Alteration of glycosphingolipid metabolism by ozone is associated with exacerbation of allergic asthma characteristics in mice

Asthma research in mice: An overview of current models and their methodological variability

Studies in murine experimental models have made significant contributions to the understanding of asthma pathophysiology and the discovery of innovative therapeutic approaches. Nonetheless, there is a plethora of options available for selecting mouse strains, sensitization methods, challenge routes and doses, as well as approaches to evaluating host response in murine asthma model protocols. Due to the diversity of models employed, comparing results across different studies proves exceedingly challenging. The study conducted a search of pertinent PubMed articles from 2022 to April 15th, 2024. After relevant publications had been selected, the characteristics of each study were extracted, including animal strains, animal sex, sensitization methods, challenge methods, and reported outcome measures. The modeling parameters of Ovalbumin (OVA)-induced asthma model, and House Dust Mite-induced asthma model were analyzed. Additionally, we extracted data on the dose of OVA sensitization, alum administration, challenge OVA dose, and alum/sensitization OVA ratio from seven included studies. Subsequently, we conducted an analysis to determine the correlation between each of these factors and the lung resistance index (RI). This study presents an overview of the current mouse asthma models, offering valuable methodological guidance for researchers. Furthermore, this study highlights that certain parameters like sensitization dose, challenge dose, and so on, exert specific effects on the asthma lung resistance. However, there is a lack of standardized criteria and guidelines in this regard. The effects and underlying mechanisms of parameters on asthma responses remain unclear, necessitating further investigation into model parameters.

Resolving multi-image spatial lipidomic responses to inhaled toxicants by machine learning

Regional responses to inhaled toxicants are essential to understand the pathogenesis of lung disease under exposure to air pollution. We evaluate the effect of combined allergen sensitization and ozone exposure on eliciting spatial differences in lipid distribution in the mouse lung that may contribute to ozone-induced exacerbations in asthma. We demonstrate the ability to normalize and segment high resolution mass spectrometry imaging data by applying established machine learning algorithms. Interestingly, our segmented regions overlap with histologically validated lung regions, enabling regional analysis across biological replicates. Our data reveal differences in the abundance of spatially distinct lipids, support the potential role of lipid saturation in healthy lung function, and highlight sex differences in regional lung lipid distribution following ozone exposure. Our study provides a framework for future mass spectrometry imaging experiments capable of relative quantification across biological replicates and expansion to multiple sample types, including human tissue.

Intermittent ozone inhalation during house dust mite-induced sensitization primes for adverse asthma phenotype

The ability of air pollution to induce acute exacerbation of asthma is well documented. However, the ability of ozone (O3), the most reactive gaseous component of air pollution, to function as a modulator during sensitization is not well established. C57BL/6 J male mice were intranasally sensitized to house dust mite (HDM) (40 μg/kg) for 3 weeks on alternate days in parallel with once-a-week O3 exposure (1 ppm). Mice were euthanized 24 h following the last HDM challenge. Lung lavage, histology, lung function (both forced oscillation and forced expiration-based), immune cell profiling, inflammation (pulmonary and systemic), and immunoglobulin production were assessed. Compared to HDM alone, HDM + O3 leads to a significant increase in peribronchial inflammation (p < 0.01), perivascular inflammation (p < 0.001) and methacholine-provoked large airway hyperreactivity (p < 0.05). Serum total IgG and IgE and HDM-specific IgG1 were 3-5 times greater in HDM + O3 co-exposure compared to PBS and O3-exposed groups. An increase in activated/mature lung total and monocyte-derived dendritic cells (p < 0.05) as well as T-activated, and T memory lymphocyte subset numbers (p < 0.05) were noted in the HDM + O3 group compared to HDM alone group. Concurrent O3 inhalation and HDM sensitization also caused significantly greater (p < 0.05) lung tissue interleukin-17 pathway gene expression and mediator levels in the serum. Redox imbalance was manifested by impaired lung antioxidant defense and increased oxidants. O3 inhalation during allergic sensitization coalesces in generating a significantly worse TH17 asthmatic phenotype.

