Biomass burning in the Amazon region causes DNA damage and cell death in human lung cells.
Nilmara de Oliveira AlvesAlexandre Teixeira VessoniAnnabel QuinetRodrigo Soares FortunatoGustavo Satoru KajitaniMilena Simões PeixotoSandra de Souza HaconPaulo ArtaxoPaulo SaldivaCarlos Frederico Martins MenckSilvia Regina Batistuzzo de MedeirosPublished in: Scientific reports (2017)
Most of the studies on air pollution focus on emissions from fossil fuel burning in urban centers. However, approximately half of the world's population is exposed to air pollution caused by biomass burning emissions. In the Brazilian Amazon population, over 10 million people are directly exposed to high levels of pollutants resulting from deforestation and agricultural fires. This work is the first study to present an integrated view of the effects of inhalable particles present in emissions of biomass burning. Exposing human lung cells to particulate matter smaller than 10 µm (PM10), significantly increased the level of reactive oxygen species (ROS), inflammatory cytokines, autophagy, and DNA damage. Continued PM10 exposure activated apoptosis and necrosis. Interestingly, retene, a polycyclic aromatic hydrocarbon present in PM10, is a potential compound for the effects of PM10, causing DNA damage and cell death. The PM10 concentrations observed during Amazon biomass burning were sufficient to induce severe adverse effects in human lung cells. Our study provides new data that will help elucidate the mechanism of PM10-mediated lung cancer development. In addition, the results of this study support the establishment of new guidelines for human health protection in regions strongly impacted by biomass burning.
Keyphrases
- particulate matter
- air pollution
- cell death
- cell cycle arrest
- dna damage
- induced apoptosis
- oxidative stress
- human health
- lung function
- heavy metals
- reactive oxygen species
- wastewater treatment
- risk assessment
- endoplasmic reticulum stress
- polycyclic aromatic hydrocarbons
- dna repair
- anaerobic digestion
- climate change
- pi k akt
- machine learning
- electronic health record
- municipal solid waste