) in vitro toxicity assays become available, this new toxicity information must be translated to assess potential risks to human health from environmental exposures. Exposure information is required to link information on potential toxicity of environmental contaminants to real-world health outcomes. In the immediate term, tools are required to characterize and classify thousands of environmental chemicals in a rapid and efficient manner to prioritize testing and assess potential for risk to human health. Rapid risk assessment requires prioritization based on both hazard and exposure dimensions of the problem. To address these immediate needs within the context of longer term objectives for chemical evaluation and risk management, a translation framework is presented for incorporating toxicity and exposure information to inform public health decisions at both the individual and population levels. Examples of required exposure science contributions are presented with a focus on early advances in tools for modeling important links across the source-to-outcome paradigm. ExpoCast, a new U.S. Environmental Protection Agency (EPA) program aimed at developing novel approaches and metrics to screen and evaluate chemicals based on the potential for biologically relevant human exposures is introduced. The goal of ExpoCast is to advance characterization of exposure required to translate findings in computational toxicology to information that can be directly used to support exposure and risk assessment for decision making and improved public health.

Differences in detection of DNA adducts in the 32P-postlabelling assay after either 1-butanol extraction or nuclease P1 treatment

The use of nuclease P1 treatment and 1-butanol extraction to increase the sensitivity of the 32P-postlabelling assay for DNA adducts have been compared. Although similar results were obtained with the two methods for standard adducts formed with benzo[a]pyrene diol epoxide I (BPDE-I), nuclease P1 treatment resulted in a significant reduction in detection of major adducts from 1-amino-6-nitropyrene (1-amino-6-NP), 1-amino-8-nitropyrene (1-amino-8-NP), 2-aminofluorene (2-AF), 2-naphthylamine (2-NA) and 4-aminobiphenyl (4-ABP) modified DNAs, but not following the 32P-postlabelling analysis of 2-acetylaminofluorene (2-AAF) modified DNA. These results suggest that, at least initially, both modifications of the 32P-postlabelling assay should be used for the detection of unknown adducts or for adducts derived from nitroaromatics and aromatic amines.

Higher Environmental Relative Moldiness Index (ERMIsm) values measured in Detroit homes of severely asthmatic children.

Sieved vacuum bag dust from the homes of 143 children in Detroit was analyzed by mold specific quantitative PCR (MSQPCR) and the Environmental Relative Moldiness Index (ERMIsm) was calculated for each home. Children living in these homes were grouped as non-asthmatic (n=83), moderately asthmatic (n=28) and severely asthmatic (n=32) based on prescription medication usage for their asthma management (none, occasional and daily, respectively). The mean ERMI for each group of homes was 6.2 for non-asthmatic, 6.3 for moderately asthmatic and 8.2 for severely asthmatic children. The ERMI values in the homes of severely asthmatic children were significantly greater compared to the non-asthmatics (p=0.04 in Wilcoxon Rank-sum test). Aspergillus niger and Aspergillus unguis were the primary mold species that distinguished severely asthmatic childrens homes and non-asthmatic childrens homes (p<0.05; Wilcoxon Rank-sum test). The determination of the homes ERMI values may aid in prioritizing home remediation efforts, particularly in those children who are at increased risk for asthma exacerbation.

Exposure science and the U.S. EPA National Center for Computational Toxicology

The emerging field of computational toxicology applies mathematical and computer models and molecular biological and chemical approaches to explore both qualitative and quantitative relationships between sources of environmental pollutant exposure and adverse health outcomes. The integration of modern computing with molecular biology and chemistry will allow scientists to better prioritize data, inform decision makers on chemical risk assessments and understand a chemicals progression from the environment to the target tissue within an organism and ultimately to the key steps that trigger an adverse health effect. In this paper, several of the major research activities being sponsored by Environmental Protection Agencys National Center for Computational Toxicology are highlighted. Potential links between research in computational toxicology and human exposure science are identified. As with the traditional approaches for toxicity testing and hazard assessment, exposure science is required to inform design and interpretation of high-throughput assays. In addition, common themes inherent throughout National Center for Computational Toxicology research activities are highlighted for emphasis as exposure science advances into the 21st century.

Biomarkers of Exposure

Publisher Summary A biomarker of exposure can be defined as the degree of individual exposure to xenobiotics in a given environment by providing a measurement of internal dose. A relatively narrow definition of an exposure biomarker would be the level of the parent compound or secondary products, such as metabolites, and/or reaction products in biological fluid, cells, or subcellular component, for example, DNA and protein adducts. Ideally, the purpose of measuring biomarkers of xenobiotic exposure is ultimately to provide a better linkage between the exposure with biological effects and/or clinical disease. Thus, biomarkers represent a spectrum between health and disease. Biomarkers of exposure may become critical in the prevention or treatment of a disease. It may also provide information regarding the mechanism(s) leading to a biological effect, and to determine if similar processes are occurring across species. An important attribute of exposure biomarkers is that it takes into account individual determinants of the dosimetry and metabolism of the absorbed pollutant. The factors that affect these rest mainly with individual susceptibility and can include avoidance behavior, personal protective equipment, physiological and psychological adaptations, physical activity, metabolic factors, and organ transport rates. Thus, measurement of the external exposure may not adequately correlate with the target tissue dose or internal dose because of these factors.