scRNA-seq identifies unique macrophage population in murine model of ozone induced asthma exacerbation

Ozone (O 3 ) inhalation triggers asthmatic airway hyperresponsiveness (AHR), but the mechanisms by which this occurs are unknown. Previously, we developed a murine model of dust mite, ragweed, and aspergillus (DRA)-induced allergic lung inflammation followed by O 3 exposure for mechanistic investigation. The present study used single cell RNA-sequencing for unbiased profiling of immune cells within the lungs of mice exposed to DRA, O 3 , or DRA+O 3 , to identify the components of the immune cell niche that contribute to AHR. Alveolar macrophages (AMs) had the greatest number of differentially expressed genes following DRA+O 3 , most of which were unique to the 2-hit exposure. Following DRA+O 3 , AMs activated transcriptional pathways related to cholesterol biosynthesis, degradation of the extracellular matrix, endosomal TLR processing, and various cytokine signals. We also identified AM and monocyte subset populations that were unique to the DRA+O 3 group. These unique AMs activated gene pathways related to inflammation, sphingolipid metabolism, and bronchial constriction. The unique monocyte population had a gene signature that suggested phospholipase activation and increased degradation of the extracellular matrix. Flow cytometry analysis of BAL immune cells showed recruited monocyte-derived AMs after DRA and DRA+O 3 , but not after O 3 exposure alone. O 3 alone increased BAL neutrophils but this response was attenuated in DRA+O 3 mice. DRA-induced changes in the airspace immune cell profile were reflected in elevated BAL cytokine/chemokine levels following DRA+O 3 compared to O 3 alone. The present work highlights the role of monocytes and AMs in the response to O 3 and suggests that the presence of distinct subpopulations following allergic inflammation may contribute to O 3 -induced AHR.

Resolving multi-image spatial lipidomic responses to inhaled toxicants by machine learning

Regional responses to inhaled toxicants are essential to understand the pathogenesis of lung disease under exposure to air pollution. We evaluated the effect of combined allergen sensitization and ozone exposure on eliciting spatial differences in lipid distribution in the mouse lung that may contribute to ozone-induced exacerbations in asthma. Lung lobes from male and female BALB/c mice were cryosectioned and acquired by high resolution mass spectrometry imaging (MSI). Processed MSI peak annotations were validated by LC-MS/MS data from scraped tissue slides and microdissected lung tissue. Images were normalized and segmented into clusters. Interestingly, segmented clusters overlapped with stained serial tissue sections, enabling statistical analysis across biological replicates for morphologically relevant lung regions. Spatially distinct lipids had higher overall degree of unsaturated fatty acids in distal lung regions compared to proximal regions. Furthermore, the airway and alveolar epithelium exhibited significantly decreased sphingolipid and glycerophospholipid abundance in females, but not in males. We demonstrate the potential role of lipid saturation in healthy lung function and highlight sex differences in regional lung lipid distribution following ozone exposure. Our study provides a framework for future MSI experiments capable of relative quantification across biological replicates and expansion to multiple sample types, including human tissue.

Lipid mediators of inhalation exposure-induced pulmonary toxicity and inflammation

Many inhalation exposures induce pulmonary inflammation contributing to disease progression. Inflammatory processes are actively regulated via mediators including bioactive lipids. Bioactive lipids are potent signaling molecules involved in both pro-inflammatory and resolution processes through receptor interactions. The formation and clearance of lipid signaling mediators are controlled by multiple metabolic enzymes. An imbalance of these lipids can result in exacerbated and sustained inflammatory processes which may result in pulmonary damage and disease. Dysregulation of pulmonary bioactive lipids contribute to inflammation and pulmonary toxicity following exposures. For example, inhalation of cigarette smoke induces activation of pro-inflammatory bioactive lipids such as sphingolipids, and ceramides contributing to chronic obstructive pulmonary disease. Additionally, exposure to silver nanoparticles causes dysregulation of inflammatory resolution lipids. As inflammation is a common consequence resulting from inhaled exposures and a component of numerous diseases it represents a broadly applicable target for therapeutic intervention. With new appreciation for bioactive lipids, technological advances to reliably identify and quantify lipids have occurred. In this review, we will summarize, integrate, and discuss findings from recent studies investigating the impact of inhaled exposures on pro-inflammatory and resolution lipids within the lung and their contribution to disease. Throughout the review current knowledge gaps in our understanding of bioactive lipids and their contribution to pulmonary effects of inhaled exposures will be presented. New methods being employed to detect and quantify disruption of pulmonary lipid levels following inhalation exposures will be highlighted. Lastly, we will describe how lipid dysregulation could potentially be addressed by therapeutic strategies to address inflammation.

