Mixtures modeling identifies chemical inducers versus repressors of toxicity associated with wildfire smoke

Exposure to wildfire smoke continues to be a growing threat to public health, yet the chemical components in wildfire smoke that primarily drive toxicity and associated disease are largely unknown. This study utilized a suite of computational approaches to identify groups of chemicals induced by variable biomass burn conditions that were associated with biological responses in the mouse lung, including pulmonary immune response and injury markers. Smoke condensate samples were collected and characterized, resulting in chemical distribution information for 86 constituents across ten different exposures. Mixtures-relevant statistical methods included (i) a chemical clustering and data-reduction method, weighted chemical co-expression network analysis (WCCNA), (ii) a quantile g-computation approach to address the joint effect of multiple chemicals in different groupings, and (iii) a correlation analysis to compare mixtures modeling results against individual chemical relationships. Seven chemical groups were identified using WCCNA based on co-occurrence showing both positive and negative relationships with biological responses. A group containing methoxyphenols (e.g., coniferyl aldehyde, eugenol, guaiacol, and vanillin) displayed highly significant, negative relationships with several biological responses, including cytokines and lung injury markers. This group was further shown through quantile g-computation methods to associate with reduced biological responses. Specifically, mixtures modeling based on all chemicals excluding those in the methoxyphenol group demonstrated more significant, positive relationships with several biological responses; whereas mixtures modeling based on just those in the methoxyphenol group demonstrated significant negative relationships with several biological responses, suggesting potential protective effects. Mixtures-based analyses also identified other groups consisting of inorganic elements and ionic constituents showing positive relationships with several biological responses, including markers of inflammation. Many of the effects identified through mixtures modeling in this analysis were not captured through individual chemical analyses. Together, this study demonstrates the utility of mixtures-based approaches to identify potential drivers and inhibitors of toxicity relevant to wildfire exposures

Airway cells from atopic asthmatic patients exposed to ozone display an enhanced innate immune gene profile

This study identifies transcriptional phenotypes of sputum samples from normal volunteers and atopic asthmatics exposed to ozone. Network analyses suggest that asthmatics elevate immune signaling following oxidative stress, while nonasthmatics attempt to mitigate the ozone-induced response

Plasma sterols and vitamin D are correlates and predictors of ozone-induced inflammation in the lung: A pilot study

BACKGROUND: Ozone (O3) exposure causes respiratory effects including lung function decrements, increased lung permeability, and airway inflammation. Additionally, baseline metabolic state can predispose individuals to adverse health effects from O3. For this reason, we conducted an exploratory study to examine the effect of O3 exposure on derivatives of cholesterol biosynthesis: sterols, oxysterols, and secosteroid (25-hydroxyvitamin D) not only in the lung, but also in circulation. METHODS: We obtained plasma and induced sputum samples from non-asthmatic (n = 12) and asthmatic (n = 12) adult volunteers 6 hours following exposure to 0.4ppm O3 for 2 hours. We quantified the concentrations of 24 cholesterol precursors and derivatives by UPLC-MS and 30 cytokines by ELISA. We use computational analyses including machine learning to determine whether baseline plasma sterols are predictive of O3 responsiveness. RESULTS: We observed an overall decrease in the concentration of cholesterol precursors and derivatives (e.g. 27-hydroxycholesterol) and an increase in concentration of autooxidation products (e.g. secosterol-B) in sputum samples. In plasma, we saw a significant increase in the concentration of secosterol-B after O3 exposure. Machine learning algorithms showed that plasma cholesterol was a top predictor of O3 responder status based on decrease in FEV1 (>5%). Further, 25-hydroxyvitamin D was positively associated with lung function in non-asthmatic subjects and with sputum uteroglobin, whereas it was inversely associated with sputum myeloperoxidase and neutrophil counts. CONCLUSION: This study highlights alterations in sterol metabolites in the airway and circulation as potential contributors to systemic health outcomes and predictors of pulmonary and inflammatory responsiveness following O3 exposure

Identifying the transcriptional response of cancer and inflammation-related genes in lung cells in relation to ambient air chemical mixtures in Houston, Texas

Atmospheric pollution represents a complex mixture of air chemicals that continually interact and transform, making it difficult to accurately evaluate associated toxicity responses representative of real-world exposure. This study leveraged data from a previously published article and reevaluated lung cell transcriptional response induced by outdoor atmospheric pollution mixtures using field-based exposure conditions in the industrialized Houston Ship Channel. The tested hypothesis was that individual and co-occurring chemicals in the atmosphere relate to altered expression of critical genes involved in inflammation and cancer-related processes in lung cells. Human lung cells were exposed at an air−liquid interface to ambient air mixtures for 4 h, with experiments replicated across 5 days. Real-time monitoring of primary and secondary gas-phase pollutants, as well as other atmospheric conditions, was simultaneously conducted. Transcriptional analysis of exposed cells identified critical genes showing differential expression associated with both individual and chemical mixtures. The individual pollutant identified with the largest amount of associated transcriptional response was benzene. Tumor necrosis factor (TNF) and interferon regulatory factor 1 (IRFN1) were identified as key upstream transcription factor regulators of the cellular response to benzene. This study is among the first to measure lung cell transcriptional responses in relation to real-world, gas-phase air mixtures

Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods

Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently integrated methods that incorporate genetic data to strengthen causal inference, despite evidence that many exposome-associated phenotypes are heritable. OBJECTIVE: We demonstrate how integration of methods and study designs that incorporate genetic data can strengthen causal inference in exposomics research by helping address six challenges: reverse causation and unmeasured confounding, comprehensive examination of phenotypic effects, low efficiency, replication, multilevel data integration, and characterization of tissue-specific effects. Examples are drawn from studies of biomarkers and health behaviors, exposure domains where the causal inference methods we describe are most often applied. DISCUSSION: Technological, computational, and statistical advances in genotyping, imputation, and analysis, combined with broad data sharing and cross-study collaborations, offer multiple opportunities to strengthen causal inference in exposomics research. Full application of these opportunities will require an expanded understanding of genetic variants that predict exposome phenotypes as well as an appreciation that the utility of genetic variants for causal inference will vary by exposure and may depend on large sample sizes. However, several of these challenges can be addressed through international scientific collaborations that prioritize data sharing. Ultimately, we anticipate that efforts to better integrate methods that incorporate genetic data will extend the reach of exposomics research by helping address the challenges of comprehensively measuring the exposome and its health effects across studies, the life course, and in varied contexts and diverse populations

The MAJORANA DEMONSTRATOR for 0νββ: Current Status and Future Plans

The MAJORANA DEMONSTRATOR will search for neutrinoless-double-beta decay (0νββ) in 76Ge, while establishing the feasibility of a future tonne-scale germanium-based 0νββ experiment, and performing searches for new physics beyond the Standard Model. The experiment, currently under construction at the Sanford Underground Research Facility in Lead, SD, will consist of a pair of modular high-purity germanium detector arrays housed inside of a compact copper, lead, and polyethylene shield. Through a combination of strict materials qualifications and assay, low-background design, and powerful background rejection techniques, the Demonstrator aims to achieve a background rate in the 0νββ region of interest (ROI) of no more than 3 counts in the 0νββ-decay ROI per tonne of target isotope per year (cnts/(ROI-t-y)). The current status of the Demonstrator is discussed, as are plans for its completion

A Dark Matter Search with MALBEK

The Majorana Demonstrator is an array of natural and enriched high purity germanium detectors that will search for the neutrinoless double-beta decay of 76Ge and perform a search for weakly interacting massive particles (WIMPs) with masses below 10 GeV. As part of the Majorana research and development efforts, we have deployed a modified, low-background broad energy germanium detector at the Kimballton Underground Research Facility. With its sub-keV energy threshold, this detector is sensitive to potential non-Standard Model physics, including interactions with WIMPs. We discuss the backgrounds present in the WIMP region of interest and explore the impact of slow surface event contamination when searching for a WIMP signal

The Majorana Demonstrator: A Search for Neutrinoless Double-beta Decay of 76Ge

Neutrinoless double-beta (0νββ) decay is a hypothesized process where in some even-even nuclei it might be possible for two neutrons to simultaneously decay into two protons and two electrons without emitting neutrinos. This is possible only if neutrinos are Majorana particles, i.e. fermions that are their own antiparticles. Neutrinos being Majorana particles would explicitly violate lepton number conservation, and might play a role in the matter-antimatter asymmetry in the universe. The observation of neutrinoless double-beta decay would also provide complementary information related to neutrino masses. The Majorana Collaboration is constructing the MAJORANA DEMONSTRATOR, with a total of 40-kg Germanium detectors, to search for the 0νββ decay of 76Ge and to demonstrate a background rate at or below 3 counts/(ROI•t•y) in the 4 keV region of interest (ROI) around the 2039 keV Q-value for 76Ge 0νββ decay. In this paper, we discuss the physics of neutrinoless double beta decay and then focus on the MAJORANA DEMONSTRATOR, including its design and approach to achieve ultra-low backgrounds and the status of the experiment

Search for Neutrinoless Double- β Decay in Ge 76 with the Majorana Demonstrator

The Majorana Collaboration is operating an array of high purity Ge detectors to search for neutrinoless double-β decay in Ge76. The Majorana Demonstrator comprises 44.1 kg of Ge detectors (29.7 kg enriched in Ge76) split between two modules contained in a low background shield at the Sanford Underground Research Facility in Lead, South Dakota. Here we present results from data taken during construction, commissioning, and the start of full operations. We achieve unprecedented energy resolution of 2.5 keV FWHM at Qββ and a very low background with no observed candidate events in 9.95 kg yr of enriched Ge exposure, resulting in a lower limit on the half-life of 1.9×1025 yr (90% C.L.). This result constrains the effective Majorana neutrino mass to below 240-520 meV, depending on the matrix elements used. In our experimental configuration with the lowest background, the background is 4.0-2.5+3.1 counts/(FWHM t yr)