Novel Mechanisms of Ozone-Induced Pulmonary Inflammation and Resolution, and the Potential Protective Role of Scavenger Receptor BI.

INTRODUCTION: Increases in ambient levels of ozone (O3), a criteria air pollutant, have been associated with increased susceptibility and exacerbations of chronic pulmonary diseases through lung injury and inflammation. O3 induces pulmonary inflammation, in part by generating damage-associated molecular patterns (DAMPs), which are recognized by pattern recognition receptors (PRRs), such as toll-like receptors (TLRs) and scavenger receptors (SRs). This inflammatory response is mediated in part by alveolar macrophages (AMs), which highly express PRRs, including scavenger receptor BI (SR-BI). Once pulmonary inflammation has been induced, an active process of resolution occurs in order to prevent secondary necrosis and to restore tissue homeostasis. The processes known to promote the resolution of inflammation include the clearance by macrophages of apoptotic cells, known as efferocytosis, and the production of specialized pro-resolving mediators (SPMs). Impaired efferocytosis and production of SPMs have been associated with the pathogenesis of chronic lung diseases; however, these impairments have yet to be linked with exposure to air pollutants. SPECIFIC AIMS: The primary goals of this study were: Aim 1 - to define the role of SR-BI in O3-derived pulmonary inflammation and resolution of injury; and Aim 2 - to determine if O3 exposure alters pulmonary production of SPMs and processes known to promote the resolution of pulmonary inflammation and injury. METHODS: To address Aim 1, female wild-type (WT) and SR-BI-deficient, or knock-out (SR-BI KO), mice were exposed to either O3 or filtered air. In one set of experiments mice were instilled with an oxidized phospholipid (oxPL). Bronchoalveolar lavage fluid (BALF) and lung tissue were collected for the analyses of inflammatory and injury markers and oxPL. To estimate efferocytosis, mice were administered apoptotic cells (derived from the Jurkat T cell line) after O3 or filtered air exposure.To address Aim 2, male WT mice were exposed to either O3 or filtered air, and levels of SPMs were assessed in the lung, as well as markers of inflammation and injury in BALF. In some experiments SPMs were administered before exposure to O3or filtered air, to determine whether SPMs could mitigate inflammatory or resolution responses. Efferocytosis was measured as in Aim 1. RESULTS: For Aim 1, SR-BI protein levels increased in the lung tissue of mice exposed to O3, compared with mice exposed to filtered air. Compared with WT controls, SR-BI KO mice had a significant increase in the number of neutrophils in their airspace 24 hours post O3 exposure. The oxPL levels increased in the airspace of both WT and SR-BI KO mice after O3 exposure, compared with filtered air controls. Four hours after instillation of an oxPL, SR-BI KO mice had an increase in BALF neutrophils and total protein, and a nonsignificant increase in macrophages compared with WT controls. O3 exposure decreased efferocytosis in both WT and SR-BI KO female mice.For Aim 2, mice given SPM supplementation before O3 exposure showed significantly increased AM efferocytosis when compared with the O3exposure control mice and also showed some mitigation of the effects of O3 on inflammation and injury. Several SPMs and their precursors were measured in lung tissue using reverse-phase high performance liquid chromatography (HPLC) with tandem mass spectrometry (MS/MS). At 24 hours after O3 exposure 14R-hydroxydocosahexaenoic acid (HDHA) and 10,17-dihydroxydocosahexaenoic acid (diHDoHE) were significantly decreased in lung tissue, but at 6 hours after exposure, levels of these SPMs increased. CONCLUSIONS: Our findings identify novel mechanisms by which O3 may induce pulmonary inflammation and also increase susceptibility to and exacerbations of chronic lung diseases.

AMON: annotation of metabolite origins via networks to integrate microbiome and metabolome data.