Metabolomics in Animal Models of Bronchial Asthma and Its Translational Importance for Clinics

Bronchial asthma is an extremely heterogenous chronic respiratory disorder with several distinct endotypes and phenotypes. These subtypes differ not only in the pathophysiological changes and/or clinical features but also in their response to the treatment. Therefore, precise diagnostics represent a fundamental condition for effective therapy. In the diagnostic process, metabolomic approaches have been increasingly used, providing detailed information on the metabolic alterations associated with human asthma. Further information is brought by metabolomic analysis of samples obtained from animal models. This article summarizes the current knowledge on metabolomic changes in human and animal studies of asthma and reveals that alterations in lipid metabolism, amino acid metabolism, purine metabolism, glycolysis and the tricarboxylic acid cycle found in the animal studies resemble, to a large extent, the changes found in human patients with asthma. The findings indicate that, despite the limitations of animal modeling in asthma, pre-clinical testing and metabolomic analysis of animal samples may, together with metabolomic analysis of human samples, contribute to a novel way of personalized treatment of asthma patients.

Early-life ozone exposure modulates region-specific gene expression in the developing rat lung

Early-life ozone exposure disrupts normal patterns of lung development, but the molecular determinants underlying these changes are not well understood. This study aimed to elucidate changes in gene expression following episodic ozone exposure to identify potential mechanisms of ozone-mediated impairments in lung development. Rat pups were exposed to either filtered air or ozone (0.5 ppm, 6 hr./day, 5 days/week) from postnatal day (PND) 7-28 (16 dams total with 8 pups each, 4 M & 4 F) and sacrificed at either PND 30-31 or PND 80-84. Lung microdissection isolated major regions for RNA-Seq analysis. Ozone modified inherent differences in gene expression between lung regions in both male and female rat pups, whereas statistically significant changes in gene expression directly attributed to ozone were only identified in females. The greatest number of differentially expressed genes was observed between the distal airways and the parenchyma of ozone-exposed juvenile female rats, with 355 genes being differentially expressed. Genes modulating epithelial-to-mesenchymal transition, cell growth, and adhesion were differentially expressed in the parenchyma of ozone exposed juvenile females, suggesting that episodic ozone exposure may affect branching morphogenesis and lung cell growth. Importantly, our study provides novel targets for future experiments investigating the impact of ozone on lung development.

SMetaS: A Sample Metadata Standardizer for Metabolomics

Metabolomics has advanced to an extent where it is desired to standardize and compare data across individual studies. While past work in standardization has focused on data acquisition, data processing, and data storage aspects, metabolomics databases are useless without ontology-based descriptions of biological samples and study designs. We introduce here a user-centric tool to automatically standardize sample metadata. Using such a tool in frontends for metabolomic databases will dramatically increase the FAIRness (Findability, Accessibility, Interoperability, and Reusability) of data, specifically for data reuse and for finding datasets that share comparable sets of metadata, e.g., study meta-analyses, cross-species analyses or large scale metabolomic atlases. SMetaS (Sample Metadata Standardizer) combines a classic database with an API and frontend and is provided in a containerized environment. The tool has two user-centric components. In the first component, the user designs a sample metadata matrix and fills the cells using natural language terminology. In the second component, the tool transforms the completed matrix by replacing freetext terms with terms from fixed vocabularies. This transformation process is designed to maximize simplicity and is guided by, among other strategies, synonym matching and typographical fixing in an n-grams/nearest neighbors model approach. The tool enables downstream analysis of submitted studies and samples via string equality for FAIR retrospective use.