Mass spectrometry-based lipidomics to explore the biochemical effects of naphthalene toxicity or tolerance in a mouse model

Abstract

Naphthalene causes mouse airway epithelial injury. However, repeated exposures of naphthalene result in mouse airway tolerance. Previous results showed that toxicity or tolerance was correlated with changes of phosphorylcholine-containing lipids. In this study, a mass spectrometry-based lipidomic approach was applied to examine the effects of naphthalene-induced injury or tolerance in the male ICR mice. The injury model was vehicle x 7 plus 300 mg/kg naphthalene while the tolerant one was 200 mg/kg daily x 7 followed by 300 mg/kg naphthalene on day 8. The lung, liver, kidney, and serum samples were collected for profiles of phosphorylcholine-containing lipids including phosphatidylcholines (PCs) and sphingomyelins (SMs). A partial least-square-discriminate analysis model showed different lung phosphorylcholine-containing lipid profiles from the injured, tolerant, and control groups. Perturbation of diacyl-PCs and plasmenylcholines may be associated with enhanced membrane flexibility and anti-oxidative mechanisms in the lungs of tolerant mice. Additionally, alterations of lyso-PCs and SMs may be responsible for pulmonary dysfunction and inflammation in the lungs of injured mice. Moreover, serum PC(16:0/18:1) has potential to reflect naphthalene-induced airway injuries. Few phosphorylcholine-containing lipid alterations were found in the mouse livers and kidneys across different treatments. This study revealed the changes in lipid profiles associated with the perturbations caused by naphthalene tolerance and toxicity; examination of lipids in serum may assist biomarker development with the potential for application in the human population.

Metabolomics of lung microdissections reveals region- and sex-specific metabolic effects of acute naphthalene exposure in mice

“…Our study demonstrates the importance of region-specific metabolomic analysis of the lung. Previous metabolomics studies of the lung have analyzed homogenized whole lung tissue to characterize the effects of naphthalene in mice (16,17,28). However, the results of our study demonstrated significant differences when comparing individual regions of microdissected lung tissue from male and female mice that received i.p.…”

“…The acute toxic effects of naphthalene exposure are well characterized in mice. However, few studies have utilized metabolomics to evaluate the contribution of global lung metabolite changes underlying the mechanism of naphthalene toxicity (7,16,28). Lung metabolomics studies in mice routinely analyze homogenized whole lung lobes, which prevents the characterization of region-specific responses following exposure to toxicants that target individual regions of the lung.…”

“…Metabolomics enables global characterization of metabolites produced by an organism and metabolic changes associated with toxicant exposure or environmental interactions (14,15). Previous studies have implemented nontargeted metabolomics analyses to characterize changes in metabolism in response to naphthalene, demonstrating significant alterations with respect to treatment (16,17). However, these analyses have been limited to the sampling of homogenized whole lung lobes, precluding the ability to distinguish metabolic responses between different lung regions.…”

Ultrastructural changes of Basolateral Amygdaloid nuclei in the Sprague Dawley rats brain following exposure to naphthalene balls

“…Naphthalene exposure is related to various toxic manifestations in laboratory animals and humans, with the lens of the eye, lungs and the brain being the most sensitive (Lee et al, 2018). Naphthalene endures wide microsomal metabolism.…”

The Hitchhiker’s Guide to Untargeted Lipidomics Analysis: Practical Guidelines

“…Partial Least-Squares Discriminant Analysis (PLS-DA) is a calibration algorithm that has become incredibly popular in the field of lipidomics [62][63][64][65]. In contrast with the classic PCA technique, PLS-DA can be considered as a "supervised" method and might be especially useful when dealing with a dataset for which a class membership for each sample is known.…”