Data-driven asthma endotypes defined from blood biomarker and gene expression data.

The diagnosis and treatment of childhood asthma is complicated by its mechanistically distinct subtypes (endotypes) driven by genetic susceptibility and modulating environmental factors. Clinical biomarkers and blood gene expression were collected from a stratified, cross-sectional study of asthmatic and non-asthmatic children from Detroit, MI. This study describes four distinct asthma endotypes identified via a purely data-driven method. Our method was specifically designed to integrate blood gene expression and clinical biomarkers in a way that provides new mechanistic insights regarding the different asthma endotypes. For example, we describe metabolic syndrome-induced systemic inflammation as an associated factor in three of the four asthma endotypes. Context provided by the clinical biomarker data was essential in interpreting gene expression patterns and identifying putative endotypes, which emphasizes the importance of integrated approaches when studying complex disease etiologies. These synthesized patterns of gene expression and clinical markers from our research may lead to development of novel serum-based biomarker panels.

Decision tree-based method for integrating gene expression, demographic, and clinical data to determine disease endotypes

BackgroundComplex diseases are often difficult to diagnose, treat and study due to the multi-factorial nature of the underlying etiology. Large data sets are now widely available that can be used to define novel, mechanistically distinct disease subtypes (endotypes) in a completely data-driven manner. However, significant challenges exist with regard to how to segregate individuals into suitable subtypes of the disease and understand the distinct biological mechanisms of each when the goal is to maximize the discovery potential of these data sets.ResultsA multi-step decision tree-based method is described for defining endotypes based on gene expression, clinical covariates, and disease indicators using childhood asthma as a case study. We attempted to use alternative approaches such as the Student’s t-test, single data domain clustering and the Modk-prototypes algorithm, which incorporates multiple data domains into a single analysis and none performed as well as the novel multi-step decision tree method. This new method gave the best segregation of asthmatics and non-asthmatics, and it provides easy access to all genes and clinical covariates that distinguish the groups.ConclusionsThe multi-step decision tree method described here will lead to better understanding of complex disease in general by allowing purely data-driven disease endotypes to facilitate the discovery of new mechanisms underlying these diseases. This application should be considered a complement to ongoing efforts to better define and diagnose known endotypes. When coupled with existing methods developed to determine the genetics of gene expression, these methods provide a mechanism for linking genetics and exposomics data and thereby accounting for both major determinants of disease.

Toxicological evaluation of complex industrial wastes: implications for exposure assessment☆

We evaluated a variety of short-term bioassays to construct a battery of tests that could be used for assessing the biological effects of potentially hazardous complex industrial wastes. Ten samples were studied for hepatotoxicity; these samples and an additional 5 were studied for mutagenicity. Although the data are limited to these samples, the results suggest that the Salmonella assay (strain TA98) or a prophage-induction assay (both in the presence of S9) in combination with determination of relative liver weight and levels of a set of serum enzymes in rats may provide a battery of tests suitable to characterize complex industrial wastes for mutagenic and hepatotoxic potential. The biological activities exhibited by the wastes were not readily predicted by the chemical profiles of the wastes, emphasizing the importance of characterizing potentially hazardous complex industrial wastes by both chemical and biological means. DNA from liver, lung and bladder of rats exposed to some of the wastes was analyzed by the 32P-postlabeling technique for the presence of DNA adducts. A waste that produced mutagenic urine produced a DNA adduct in bladder DNA. The implications of this approach for assessment of exposure to complex hazardous waste mixtures are discussed.

Evaluation of genetic susceptibility to childhood allergy and asthma in an African American urban population

BackgroundAsthma and allergy represent complex phenotypes, which disproportionately burden ethnic minorities in the United States. Strong evidence for genomic factors predisposing subjects to asthma/allergy is available. However, methods to utilize this information to identify high risk groups are variable and replication of genetic associations in African Americans is warranted.MethodsWe evaluated 41 single nucleotide polymorphisms (SNP) and a deletion corresponding to 11 genes demonstrating association with asthma in the literature, for association with asthma, atopy, testing positive for food allergens, eosinophilia, and total serum IgE among 141 African American children living in Detroit, Michigan. Independent SNP and haplotype associations were investigated for association with each trait, and subsequently assessed in concert using a genetic risk score (GRS).ResultsStatistically significant associations with asthma were observed for SNPs in GSTM1, MS4A2, and GSTP1 genes, after correction for multiple testing. Chromosome 11 haplotype CTACGAGGCC (corresponding to MS4A2 rs574700, rs1441586, rs556917, rs502581, rs502419 and GSTP1 rs6591256, rs17593068, rs1695, rs1871042, rs947895) was associated with a nearly five-fold increase in the odds of asthma (Odds Ratio (OR) = 4.8, p = 0.007). The GRS was significantly associated with a higher odds of asthma (OR = 1.61, 95% Confidence Interval = 1.21, 2.13; p = 0.001).ConclusionsVariation in genes associated with asthma in predominantly non-African ethnic groups contributed to increased odds of asthma in this African American study population. Evaluating all significant variants in concert helped to identify the highest risk subset of this group.