Multi-Omics analysis reveals molecular insights into the effects of acute ozone exposure on lung tissues of normal and obese male mice.

Certain sub-groups, including men and obese individuals, are more susceptible to ozone (O3) exposure, but the underlying molecular mechanisms remain unclear. In this study, the male mice were divided into two dietary groups: one fed a high-fat diet (HFD), mimicking obesity conditions, and the other fed a normal diet (ND), then exposed to 0.5 ppm and 2 ppm O3 for 4 h per day over two days. The HFD mice exhibited significantly higher body weight and serum lipid biochemical indicators compared to the ND mice. Obese mice also exhibited more severe pulmonary inflammation and oxidative stress. Using a multi-omics approach including proteomics, metabolomics, and lipidomics, we observed that O3 exposure induced significant pulmonary molecular changes in both obese and normal mice, primarily arachidonic acid metabolism and lipid metabolism. Different molecular biomarker responses to acute O3 exposure were also observed between two dietary groups, with immune-related proteins impacted in obese mice and PPAR pathway-related proteins affected in normal mice. Furthermore, although not statistically significant, O3 exposure tended to aggravate HFD-induced disturbances in lung glycerophospholipid metabolism. Overall, this study provides valuable molecular insights into the responses of lung to O3 exposure and highlights the potential impact of O3 on obesity-induced metabolic changes.

Ozone exposure associates with sperm quality indicators: Sperm telomere length as a potential mediating factor.

Evidence linking O3 exposure and human semen quality is limited and conflicting and the mechanism underlying the association remains unclear. Therefore, we investigated the associations between ambient O3 exposure and sperm quality parameters and explored the mediating role of sperm mitochondrial DNA copy number (mtDNAcn) and sperm telomere length (STL) among 1068 potential sperm donors who provided 5002 repeated semen samples over approximately 90 days. We found that every 10 µg/m3 increase in O3 exposure was associated with a decrease in STL, sperm concentration, total count, total motile sperm number, and semen volume. However, O3 exposure was associated with increased total motility and progressive motility. The association for sperm quality parameters was stronger when exposure was measured at spermatogenesis stages I and II. For STL, the strongest association was observed when exposure was measured at spermatogenesis stage II. Additionally, we found that approximately 9% and 8% of the association between O3 exposure and sperm concentration and count was mediated by STL, respectively. In summary, our findings suggest that O3 pollution may affect sperm telomere length, eventually leading to reduced semen quality.

Arsenic exposure induces urinary metabolome disruption in Pakistani male population.

Millions of people are at risk of consuming arsenic (As) contaminated drinking water in Pakistan. The current study aimed to investigate urinary arsenic species [iAsIII, iAsV, dimethylarsinic acid (DMA), methylarsonic acid (MMA)] and their potential toxicity biomarkers (based on urinary metabolome) in order to characterize the health effects in general adult male participants (n = 588) exposed to various levels of arsenic in different floodplain areas of Pakistan. The total urinary arsenic concentration (mean; 161 µg/L) of studied participants was lower and/or comparable than those values reported from other highly contaminated regions, but exceeded the Agency for Toxic Substances and Disease Registry (ATSDR) limits. For all the participants, the most excreted species was DMA accounting for 65% of the total arsenic, followed by MMA (20%) and iAs (16%). The percentage of MMA detected in this study was higher than those of previously reported data from other countries. These results suggested that studied population might have high risk of developing arsenic exposure related adverse health outcomes. Furthermore, random forest machine learning algorithm, partial correlation and binary logistic regression analysis were performed to screen the arsenic species-related urinary metabolites. A total of thirty-eight metabolites were extracted from 2776 metabolic features and identified as the potential arsenic toxicity biomarkers. The metabolites were mainly classified into xanthines, purines, and amino acids, which provided the clues linking the arsenic exposure with oxidative stress, one-carbon metabolism, purine metabolism, caffeine metabolism and hormone metabolism. These results would be helpful to develop early health warning system in context of arsenic exposure among the general populations of Pakistan.