BACKGROUND: Untargeted metabolomics of host-associated samples has yielded insights into mechanisms by which microbes modulate health. However, data interpretation is challenged by the complexity of origins of the small molecules measured, which can come from the host, microbes that live within the host, or from other exposures such as diet or the environment. RESULTS: We address this challenge through development of AMON: Annotation of Metabolite Origins via Networks. AMON is an open-source bioinformatics application that can be used to annotate which compounds in the metabolome could have been produced by bacteria present or the host, to evaluate pathway enrichment of host verses microbial metabolites, and to visualize which compounds may have been produced by host versus microbial enzymes in KEGG pathway maps. CONCLUSIONS: AMON empowers researchers to predict origins of metabolites via genomic information and to visualize potential host:microbe interplay. Additionally, the evaluation of enrichment of pathway metabolites of host versus microbial origin gives insight into the metabolic functionality that a microbial community adds to a host:microbe system. Through integrated analysis of microbiome and metabolome data, mechanistic relationships between microbial communities and host phenotypes can be better understood.

Dysregulation of metabolic pathways in a mouse model of allergic asthma.

BACKGROUND: Asthma is a complex lung disease resulting from the interplay of genetic and environmental factors. To understand the molecular changes that occur during the development of allergic asthma without genetic and environmental confounders, an experimental model of allergic asthma in mice was used. Our goals were to (1) identify changes at the small molecule level due to allergen exposure, (2) determine perturbed pathways due to disease, and (3) determine whether small molecule changes correlate with lung function. METHODS: In this experimental model of allergic asthma, matched bronchoalveolar lavage (BAL) fluid and plasma were collected from three groups of C57BL6 mice (control vs sensitized and/or challenged with ovalbumin, n=3-5/group) 6 hour, 24 hour, and 48 hour after the last challenge. Samples were analyzed using liquid chromatography-mass spectrometry-based metabolomics. Airway hyper-responsiveness (AHR) measurements and differential cell counts were performed. RESULTS: In total, 398 and 368 dysregulated metabolites in the BAL fluid and plasma of sensitized and challenged mice were identified, respectively. These belonged to four, interconnected pathways relevant to asthma pathogenesis: sphingolipid metabolism (P=6.6×10-5 ), arginine and proline metabolism (P=1.12×10-7 ), glycerophospholipid metabolism (P=1.3×10-10 ), and the neurotrophin signaling pathway (P=7.0×10-6 ). Furthermore, within the arginine and proline metabolism pathway, a positive correlation between urea-1-carboxylate and AHR was observed in plasma metabolites, while ornithine revealed a reciprocal effect. In addition, agmatine positively correlated with lung eosinophilia. CONCLUSION: These findings point to potential targets and pathways that may be central to asthma pathogenesis and can serve as novel therapeutic targets.

Identification of a Preliminary Plasma Metabolome-based Biomarker for Circadian Phase in Humans.

Measuring individual circadian phase is important to diagnose and treat circadian rhythm sleep-wake disorders and circadian misalignment, inform chronotherapy, and advance circadian science. Initial findings using blood transcriptomics to predict the circadian phase marker dim-light melatonin onset (DLMO) show promise. Alternatively, there are limited attempts using metabolomics to predict DLMO and no known omics-based biomarkers predict dim-light melatonin offset (DLMOff). We analyzed the human plasma metabolome during adequate and insufficient sleep to predict DLMO and DLMOff using one blood sample. Sixteen (8 male/8 female) healthy participants aged 22.4 ± 4.8 years (mean ± SD) completed an in-laboratory study with 3 baseline days (9 h sleep opportunity/night), followed by a randomized cross-over protocol with 9-h adequate sleep and 5-h insufficient sleep conditions, each lasting 5 days. Blood was collected hourly during the final 24 h of each condition to independently determine DLMO and DLMOff. Blood samples collected every 4 h were analyzed by untargeted metabolomics and were randomly split into training (68%) and test (32%) sets for biomarker analyses. DLMO and DLMOff biomarker models were developed using partial least squares regression in the training set followed by performance assessments using the test set. At baseline, the DLMOff model showed the highest performance (0.91 R2 and 1.1 ± 1.1 h median absolute error ± interquartile range [MdAE ± IQR]), with significantly (p < 0.01) lower prediction error versus the DLMO model. When all conditions (baseline, 9 h, and 5 h) were included in performance analyses, the DLMO (0.60 R2; 2.2 ± 2.8 h MdAE; 44% of the samples with an error under 2 h) and DLMOff (0.62 R2; 1.8 ± 2.6 h MdAE; 51% of the samples with an error under 2 h) models were not statistically different. These findings show promise for metabolomics-based biomarkers of circadian phase and highlight the need to test biomarkers that predict multiple circadian phase markers under different physiological conditions.